minerva/0000755000176200001440000000000013472720023011713 5ustar liggesusersminerva/inst/0000755000176200001440000000000013271553720012675 5ustar liggesusersminerva/inst/CITATION0000644000176200001440000000144513271553720014036 0ustar liggesuserscitHeader("To cite the 'minerva' package in publications use:") year <- sub(".*(2[[:digit:]]{3})-.*", "\\1", meta$Date, perl = TRUE) vers <- paste("R package version", meta$Version) citEntry(entry="article", title="Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers", author=personList(as.person("Davide Albanese"), as.person("Michele Filosi"), as.person("Roberto Visintainer"), as.person("Samantha Riccadonna"), as.person("Giuseppe Jurman"), as.person("Cesare Furlanello")), journal="Bioinformatics", pages="bts707", year="2012", publisher="Oxford Univ Press", textVersion=paste("Albanese, Davide, et al.", "Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers.","Bioinformatics 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YKL072W,0.22,0.09,0.11,0.13,-0.03,0.02,-0.19,0.11,-0.25,-0.02,-0.69,-0.07,-0.35,0.02,0.06,0.15,0.44,0.02,0.09,-0.27,0.05,-0.1,-0.04 YKL073W,-0.065000005,0.05,0.065,0.08,-0.76,0.19,-0.28,0.16,-0.52,0.08,0.24,0.19,-0.4,0.31,-0.36,0.39,-0.25,0.16,-0.08,0.21,0.05,0.36,0.20500001 YKL074C,0.46,0.31,0.305,0.3,-0.35,0.3,-0.01,0.38,-0.44,0.55,-0.44,0.16,-0.66,0.06,-0.57,0.48,-0.37,0.3,-0.74,-0.94,-0.66,0.49,0.335 YKL075C,0.48,0.44,0.4,0.36,-0.75,0.35,-0.17,0.4,-0.73,0.32,0.02,-0.13,-0.53,0.12,-0.11,0.23,-0.41,0.09,-0.64,-0.27,-0.3,0.37,0.235 YKL076C,0.29500002,0.25,0.29000002,0.33,0.06,0.19,0.04,0.18,-0.21,0,-0.42,-0.03,-0.35,0.14,-0.39,-0.09,0.27,0.04,-0.03,-0.72,0.14,-0.02,-0.085 YKL077W,0.17,0.28,0.41500002,0.55,-0.41,0.53,-0.2,0.36,-0.69,0.13,-0.38,0.12,-0.33,0.37,-0.05,0.47,-0.69,0.4,-0.33,-0.6,-0.46,0.17,0.125 YKL078W,0.8,0.41,0.39999998,0.39,-0.03,0.53,0.05,0.46,-0.2,0.39,-0.52,0.05,-0.87,-0.09,-0.44,0.57,0.01,0.17,-0.35,-1.15,-0.65,-0.21,-0.42 YKL079W,0.425,0.49,0.3,0.11,-0.78,0.28,-0.52,0.21,-0.69,0.14,0.07,-0.03,-0.72,0,-0.13,0.25,-0.49,0.34,-0.44,0.17,-0.15,0.48,0.365 YKL080W,-0.39,-0.31,-0.15,0.01,0.11,0.24,0.13,0.17,0.23,0.29,-0.09,0.35,-0.01,0.05,-0.49,0.17,-0.06,0.33,-0.02,-0.43,0.04,-0.02,0.07 YKL081W,0.074999996,0.16,0.255,0.35,-0.12,0.5,0.03,0.28,0.16,0.25,-0.1,0.18,-0.28,0.01,0.09,0.2,-0.3,0.31,0.03,-0.77,-0.46,0.01,-0.11 YKL082C,0.84,0.62,0.47500002,0.33,-0.91,0.37,-0.6,0.35,-1.18,0.45,-0.79,0.41,-1.14,0.17,-0.88,0.64,-0.65,0.15,-0.68,0.5,-0.6,0.71,0.43 YKL084W,0.18,0.2,0.26500002,0.33,0.64,0.26,0.25,0.18,0.25,-0.26,-0.09,-0.01,-0.09,0.01,0.1,-0.14,0.03,0.11,0.25,-0.7,-0.24,-0.51,-0.605 YKL085W,-1.095,-0.75,-0.375,0,0,0.15,-0.14,0.1,-0.43,0.05,-0.14,0.06,-0.11,0.41,0.53,-0.03,-0.08,0.39,-0.46,0.93,0.68,0.61,0.6 YKL086W,-0.255,-0.29,-0.065,0.16,0.09,-0.13,0.03,-0.15,0.1,0.05,-0.22,0.05,-0.63,0.14,0.32,-0.07,0.21,-0.05,0.16,-0.03,0.23,0.44,0.13499999 YKL087C,-0.39999998,-0.39,-0.255,-0.12,-0.33,0.01,-0.24,0.2,-0.2,0.09,-0.19,0.13,-0.35,-0.01,0.46,0.07,-0.14,0.35,-0.27,0.43,0.35,0.29,0.17999999 YKL088W,0.13,0.07,0.07,0.07,-0.28,-0.01,-0.49,0.11,-0.08,0.23,-0.17,0.09,-0.03,0.26,-0.09,0.01,-0.22,-0.37,0.27,0.32,-0.28,0.39,0.24499999 YKL091C,-0.79499996,-0.71,-0.29999998,0.11,0.06,-0.02,0.19,-0.25,0.07,-0.12,-0.48,-0.23,-0.22,0.33,0.47,-0.16,1.35,-0.15,0.02,0.07,0.25,0.42,0.41 YKL093W,-0.31,-0.44,-0.065,0.31,0.34,-0.04,-0.07,-0.03,0.33,-0.37,-0.87,-0.34,-0.03,0.48,0.51,-0.04,0.41,-0.37,0.64,-0.24,0.88,-0.19,-0.21000001 YKL095W,0.39000002,0.12,0.08,0.04,-0.35,-0.71,0.23,0.25,-0.23,-0.05,-0.14,-0.29,-0.45,0.03,0.31,-0.07,0.03,-0.21,-0.33,-0.41,-0.33,0.34,0.3 YKL096W,-1.14,0.08,0.325,0.57,1.36,0.59,0.22,-0.46,-0.26,-0.59,0.48,0.83,1.12,0.66,0.7,0.5,0.82,0.08,0.24,-1.02,-0.73,-0.63,-0.45999998 YKL096W-A,-0.98,-0.66,-0.23000002,0.2,0.79,0.65,0.76,0.59,0.84,-0.03,-0.21,-0.19,0.07,-0.3,-0.29,0.33,0.59,0.64,0.64,-0.39,-0.24,-0.4,-0.495 YKL097C,0.11,0.09,-0.024999999,-0.14,-0.53,0.24,0.53,-0.36,0.29,-0.47,0.31,-0.5,0.15,0.33,0.44,-0.16,0.38,0.11,0.34,-0.39,0.39,-0.23,-0.38 YKL098W,0.17,0.22,0.305,0.39,-0.1,0.32,-0.14,0.35,-0.36,0.38,-0.27,0.47,-0.14,-0.11,0.1,0.32,-0.36,0.2,-0.35,-0.48,-0.9,0.27,0.11500001 YKL099C,0.79499996,0.75,0.515,0.28,-0.33,0.2,-0.33,0.1,-0.68,0.21,-0.5,0.36,-0.79,0.28,-0.54,0.43,-0.17,0.27,-0.7,0.14,-0.5,0.02,-0.085 YKL101W,0.66,0.97,0.70000005,0.43,0.16,-0.48,-0.68,-1.06,-0.48,0.16,0.09,0.8,0.42,0.88,0.13,-0.19,0.09,-0.55,-0.15,-0.24,0.16,-0.09,-0.13 YKL103C,-0.82,-0.71,-0.57,-0.43,-0.55,-0.6,-1.04,-0.97,-0.95,0.12,-0.37,0.82,0.14,0.42,0.14,0.6,0.3,0.23,0.05,0.21,0.09,1.03,1.105 YKL104C,0.065,0,-0.16,-0.32,-0.67,-0.09,-0.19,0.25,0.43,0.41,0.31,0.27,0.07,-0.07,-0.29,-0.45,-0.61,-0.18,-0.06,-0.65,0.1,0.41,0.42000002 YKL105C,0.135,0.22,0.22999999,0.24,0.15,-0.03,-0.11,0.17,-0.47,-0.04,-0.49,0.74,-0.3,0.05,0.13,0.01,0.26,-0.15,-0.32,0.15,0.31,0.07,-0.11500001 YKL106W,-0.120000005,-0.34,-0.120000005,0.1,-0.1,0.01,0.1,0.05,0.2,0.09,-0.14,0.14,0.15,0.17,-0.02,0.09,-0.14,-0.08,0.37,-0.32,-0.1,-0.27,-0.32 YKL108W,0.62,0.41,-0.70500004,-1.82,-0.33,-0.5,-0.11,-0.62,0.02,0.37,0.3,0.43,0.49,0.78,0.14,-0.24,-0.5,-0.16,-0.2,0.3,0.27,0.27,0.13000001 YKL110C,0.385,0.24,0.145,0.05,-0.44,0.03,0,0.17,-0.21,0.14,-0.5,0.03,-0.39,0.16,-0.5,0.27,-0.65,0.3,-0.53,-0.08,0.07,0.36,0.155 YKL112W,-0.71000004,-0.51,-0.24499999,0.02,-0.13,0.16,0.16,0.12,-0.35,-0.45,-0.48,-0.05,0,0.4,0.45,0.52,0.65,0.54,0,0.16,0.43,0.07,0.035 YKL114C,0.4,0.33,0.22500001,0.12,-0.31,0.03,-0.14,0,-0.07,0.2,-0.26,0.3,0.12,0.17,0,-0.29,-0.95,-0.01,-0.35,0.08,-0.03,0.25,0.09 YKL116C,0.69000006,-0.19,-0.35999998,-0.53,-0.76,-0.67,0.18,-0.19,0.16,1.37,0.47,0.54,-0.15,-0.37,-0.46,-0.57,-1.22,-0.55,-0.3,-0.19,1.21,0.62,0.35500002 YKL117W,-0.305,-0.37,-0.22,-0.07,-0.43,-0.01,-0.35,0,-0.49,0.22,-0.43,0.09,-0.45,0.16,-0.43,0.2,-0.45,0.26,-0.39,1.21,-0.58,0.97,0.89 YKL118W,0.3,0.31,0.019999996,-0.27,0.23,-0.03,0.47,-0.81,0.4,-0.62,0.06,-0.21,0.31,0.15,0.47,-0.85,0.06,-0.13,0.4,-0.13,0.5,-0.27,-0.2 YKL119C,0.19999999,0.26,0.065,-0.13,0.17,-0.17,-0.06,-0.13,-0.35,0,-0.26,0.72,-0.26,0.11,0.05,-0.17,0.3,-0.07,0,0.25,-0.26,-0.11,-0.065 YKL120W,0.435,0.41,0.46499997,0.52,-0.3,0.34,-0.34,0.16,-0.58,0.05,-0.75,0,-0.46,0.39,-0.22,0.32,-0.36,0.37,-0.46,-0.49,-0.46,0.39,0.27499998 YKL121W,0.06,0.16,-0.015000001,-0.19,-0.06,-0.36,-0.32,-0.09,-0.24,-0.42,-0.36,0.51,-0.26,0.22,0.02,-0.21,0.34,-0.24,0.2,0.08,0.43,0,0.415 YKL122C,0.845,0.89,0.66999996,0.45,-0.55,0.27,-0.13,0.2,-0.77,0.4,-0.42,0.49,-0.42,0.47,-0.53,0.21,-0.96,0.35,-1.49,-0.79,-1.11,0.68,0.53 YKL123W,0.155,0.07,0.055,0.04,-0.07,-0.2,-0.18,-0.04,-0.32,0.07,-0.36,0.43,-0.04,-0.17,0.24,-0.27,-0.2,0.03,0.21,0.1,0.52,0.05,0.15 YKL127W,0.034999996,0.39,0.48999998,0.59,0.32,0.17,-0.7,-0.47,-0.64,0.02,-0.14,1.69,0.44,0.77,0.02,0.42,0.24,-0.04,-0.14,-0.47,-0.39,-1.5,-0.77 YKL129C,0.04,-0.02,0.060000002,0.14,-0.02,0.19,-0.54,0.08,-0.28,0.17,-0.35,0.89,-0.37,0.01,-0.39,0.02,0.11,0.08,-0.07,0.12,-0.24,0.05,0.11 YKL130C,-0.4,-0.44,-0.034999996,0.37,0.32,0.48,0.6,0.65,0.38,-0.07,-0.36,-0.19,0.07,-0.02,0.2,-0.99,-0.6,0.44,0.1,-0.1,0.66,-0.05,-0.305 YKL135C,0.024999999,-0.09,-0.030000001,0.03,-0.2,-0.05,-0.09,0.08,-0.05,0.14,-0.22,0.3,-0.02,0.11,-0.18,-0.09,-0.14,-0.11,0,-0.02,-0.38,0.25,0.28 YKL136W,0,-0.08,0.12,0.32,-0.58,-0.26,0.23,0.14,0.05,-0.33,-0.13,0.26,-0.02,0.41,0.55,-0.78,0.36,-0.2,0.46,0.08,0.69,-0.31,-0.120000005 YKL138C,-0.65999997,-0.61,-0.51,-0.41,-0.89,0.07,0,0.26,-0.3,0.32,-0.13,0.2,-0.3,0.28,0.05,0.38,-0.86,0.43,-0.35,0.4,0.08,1.01,0.79999995 YKL140W,-0.19,0.02,0.19000001,0.36,0.15,0.15,0.08,0.07,-0.13,-0.1,-0.22,0.22,0.18,0.38,0.1,0.17,-0.55,0.03,-0.16,-0.11,0.07,0.22,0.095 YKL142W,-0.085,-0.05,-0.010000001,0.03,-0.66,0.08,-0.29,-0.12,-0.56,0.21,-0.4,0.16,-0.29,0.19,-0.51,0.14,-1.09,0.35,-0.95,0.03,-0.29,1.15,1.2049999 YKL144C,0.49,0.44,0.375,0.31,-0.43,0.42,0.33,0.32,-0.46,0.28,-0.56,-0.15,-0.25,0.2,-0.41,0.28,-0.48,0.36,-0.69,-0.69,-0.56,0.23,0.14 YKL145W,-0.1,-0.05,-0.075,-0.1,-0.44,0.03,-0.38,-0.1,-0.59,0.3,-0.56,0.27,-0.53,0.14,-1.73,0.39,-0.27,0.47,-0.5,0.7,-0.33,1.31,1.23 YKL146W,0.005000003,0.15,0.17500001,0.2,-0.14,0.27,0.12,0.14,0.23,0.15,-0.16,0.39,0.08,0.23,-0.11,0.23,-0.07,-0.07,0.01,-0.72,-0.14,-0.12,-0.105000004 YKL147C,-0.2,-0.02,-0.035,-0.05,0.2,0.05,-0.14,0.09,0.09,0.23,0.09,0.31,0.14,-0.09,-0.18,0.05,0.06,-0.95,0.44,0.15,0.28,0.03,0.03 YKL148C,-1,-0.64,-0.16,0.32,0.36,0.56,0.32,0.42,0.57,0.49,0.1,-0.77,0.08,0.31,-0.07,-0.01,-0.19,0.09,-0.17,-0.5,0.23,0.31,0.28 YKL149C,0.325,0.35,0.16499999,-0.02,-0.02,0.09,-0.25,0.15,-0.46,0.2,-0.34,0.31,-0.42,0.2,-0.08,0.11,0.09,0.09,-0.12,0.12,-0.11,0.07,0.04 YKL150W,-0.35000002,-0.16,0.16,0.48,-0.27,0.57,-0.03,0.47,-0.27,0.73,-0.41,0.38,-0.53,0.34,-1.19,0.27,-0.79,0.48,-0.76,-0.61,-0.23,0.79,0.755 YKL151C,-0.99,-1,-0.675,-0.35,-0.15,-0.16,-0.33,0.06,2.3,0.2,-0.33,0.39,-0.63,-0.2,-0.18,-0.35,-0.1,-0.03,0.18,0.29,0.22,0.49,0.46 YKL152C,-1.205,-1.19,-0.81000006,-0.43,-0.83,0.21,0.03,0.51,-0.09,0.53,-0.01,0.5,-0.22,0.43,-0.58,0.65,-0.31,0.93,-0.39,-0.47,0.15,0.94,0.9 YKL153W,-1.45,-1.39,-1.06,-0.73,-0.25,0.02,0.02,0.28,0.25,0.37,-0.07,0.31,-0.05,0.21,-0.05,0.42,0.16,0.68,0.36,0.33,0.03,0.71,0.635 YKL154W,0.09,0.09,0.255,0.42,0.11,0.2,0.33,-0.01,0.08,-0.17,-0.38,0.04,-0.14,0.22,-0.45,0.32,-0.71,0.28,-0.3,0.04,-0.17,-0.01,-0.11 YKL155C,-0.045,-0.17,-0.17500001,-0.18,-0.38,-0.41,-0.3,-0.13,-0.56,0.21,-0.42,0.08,-0.13,0.38,-0.26,0.08,0.03,0.11,0.17,0.84,-0.04,0.5,0.29 YKL156W,-0.035,-0.01,0.12,0.25,0.16,0.31,0.23,0.3,-0.13,0.09,-0.16,0.17,-0.16,0.05,-0.98,0.02,-0.39,0.33,-0.16,0.11,-0.09,0.12,-0.075 YKL160W,0.19,0.23,0.23,0.23,-0.14,0.32,0.11,0.38,-0.37,0.16,-0.48,-0.02,-0.46,0.16,-0.73,0.22,-0.88,0.16,-1.01,0.34,-0.25,0.73,0.52 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YKL169C,-0.44,-0.41,-0.55,-0.69,-0.62,-0.47,-0.59,0.1,-0.29,0.43,-0.04,0.26,-0.24,0.2,0.02,0.07,-0.24,0.39,0.06,0.85,0.15,0.46,0.525 YKL170W,-0.099999994,-0.13,-0.255,-0.38,-0.92,-1.44,-0.53,0.33,-0.43,0.87,0.1,0.15,-0.17,0.46,-0.54,0.3,-0.64,0.53,-0.73,-0.15,-0.17,1.11,1.03 YKL172W,1.49,1.35,0.885,0.42,-1.84,0.69,-0.36,0.54,-1.09,0.72,-0.5,0.63,-0.73,0.29,-1.63,0.56,-1.28,0.58,-1.77,-0.33,-1.56,0.95,0.725 YKL173W,-0.025000002,-0.17,-0.11,-0.05,0.18,0.22,-0.39,0.3,-0.32,0.23,0.06,0.09,-0.88,0.11,-0.17,0.16,0.15,-0.09,0.09,0.31,-0.1,-0.15,-0.11 YKL174C,0.015000001,0.05,0.105,0.16,-0.44,0.35,0.54,0.3,0.47,0.06,-0.35,0.26,0.08,-0.25,-0.16,0.19,0.11,0.14,0.13,-0.94,0.05,-0.35,-0.245 YKL175W,-0.25,-0.26,-0.059999995,0.14,0.35,0.28,-0.1,0.22,0.11,0.02,-0.2,-0.02,-0.02,-0.15,0.33,0.03,0.67,-0.01,0.6,-0.33,0.02,-0.46,-0.435 YKL176C,0.19,0.25,0.2,0.15,-0.58,0.4,0.5,0.23,-0.01,0.13,-0.26,0.1,-0.04,-0.13,-0.11,0.19,0.1,0.13,0.13,-0.74,-0.38,-0.04,-0.14 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YPR176C,-0.1,-0.06,0.095,0.25,0.32,-0.25,-0.16,-0.08,0.27,-0.06,-0.01,0.14,0.31,0.44,-0.5,-0.33,0.72,-0.16,0.63,-0.64,0.43,-0.41,-0.34 YPR177C,0.24000001,0.27,0.18,0.09,-0.22,0.34,0.24,0.24,-0.18,0.19,-0.26,0.05,-0.29,0.05,-0.08,0.05,-0.26,0.09,-0.26,-0.83,0.14,0.24,0.155 YPR178W,0.255,0.18,0.040000003,-0.1,-0.15,0.02,-0.14,0.17,-0.21,0.12,-0.47,0.2,-0.45,0.01,0.46,-0.4,0.06,-0.17,-0.01,-0.21,0.33,0.24,0.125 YPR179C,0.385,0.39,0.24,0.09,-0.12,-0.61,0.12,0.35,-0.12,0.3,-0.14,-0.06,-0.3,0.17,-0.19,0.05,-0.12,0.12,-0.64,-0.35,-0.25,0.36,0.275 YPR180W,0.16499999,0.25,-0.13999999,-0.53,-0.06,-0.39,0.08,-0.14,-0.37,0.03,0.01,0.26,-0.43,0.26,0.14,-0.34,0.09,-0.08,-0.18,0.16,0.11,0.17,0.26500002 YPR181C,-0.35,-0.28,-0.04,0.2,0.13,0.27,0.23,0.27,0.58,0.19,0.15,0.16,0.29,0.2,-0.69,0.15,0.29,-0.01,-0.42,-0.95,0.52,-0.17,-0.17 YPR183W,0.39,0.43,0.34,0.25,-0.1,0.13,-0.49,-0.08,-0.73,0.38,0.16,0.6,0.41,0.2,-0.24,0.03,0.25,0.22,-0.57,-1.71,-0.05,0.04,0.024999999 YPR185W,0.135,0.28,-0.065,-0.41,-0.05,-0.39,-0.12,-0.3,-0.03,0,-0.24,0.35,-0.01,0.39,0.05,-0.43,-0.32,-0.24,0.28,0.02,0.64,0.23,0.235 YPR186C,0.32999998,0.25,0.14,0.03,-0.47,0.04,0.03,0.28,-0.37,0.1,-0.28,0.14,-0.58,0.13,-0.3,0.27,0.14,0.03,0.22,0.14,-0.6,0.25,0.16 YPR189W,0.13,0.08,-0.060000002,-0.2,0.05,-0.01,0.07,-0.15,-0.11,0.05,-0.2,0.2,-0.05,-0.01,0.64,-0.18,0.39,-0.22,0.03,-0.24,0.38,-0.34,-0.28 YPR191W,-0.435,-0.13,-0.35,-0.57,-0.21,-0.44,0.58,-0.13,0.39,-0.21,0.05,0.82,0.28,0.2,0.3,-0.92,0.23,-0.4,0.78,0.78,0.94,-0.34,-0.36 YPR193C,-0.005,0.02,-0.17999999,-0.38,0.03,-0.25,0.23,-0.1,0.4,-0.43,-0.15,-0.17,0.05,0.06,0.29,-0.38,-0.21,-0.28,0.13,0.4,0.85,0.13,0.145 YPR194C,-0.36499998,-0.59,-0.54999995,-0.51,0.39,-0.22,0.29,-0.92,0.42,-0.59,0.04,-0.09,0.77,0.68,0.87,-0.22,0.95,-0.35,1.19,0.56,0.52,-0.74,-0.605 YPR197C,0.015000001,0.1,0.21,0.32,0.11,0.31,0.1,0.23,-0.12,0.31,0.21,0.07,0.13,-0.21,0.17,-0.01,0.07,0.08,-0.29,-1.29,0.26,-0.14,-0.175 YPR198W,-0.060000002,0.08,0.21000001,0.34,0.65,-0.26,0.14,-0.33,0.53,-0.02,0.13,0.08,0.07,0.14,-0.64,-0.26,0.53,-0.17,0.59,-0.96,0.4,-0.23,-0.325 YPR199C,0.155,0.19,0.235,0.28,-0.26,0.21,-0.4,0.34,-0.8,0.34,-0.25,0.25,-0.38,0.34,0.15,0.3,-0.06,0.13,-0.44,-1.03,0.14,0.3,0.25 YPR201W,-0.255,-0.36,-0.3,-0.24,1.3,-0.07,0.29,-0.2,0.25,-0.47,0.23,-1.2,0.12,-0.81,0.89,0.07,1.04,-0.32,0.8,-0.13,0.84,-0.39,-0.415 YPR203W,0.57,0.12,-0.06999999,-0.26,-0.44,-0.21,-1.08,0.39,-0.17,1.61,1.12,1.11,0.29,0.12,-0.96,-0.31,-0.81,-0.34,-1.21,-1.36,-0.12,0.69,0.555 YPR204W,0.405,0.17,-0.044999994,-0.26,-0.6,-0.09,-0.85,0.17,-0.05,1.26,0.67,0.79,0.19,0.17,-1.9,-0.21,-0.45,-0.31,-0.39,-0.22,-0.08,0.65,0.52 minerva/tests/testspellman0.R0000644000176200001440000000131213470715275016003 0ustar liggesuserslibrary(minerva) myalpha <- 0.6 myc <- 15 data(Spellman) Spellman <- as.matrix(Spellman) ## Spellman[,2] <- 0 mydata <- Spellman[,1:10] res <- mine(Spellman,master=1,n.cores=1,alpha=myalpha,C=myc) res1 <- mine(mydata, n.cores=1, var.thr=0.0, use="pair", est="mic_approx") res <- mine(mydata, n.cores=1, var.thr=0.0, use="pair", est="mic_approx", normalization = TRUE) ## aval.cores <- detectCores() ## if (aval.cores > 1){ ## cat("Multicore: On this machinhe you have ",aval.cores," computational cores.\n") ## } ## if (aval.cores > 2){ ## cat("We suggest to exploit minerva parallel computing possibilities with ",aval.cores-1," cores.\n(where possible set 'n.cores = 3')\n") ## cat("Test ok!!!\n") ## } minerva/tests/test_minepy.R0000644000176200001440000000012313271553720015541 0ustar liggesuserslibrary(minerva) x <- 0:200 / 200 y <- sin(10 * pi * x) + x mine(x,y, n.cores=1) minerva/tests/MINEJAVA.sh0000644000176200001440000000004113271553720014603 0ustar liggesusersjava -jar MINE.jar Spellman.csv 0minerva/tests/valgrind.out0000644000176200001440000000521613271553720015425 0ustar liggesusers********************************************************** MINE version 1.0.1d Copyright 2011 by David Reshef and Yakir Reshef. This application is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for more information. ********************************************************** input file = Spellman.csv analysis style = mv=0 results file name = 'Spellman.csv,mv=0,cv=0.0,B=n^0.6,Results.csv' print status frequency = every 100 variable pairs status file name = 'Spellman.csv,mv=0,cv=0.0,B=n^0.6,Status.txt' alpha = 0.6 numClumpsFactor = 15.0 debug level = 0 required common values fraction = 0.0 garbage collection forced every 2147483647 variable pairs Reading in dataset... I see only numbers beyond the first column of line 1, so I'm assuming variable names are in the first column. DONE. Read 4382 lines and 4382 variables. Analyzing... 1 calculating: time vs YAL001C... 101 calculating: time vs YBR156C... 201 calculating: time vs YBR281C... 301 calculating: time vs YCR010C... 401 calculating: time vs YDL017W... 501 calculating: time vs YDL131W... 601 calculating: time vs YDR060W... 701 calculating: time vs YDR173C... 801 calculating: time vs YDR292C... 901 calculating: time vs YDR418W... 1001 calculating: time vs YEL011W... 1101 calculating: time vs YER053C... 1201 calculating: time vs YFL020C... 1301 calculating: time vs YGL040C... 1401 calculating: time vs YGL167C... 1501 calculating: time vs YGR044C... 1601 calculating: time vs YGR168C... 1701 calculating: time vs YHL008C... 1801 calculating: time vs YHR079C... 1901 calculating: time vs YIL022W... 2001 calculating: time vs YIL135C... 2101 calculating: time vs YJL029C... 2201 calculating: time vs YJL161W... 2301 calculating: time vs YJR047C... 2401 calculating: time vs YKL022C... 2501 calculating: time vs YKL149C... 2601 calculating: time vs YKR047W... 2701 calculating: time vs YLR011W... 2801 calculating: time vs YLR130C... 2901 calculating: time vs YLR246W... 3001 calculating: time vs YLR360W... 3101 calculating: time vs YML016C... 3201 calculating: time vs YMR011W... 3301 calculating: time vs YMR160W... 3401 calculating: time vs YNL006W... 3501 calculating: time vs YNL160W... 3601 calculating: time vs YNR009W... 3701 calculating: time vs YOL070C... 3801 calculating: time vs YOR063W... 3901 calculating: time vs YOR199W... 4001 calculating: time vs YOR319W... 4101 calculating: time vs YPL080C... 4201 calculating: time vs YPL228W... 4301 calculating: time vs YPR100W... 4381 variable pairs analyzed Sorting results in descending order... done. Printing all results !!!with MIC > 0.0000001...!!! All done. Goodbye! minerva/tests/Spellman.csv,mv=0,cv=0.0,B=n^0.6,Status.txt0000644000176200001440000000004413271553720022175 0ustar liggesusers4301 calculating: time vs YPR100W...minerva/tests/MINE.jar0000644000176200001440000013065213271553720014317 0ustar liggesusersPKS+@META-INF/MANIFEST.MFMLK-. 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time,YBR300C,0.1742,0.17419335,0.0,0.08708,2.5849626,-0.00257841 time,YDR014W,0.1742,0.17417759,0.018779993,0.09907,2.5849626,0.00473387 time,YLL044W,0.1742,0.15046121,0.010669991,0.08708,2.5849626,-0.15407395 time,YLL064C,0.1742,0.1741398,0.0,0.09907,2.5849626,0.0077590463 minerva/tests/testthat/0000755000176200001440000000000013472710557014730 5ustar liggesusersminerva/tests/testthat/testthat.R0000644000176200001440000001152113472710256016707 0ustar liggesuserscontext("Check MINE scores consistency") ## Create constant vector const.create <- function(n){ x <- seq(0, 1, length.out=n) y <- rep(0, n) retval <- data.frame(X=x, Y=y) return(retval) } ## Create two linear vector lin.create <- function(n){ x <- seq(0, 1, length.out=n) retval <- data.frame(X=x, Y=x) return(retval) } ## Create SIN sin.create <- function(n){ x <- seq(0, 1, length.out=n) y <- sin(8 * pi * x) retval <- data.frame(X=x, Y=y) return(retval) } ## Create exp exp.create <- function(n){ x <- seq(0, 1, length.out=n) y <- 2**x retval <- data.frame(X=x, Y=y) return(retval) } test_that("Test constant:", { mydata <- const.create(1000) expect_warning(mm <- mine(mydata$X, mydata$Y)) expect_equal(mm$MIC, 0, tolerance=1e-4) expect_equal(mm$MAS, 0, tolerance=1e-4) expect_equal(mm$MEV, 0, tolerance=1e-4) expect_equal(mm$MCN, 2, tolerance=1e-4) } ) test_that("Test linear:", { mydata <- lin.create(1000) mm <- mine(mydata$X, mydata$Y) expect_equal(mm$MIC, 1, tolerance=1e-4) expect_equal(mm$MAS, 0, tolerance=1e-4) expect_equal(mm$MEV, 1, tolerance=1e-4) expect_equal(mm$MCN, 2, tolerance=1e-4) } ) test_that("Test sin:", { mydata <- sin.create(1000) mm <- mine(mydata$X, mydata$Y) expect_equal(mm$MIC, 1, tolerance=1e-4) expect_equal(mm$MAS, 0.875, tolerance=1e-4) expect_equal(mm$MEV, 1, tolerance=1e-4) expect_equal(mm$MCN, 4, tolerance=1e-4) } ) test_that("Test exp:", { mydata <- exp.create(1000) mm <- mine(mydata$X, mydata$Y) expect_equal(mm$MIC, 1, tolerance=1e-4) expect_equal(mm$MAS, 0, tolerance=1e-4) expect_equal(mm$MEV, 1, tolerance=1e-4) expect_equal(mm$MCN, 2, tolerance=1e-4) } ) ## test_that("Test bug 1:", { ## a <- mine(x = cbind(matrix(c(rnorm(6), 0, 0, 0), nrow = 3), rnorm(3)), master = 4) ## b <- mine(x = matrix(c(rnorm(6), 0, 0, 0), nrow = 3), y = rnorm(3)) ## expect_equal(a$MIC, b$MIC, tolerance=1e-4) ## } ## ) context("Check Mictools pipeline") test_that("Mictools pipeline:", { mydata <- matrix(rep(1:10, 4), ncol=4, nrow=10) + matrix(rep((0:3), each=10), ncol=4, nrow=10) nperm <- 100 rr <- mictools(mydata,nperm=nperm, seed=0) lr <- length(rr) expect_equal(lr, 5, tolerance=1e-4) expect_equal(length(rr$tic), nperm) }) test_that("mic_strength pval.col parameters",{ mydata <- matrix(rep(1:10, 4), ncol=4, nrow=10) + matrix(rep((0:3), each=10), ncol=4, nrow=10) rr <- mictools(mydata, nperm=100, seed=10) expect_error(mic_strength(mydata, pval=rr$pval, pval.col=6)) }) test_that("mic_strength high correlation == 1",{ mydata <- matrix(rep(1:10, 4), ncol=4, nrow=10) + matrix(rep((0:3), each=10), ncol=4, nrow=10) rr <- mictools(mydata, nperm=100, seed=10) ms <- mic_strength(mydata, pval=rr$pval, pval.col=c(6, 2, 3)) expect_equal(ms$MIC-1, rep(0, nrow(ms)), tolerance=1e-10) }) context("Rcpp Interface") test_that("Test rcpp interface:", { mydata <- lin.create(1000) mm <- mine(mydata$X, mydata$Y, alpha=0.6, C=15) mm2 <- mine_stat(mydata$X, mydata$Y, alpha=0.6, C=15) expect_equal(mm$MIC, mm2, tolerance=1e-6) }) test_that("Test mineRonevar:", { x <- matrix(rnorm(320*8), nrow=320, ncol=8) m1 <- mine(x[, 1], x[, 2], n.cores=1, alpha=0.6, C=15, normalization=TRUE) mmes <- c("mic", "mas", "mev", "mcn", "mic-r2", "gmic", "tic") m2 <- sapply(mmes, function(y, data){ if(y != "mic-r2"){ res <- mine_stat(data[,1], data[, 2], measure=y, alpha=0.6, C=15, norm=TRUE) } else { r2 <- cor(data[,1], data[, 2]) ** 2 res <- mine_stat(data[,1], data[, 2], measure="mic", alpha=0.6, C=15, norm=TRUE) - r2 } return(res) }, data=x) expect_equal(length(m1), length(m2)) for (i in 1:length(m1)) { expect_equal(m1[[i]], m2[[i]], tolerance=1e-10) } }) test_that("Test measure consistency:", { x <- matrix(rnorm(320*8), nrow=320, ncol=8) m1 <- mine(x[, 1], x[, 2], n.cores=1, alpha=0.6, C=15, normalization=TRUE) for (m in c("mic", "mas", "mev", "gmic", "tic")) { m2 <- mine_stat(x[, 1], x[, 2], alpha=0.6, C=15, measure = m, norm=TRUE) expect_equal(m1[[toupper(m)]], m2, tolerance=1e-10) } }) test_that("Parameters error through Rcpp interface",{ mydata <- lin.create(1000) expect_error(mine_stat(mydata[, 1], mydata[, 2], alpha=-5), NULL) expect_error(mine_stat(mydata[, 1], mydata[, 2], C=-3), NULL) expect_error(mine_stat(mydata[, 1], mydata[, 2], est='ciccio'), NULL) expect_error(mine_stat(mydata[, 1], mydata[, 2], eps=10, measure="mcn")) }) test_that("Array dimension in cstats", { x <- matrix(rnorm(320*8), nrow=320, ncol=8) y <- matrix(rnorm(320*4), nrow=320, ncol=4) y2 <- matrix(rnorm(240*8), nrow=240, ncol=8) mictic <- cstats(x, y, alpha=9, C=5, est="mic_e") expect_error(cstats(x, y2, alpha=9, C=5, est="mic_e")) expect_equal(ncol(mictic), 4) expect_equal(nrow(mictic), 8*4) }) minerva/tests/hs_err_pid16149.log0000644000176200001440000006152413271553720016320 0ustar liggesusers# # A fatal error has been detected by the Java Runtime Environment: # # SIGILL (0x4) at pc=0x000000000830c8e0, pid=16149, tid=68441856 # # JRE version: 6.0_24-b24 # Java VM: OpenJDK 64-Bit Server VM (20.0-b12 mixed mode linux-amd64 compressed oops) # Derivative: IcedTea6 1.11.3 # Distribution: Ubuntu 12.04 LTS, package 6b24-1.11.3-1ubuntu0.12.04.1 # Problematic frame: # J mine.core.GridFinder.BaseCases([Ldata/Point;ILjava/util/Map;[Ljava/lang/Integer;[[Lmine/core/GridFinder$LinkedListNode;[[F[[I)V # # If you would like to submit a bug report, please include # instructions how to reproduce the bug and visit: # https://bugs.launchpad.net/ubuntu/+source/openjdk-6/ # --------------- T H R E A D --------------- Current thread (0x0000000005a55800): JavaThread "main" [_thread_in_Java, id=16150, stack(0x0000000004045000,0x0000000004146000)] siginfo:si_signo=SIGILL: si_errno=0, si_code=1 (ILL_ILLOPC), si_addr=0x000000000830c8e0 Registers: RAX=0x0000000000000000, RBX=0x0000000000000001, RCX=0x0000000000000001, RDX=0x0000000097036ba0 RSP=0x00000000041443e8, RBP=0x0000000000000002, RSI=0x00000000ffffffff, RDI=0x0000000000000001 R8 =0x0000000000000000, R9 =0x0000000000000001, R10=0x0000000000000001, R11=0x0000000000000001 R12=0x0000000000000000, R13=0x00000000970366b0, R14=0x0000000000000001, R15=0x0000000005a55800 RIP=0x000000000830c8e0, EFLAGS=0x0000000000000014, CSGSFS=0x0000000000000000, ERR=0x0000000000000000 TRAPNO=0x0000000000000000 Top of Stack: (sp=0x00000000041443e8) 0x00000000041443e8: 3ff0000000000000 0000000000000018 0x00000000041443f8: 0000000000000009 00000000970366e8 0x0000000004144408: ffffffff00000001 0000000004144518 0x0000000004144418: 00000000041444c8 00000000041444c8 0x0000000004144428: 0000000097036b88 0000000208293dd0 0x0000000004144438: 0000000097036a78 0000000097036968 0x0000000004144448: 0000000006475b93 00000002ff7fffff 0x0000000004144458: 0000000000072c08 0000000000000000 0x0000000004144468: 00000000082a0279 0000000004144518 0x0000000004144478: 000000000829485a 0000000000000001 0x0000000004144488: 00000000041444d0 0000000039677110 0x0000000004144498: 000000000829485a 00000000970366e8 0x00000000041444a8: 0000000097036a78 0000000097036968 0x00000000041444b8: 00000000970366b0 0000000097035af0 0x00000000041444c8: 0000000000000002 0000000097033108 0x00000000041444d8: 00000000041444d8 000000003993aede 0x00000000041444e8: 00000000041445a0 000000003993c7d8 0x00000000041444f8: 0000000000000000 000000003993b0f8 0x0000000004144508: 00000000041444a0 0000000004144570 0x0000000004144518: 00000000041445e8 00000000082949b3 0x0000000004144528: 0000000000000000 0000000000000000 0x0000000004144538: 0000000000000000 0000000097036a78 0x0000000004144548: 0000000097036968 00000000970366e8 0x0000000004144558: 00000000970366b0 0000000097035af0 0x0000000004144568: 0000000097035ad0 0000000000000000 0x0000000004144578: 402e000000000000 00000000041445e8 0x0000000004144588: 0000000000000002 0000000000000003 0x0000000004144598: 0000000097030758 0000000097033108 0x00000000041445a8: 00000000041445a8 0000000039937926 0x00000000041445b8: 0000000004144620 0000000039938c18 0x00000000041445c8: 0000000000000000 0000000039937c08 0x00000000041445d8: 0000000004144570 0000000004144610 Instructions: (pc=0x000000000830c8e0) 0x000000000830c8c0: 5a c0 f3 0f 2a d3 0f 28 ca f3 0f 5e ce f3 0f 5a 0x000000000830c8d0: d2 f3 0f 5a c9 d9 ed 48 83 ec 08 f2 0f 11 0c 24 0x000000000830c8e0: 66 dd 04 24 d9 f1 dd 5c 24 00 f2 0f 10 0c 24 48 0x000000000830c8f0: 83 c4 08 f2 0f 59 ca f2 0f 58 c8 f2 0f 5a c1 41 Register to memory mapping: RAX=0x0000000000000000 is an unknown value RBX=0x0000000000000001 is an unknown value RCX=0x0000000000000001 is an unknown value RDX=0x0000000097036ba0 is an oop [I - klass: {type array int} - length: 2 RSP=0x00000000041443e8 is pointing into the stack for thread: 0x0000000005a55800 RBP=0x0000000000000002 is an unknown value RSI=0x00000000ffffffff is an unknown value RDI=0x0000000000000001 is an unknown value R8 =0x0000000000000000 is an unknown value R9 =0x0000000000000001 is an unknown value R10=0x0000000000000001 is an unknown value R11=0x0000000000000001 is an unknown value R12=0x0000000000000000 is an unknown value R13=0x00000000970366b0 is an oop [Ljava.lang.Integer; - klass: 'java/lang/Integer'[] - length: 9 R14=0x0000000000000001 is an unknown value R15=0x0000000005a55800 is a thread Stack: [0x0000000004045000,0x0000000004146000], sp=0x00000000041443e8, free space=1020k Native frames: (J=compiled Java code, j=interpreted, Vv=VM code, C=native code) J mine.core.GridFinder.BaseCases([Ldata/Point;ILjava/util/Map;[Ljava/lang/Integer;[[Lmine/core/GridFinder$LinkedListNode;[[F[[I)V --------------- P R O C E S S --------------- Java Threads: ( => current thread ) 0x0000000005de6800 JavaThread "Low Memory Detector" daemon [_thread_blocked, id=16161, stack(0x000000000d01e000,0x000000000d11f000)] 0x0000000005de3000 JavaThread "C2 CompilerThread1" daemon [_thread_blocked, id=16160, stack(0x000000000cf1d000,0x000000000d01e000)] 0x0000000005de1000 JavaThread "C2 CompilerThread0" daemon [_thread_blocked, id=16159, stack(0x000000000ce1c000,0x000000000cf1d000)] 0x000000000c299800 JavaThread "Signal Dispatcher" daemon [_thread_blocked, id=16158, stack(0x000000000cd1b000,0x000000000ce1c000)] 0x000000000c240000 JavaThread "Finalizer" daemon [_thread_blocked, id=16157, stack(0x000000000c531000,0x000000000c632000)] 0x0000000005d7e000 JavaThread "Reference Handler" daemon [_thread_blocked, id=16156, stack(0x000000000c430000,0x000000000c531000)] =>0x0000000005a55800 JavaThread "main" [_thread_in_Java, id=16150, stack(0x0000000004045000,0x0000000004146000)] Other Threads: 0x000000000c230800 VMThread [stack: 0x000000000c32f000,0x000000000c430000] [id=16155] 0x000000000c2a1000 WatcherThread [stack: 0x000000000d11f000,0x000000000d220000] [id=16162] VM state:not at safepoint (normal execution) VM Mutex/Monitor currently owned by a thread: None Heap PSYoungGen total 36672K, used 23936K [0x0000000095920000, 0x0000000098200000, 0x00000000be7c0000) eden space 31488K, 76% used [0x0000000095920000,0x0000000097080000,0x00000000977e0000) from space 5184K, 0% used [0x0000000097cf0000,0x0000000097cf0000,0x0000000098200000) to space 5184K, 0% used [0x00000000977e0000,0x00000000977e0000,0x0000000097cf0000) PSOldGen total 83776K, used 0K [0x0000000043bc0000, 0x0000000048d90000, 0x0000000095920000) object space 83776K, 0% used [0x0000000043bc0000,0x0000000043bc0000,0x0000000048d90000) PSPermGen total 21248K, used 3700K [0x00000000395c0000, 0x000000003aa80000, 0x0000000043bc0000) object space 21248K, 17% used [0x00000000395c0000,0x000000003995d270,0x000000003aa80000) Code Cache [0x000000000828f000, 0x00000000084ff000, 0x000000000b28f000) total_blobs=235 nmethods=54 adapters=135 free_code_cache=49818240 largest_free_block=6016 Dynamic libraries: 00400000-00409000 r-xp 00000000 08:01 134133 /usr/lib/jvm/java-6-openjdk-amd64/jre/bin/java 00608000-00609000 r--p 00008000 08:01 134133 /usr/lib/jvm/java-6-openjdk-amd64/jre/bin/java 00609000-0060a000 rw-p 00009000 08:01 134133 /usr/lib/jvm/java-6-openjdk-amd64/jre/bin/java 04000000-04022000 r-xp 00000000 08:01 14421469 /lib/x86_64-linux-gnu/ld-2.15.so 04022000-04023000 rw-s 00000000 08:01 15597706 /tmp/vgdb-pipe-shared-mem-vgdb-16149-by-roberto-on-??? 04023000-04026000 rw-p 00000000 00:00 0 04026000-04027000 r--p 00000000 00:00 0 04027000-04028000 rw-p 00000000 00:00 0 04028000-04030000 rw-s 00000000 08:01 15597707 /tmp/hsperfdata_roberto/16149 04030000-04032000 r--s 0000a000 08:01 14161165 /home/roberto/mpba_scripts_network/minerva/tests/MINE.jar 04032000-04035000 r--s 00077000 08:01 18622507 /usr/lib/jvm/java-6-openjdk-common/jre/lib/ext/localedata.jar 04035000-04037000 r--s 00001000 08:01 18622509 /usr/lib/jvm/java-6-openjdk-common/jre/lib/ext/dnsns.jar 04037000-04039000 r--s 0000f000 08:01 18622505 /usr/lib/jvm/java-6-openjdk-common/jre/lib/ext/pulse-java.jar 04039000-0403d000 r--s 00038000 08:01 18622508 /usr/lib/jvm/java-6-openjdk-common/jre/lib/ext/sunpkcs11.jar 0403d000-0403e000 r--s 00007000 08:01 18882605 /usr/share/java/java-atk-wrapper.jar 0403e000-04041000 r--s 00031000 08:01 18622506 /usr/lib/jvm/java-6-openjdk-common/jre/lib/ext/sunjce_provider.jar 04042000-04045000 rw-p 00000000 00:00 0 04045000-04048000 ---p 00000000 00:00 0 04048000-04150000 rw-p 00000000 00:00 0 04150000-04206000 rw-p 00000000 00:00 0 04206000-04208000 r--s 0000a000 08:01 14161165 /home/roberto/mpba_scripts_network/minerva/tests/MINE.jar 04208000-0420b000 r--s 0007d000 08:01 18622520 /usr/lib/jvm/java-6-openjdk-common/jre/lib/jsse.jar 04222000-04223000 r--p 00022000 08:01 14421469 /lib/x86_64-linux-gnu/ld-2.15.so 04223000-04225000 rw-p 00023000 08:01 14421469 /lib/x86_64-linux-gnu/ld-2.15.so 04225000-04226000 rwxp 00000000 00:00 0 04a25000-04a26000 r-xp 00000000 08:01 18624260 /usr/lib/valgrind/vgpreload_core-amd64-linux.so 04a26000-04c25000 ---p 00001000 08:01 18624260 /usr/lib/valgrind/vgpreload_core-amd64-linux.so 04c25000-04c26000 r--p 00000000 08:01 18624260 /usr/lib/valgrind/vgpreload_core-amd64-linux.so 04c26000-04c27000 rw-p 00001000 08:01 18624260 /usr/lib/valgrind/vgpreload_core-amd64-linux.so 04c27000-04c31000 r-xp 00000000 08:01 18624319 /usr/lib/valgrind/vgpreload_memcheck-amd64-linux.so 04c31000-04e30000 ---p 0000a000 08:01 18624319 /usr/lib/valgrind/vgpreload_memcheck-amd64-linux.so 04e30000-04e31000 r--p 00009000 08:01 18624319 /usr/lib/valgrind/vgpreload_memcheck-amd64-linux.so 04e31000-04e32000 rw-p 0000a000 08:01 18624319 /usr/lib/valgrind/vgpreload_memcheck-amd64-linux.so 04e32000-04e4a000 r-xp 00000000 08:01 14421569 /lib/x86_64-linux-gnu/libpthread-2.15.so 04e4a000-05049000 ---p 00018000 08:01 14421569 /lib/x86_64-linux-gnu/libpthread-2.15.so 05049000-0504a000 r--p 00017000 08:01 14421569 /lib/x86_64-linux-gnu/libpthread-2.15.so 0504a000-0504b000 rw-p 00018000 08:01 14421569 /lib/x86_64-linux-gnu/libpthread-2.15.so 0504b000-0504f000 rw-p 00000000 00:00 0 0504f000-05053000 r-xp 00000000 08:01 18750613 /usr/lib/jvm/java-6-openjdk-amd64/jre/lib/amd64/jli/libjli.so 05053000-05252000 ---p 00004000 08:01 18750613 /usr/lib/jvm/java-6-openjdk-amd64/jre/lib/amd64/jli/libjli.so 05252000-05253000 r--p 00003000 08:01 18750613 /usr/lib/jvm/java-6-openjdk-amd64/jre/lib/amd64/jli/libjli.so 05253000-05254000 rw-p 00004000 08:01 18750613 /usr/lib/jvm/java-6-openjdk-amd64/jre/lib/amd64/jli/libjli.so 05254000-05256000 r-xp 00000000 08:01 14421502 /lib/x86_64-linux-gnu/libdl-2.15.so 05256000-05456000 ---p 00002000 08:01 14421502 /lib/x86_64-linux-gnu/libdl-2.15.so 05456000-05457000 r--p 00002000 08:01 14421502 /lib/x86_64-linux-gnu/libdl-2.15.so 05457000-05458000 rw-p 00003000 08:01 14421502 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00000000 00:00 0 [stack] ffffffffff600000-ffffffffff601000 r-xp 00000000 00:00 0 [vsyscall] VM Arguments: java_command: MINE.jar Spellman.csv 0 Launcher Type: SUN_STANDARD Environment Variables: PATH=/usr/lib/lightdm/lightdm:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games LD_LIBRARY_PATH=/usr/lib/jvm/java-6-openjdk-amd64/jre/lib/amd64/server:/usr/lib/jvm/java-6-openjdk-amd64/jre/lib/amd64:/usr/lib/jvm/java-6-openjdk-amd64/jre/../lib/amd64:/usr/lib/debug LD_PRELOAD=/usr/lib/valgrind/vgpreload_core-amd64-linux.so:/usr/lib/valgrind/vgpreload_memcheck-amd64-linux.so:/usr/lib/valgrind/vgpreload_core-amd64-linux.so:/usr/lib/valgrind/vgpreload_memcheck-amd64-linux.so:/usr/lib/valgrind/vgpreload_core-amd64-linux.so:/usr/lib/valgrind/vgpreload_memcheck-amd64-linux.so SHELL=/bin/bash DISPLAY=:0.0 Signal Handlers: SIGSEGV: [libjvm.so+0x784310], sa_mask[0]=0x7ffbfeff, sa_flags=0x10000004 SIGBUS: [libjvm.so+0x784310], sa_mask[0]=0x7ffbfeff, sa_flags=0x10000004 SIGFPE: [libjvm.so+0x648580], sa_mask[0]=0x7ffbfeff, sa_flags=0x10000004 SIGPIPE: [libjvm.so+0x648580], sa_mask[0]=0x7ffbfeff, sa_flags=0x10000004 SIGXFSZ: [libjvm.so+0x648580], sa_mask[0]=0x7ffbfeff, sa_flags=0x10000004 SIGILL: [libjvm.so+0x648580], sa_mask[0]=0x7ffbfeff, sa_flags=0x10000004 SIGUSR1: SIG_DFL, sa_mask[0]=0x00000000, sa_flags=0x00000000 SIGUSR2: [libjvm.so+0x648730], sa_mask[0]=0x00000000, sa_flags=0x10000004 SIGHUP: [libjvm.so+0x64b250], sa_mask[0]=0x7ffbfeff, sa_flags=0x10000004 SIGINT: [libjvm.so+0x64b250], sa_mask[0]=0x7ffbfeff, sa_flags=0x10000004 SIGTERM: [libjvm.so+0x64b250], sa_mask[0]=0x7ffbfeff, sa_flags=0x10000004 SIGQUIT: [libjvm.so+0x64b250], sa_mask[0]=0x7ffbfeff, sa_flags=0x10000004 --------------- S Y S T E M --------------- OS:Ubuntu 12.04 (precise) uname:Linux 3.2.0-27-generic #43-Ubuntu SMP Fri Jul 6 14:25:57 UTC 2012 x86_64 libc:glibc 2.15 NPTL 2.15 rlimit: STACK 8192k, CORE 0k, NPROC 62651, NOFILE 1044, AS infinity load average:minerva/tests/Spellman.csv,0-vs-1,cv=0.0,B=n^0.6,Status.txt0000644000176200001440000000004113271553720022316 0ustar liggesusers1 calculating: time vs YAL001C...minerva/src/0000755000176200001440000000000013472710557012515 5ustar liggesusersminerva/src/mine_int.h0000644000176200001440000000125313472710256014465 0ustar liggesusers#ifndef MINE_INT_H #define MINE_INT_H const std::map create_measure_map(); const std::map create_est_map(); /* Create a constant map for available measures * of the mine statistic. * Values are coded as follows: * {"mic", 1}, {"mas", 2}, {"mev", 3}, {"mcn", 4}, {"tic", 5}, {"gmic", 6}; * It works with upper case also. */ const std::map MEASURE=create_measure_map(); // const std::map EST=create_est_map(); #include double mine_stat(Rcpp::NumericVector x, Rcpp::NumericVector y, double alpha, double C, Rcpp::String est, Rcpp::String measure, double eps, double p, bool norm); #endif minerva/src/mine.h0000644000176200001440000001170413471734163013617 0ustar liggesusers#ifndef _LIBMINE_H #define _LIBMINE_H #define LIBMINE_VERSION "1.2.4.dev" #define EST_MIC_APPROX 0 /* The original MIC estimator */ #define EST_MIC_E 1 /* MIC_e (TIC_e) estimator */ #define FALSE 0 #define TRUE 1 #ifdef __cplusplus extern "C" { #endif extern char *libmine_version; /* * The mine_problem structure describes the problem. x and y are the two * variables of length n. */ typedef struct mine_problem { int n; double *x; double *y; } mine_problem; /* * The mine_parameter structure describes the MINE parameters. * * If alpha is in (0,1] then B will be max(n^alpha, 4) where n is the number of * samples. If alpha is >=4 then alpha defines directly the B parameter. If * alpha is higher than the number of samples (n) it will be limited to n, so * B = min(alpha, n). * * c determines how many more clumps there will be than columns in every * partition, c=15 meaning that when trying to draw x grid lines on the x-axis, * the algorithm will start with at most 15*x clumps. c must be > 0. * * Est is the estimator, EST_MIC_APPROX and EST_MIC_E. With est=EST_MIC_APPROX, * the mine_*() functions return the original statistics, with est=EST_MIC_E the * equicharacteristic matrix is computed and mine_mic() and mine_tic() will * return MIC_e and TIC_e respectively. */ typedef struct mine_parameter { double alpha; double c; int est; } mine_parameter; /* * The mine_score structure describes the maximum normalized mutual information * scores (i.e. the characteristic matrix if est=EST_MIC_APPROX, the * equicharacteristic matrix instead). M[i][j] contains the score using a grid * partitioning x-values into i+2 bins and y-values into j+2 bins. m and M are * of length n and each M[i] is of length m[i]. */ typedef struct mine_score { int n; /* number of rows of M */ int *m; /* number of cols of M[i] for each i */ double **M; /* the (equi)characteristic matrix */ } mine_score; /* * Computes the (equi)characteristic matrix (i.e. maximum normalized mutual * information scores and returns a mine_score structure. Returns NULL if an * error occurs. */ mine_score *mine_compute_score(mine_problem *prob, mine_parameter *param); /* * Checks the parameters. This function should be called before calling * mine_compute_score(). It returns NULL if the parameters are feasible, * otherwise an error message is returned. See the mine_parameter * documentation. */ char *mine_check_parameter(mine_parameter *param); /* Returns the Maximal Information Coefficient (MIC or MIC_e). */ double mine_mic(mine_score *score); /* Returns the Maximum Asymmetry Score (MAS). */ double mine_mas(mine_score *score); /* Returns the Maximum Edge Value (MEV). */ double mine_mev(mine_score *score); /* Returns the Minimum Cell Number (MCN), with eps >= 0. */ double mine_mcn(mine_score *score, double eps); /* Returns the Minimum Cell Number (MCN) with eps = 1 - MIC. */ double mine_mcn_general(mine_score *score); /* * Returns the Total Information Coefficient (TIC or TIC_e). norm = TRUE * normalizes the Total Information Coefficient, returning values in [0, 1]. */ double mine_tic(mine_score *score, int norm); /* Returns the Generalized Mean Information Coefficient (GMIC). */ double mine_gmic(mine_score *score, double p); /* * This function frees the memory used by a mine_score and destroys the score * structure. */ void mine_free_score(mine_score **score); /* CONVENIENCE STRUCTURES AND FUNCTIONS */ /* Mine matrix, variables x samples. */ typedef struct mine_matrix { double *data; /* matrix in row-major order */ int n; /* number of rows */ int m; /* number of cols */ } mine_matrix; /* * For each statistic, the upper triangle of the matrix is stored by row * (condensed matrix). If m is the number of variables, then for i < j < m, the * statistic between (row) i and j is stored in k = m*i - i*(i+1)/2 - i - 1 + j. * The length of the vectors is n = m*(m-1)/2. */ typedef struct mine_pstats { double *mic; /* condensed matrix */ double *tic; /* condensed matrix */ int n; /* number of elements */ } mine_pstats; /* * For each statistic, the matrix is stored by row. If n and m are the number of * variables in X and Y respectively, then the statistic between the (row) i * (for X) and j (for Y) is stored in k = i*m + j. The length of the vector is * d = n*m. */ typedef struct mine_cstats { double *mic; /* matrix in row-major order */ double *tic; /* matrix in row-major order */ int n; /* number of rows */ int m; /* number of cols */ } mine_cstats; /* * Compute pairwise statistics (MIC and normalized TIC) between variables. */ mine_pstats *mine_compute_pstats(mine_matrix *X, mine_parameter *param); /* * Compute statistics (MIC and normalized TIC) between each pair of the two * collections of variables. */ mine_cstats *mine_compute_cstats(mine_matrix *X, mine_matrix *Y, mine_parameter *param); #ifdef __cplusplus } #endif #endif /* _LIBMINE_H */ minerva/src/mine.c0000644000176200001440000006643413471734163013624 0ustar liggesusers/* * Libmine core library. * * This code is written by Davide Albanese * and Michele Filosi . * * Copyright (C) 2012-2016 Davide Albanese, Copyright (C) 2012 Michele * Filosi, Copyright (C) 2012 Fondazione Bruno Kessler. * * References: * * Original MINE paper: DOI: 10.1126/science.1205438; * * Minepy paper: DOI: 10.1093/bioinformatics/bts707D; * * MIC_e and TIC: DOI: arXiv:1505.02213 and DOI: arXiv:1505.02214; * * GMIC: DOI: arXiv:1308.5712. * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include #include #include #include #include #include "mine.h" char *libmine_version = LIBMINE_VERSION; #define MAX(a, b) (((a) > (b) ? (a) : (b))) #define MIN(a, b) (((a) < (b) ? (a) : (b))) #define TRUE 1 #define FALSE 0 void quicksort(double *a, int *idx, int l, int u) { int i, m, idx_temp; double a_temp; if (l >= u) return; m = l; for (i=l+1; i<=u; i++) { if (a[i] < a[l]) { ++m; idx_temp = idx[m]; idx[m] = idx[i]; idx[i] = idx_temp; a_temp = a[m]; a[m] = a[i]; a[i] = a_temp; } } idx_temp = idx[l]; idx[l] = idx[m]; idx[m] = idx_temp; a_temp = a[l]; a[l] = a[m]; a[m] = a_temp; quicksort(a, idx, l, m-1); quicksort(a, idx, m+1, u); } int *argsort(double *a, int n) { double *a_cpy; int i, *idx; a_cpy = (double *) malloc(n * sizeof(double)); if (a_cpy == NULL) return NULL; idx = (int *) malloc(n * sizeof(int)); if (idx == NULL) { free(a_cpy); return NULL; } /* fill a_cpy */ memcpy(a_cpy, a, n * sizeof(double)); /* fill idx */ for (i=0; i. See line 5 of Algorithm 2, SOM. * * Parameters * c : c_1, ..., c_p * c_log : log(c) * s : s in c_s * t : t in c_t */ double hp3(int *c, double *c_log, int s, int t) { int sum; double total, total_log, prob, prob_log, H = 0.0; if (s == t) return 0.0; total = (double) c[t-1]; total_log = log(total); prob = (double) c[s-1] / total; if (prob != 0) { prob_log = c_log[s-1] - total_log; H -= prob * prob_log; } sum = c[t-1] - c[s-1]; prob = (double) sum / total; if (sum != 0) { prob_log = log((double) sum) - total_log; H -= prob * prob_log; } return H; } /* * Returns the entropy induced by the points on the partition * , Q. See line 5 of Algorithm 2 in SOM. * * Parameters * cumhist : cumulative histogram matrix along P_map * cumhist_log : log(cumhist) * c : c_1, ..., c_p * q : number of rows of cumhist (number of partitions in Q_map) * p : number of cols of cumhist (number of partitions in P_map) * s : s in c_s * t : t in c_t */ double hp3q(int **cumhist, double **cumhist_log, int *c, int q, int p, int s, int t) { int i, sum; double total, total_log, prob, prob_log, H = 0.0; total = (double) c[t-1]; total_log = log(total); for (i=0; i * and Q. See line 13 of Algorithm 2, SOM. * * Parameters * cumhist : cumulative histogram matrix along P_map * c : c_1, ..., c_p * q : number of rows of cumhist (number of partitions * in Q_map) * p : number of cols of cumhist (number of partitions * in P_map) * s : s in c_s * t : t in c_t */ double hp2q(int **cumhist, int *c, int q, int p, int s, int t) { int i, sum; double total, total_log, prob, prob_log, H = 0.0 ; if (s == t) return 0.0; total = (double) (c[t-1] - c[s-1]); total_log = log(total); for (i=0; i {0, ...,q-1}. * See Algorithm 3 in SOM. * * Parameters * dy (IN): y-data sorted in increasing order * n (IN): number of elements of dy * y (IN): an integer greater than 1 * Q_map (OUT) : the map Q. Q_map must be a preallocated vector of * size n * q (OUT) : number of partitions in Q_map. q can be < y * * Returns * 0 */ int EquipartitionYAxis(double *dy, int n, int y, int *Q_map, int *q) { int i, j, s, h, curr; double temp1, temp2; double rowsize = (double) n / (double) y; i = 0; h = 0; curr = 0; while (i < n) { s = 1; for (j=i+1; j= temp2)) { ++curr; h = 0; temp1 = (double) n - (double) i; temp2 = (double) y - (double) curr; rowsize = temp1 / temp2; } for (j=0; j {0, ...,p-1}. * * Parameters * dx (IN) : x-data sorted in increasing order * n (IN) : number of elements of dx * Q_map (IN) : the map Q computed by EquipartitionYAxis sorted in * increasing order by dx-values * P_map (OUT) : the map P. P_map must be a preallocated vector * of size n * p (OUT) : number of partitions in P_map * * Returns * 0 on success, 1 if an error occurs */ int GetClumpsPartition(double *dx, int n, int *Q_map, int *P_map, int *p) { int i, j, flag, c, s; int *Q_tilde; i = 0; c = -1; Q_tilde = (int *) malloc (n * sizeof(int)); if (Q_tilde == NULL) return 1; memcpy(Q_tilde, Q_map, n*sizeof(int)); while (i < n) { s = 1; flag = FALSE; for (j=i+1; j 1) && (flag == TRUE)) { for (j=0; j {0, ...,p-1}. * * Parameters * dx (IN) : x-data sorted in increasing order * n (IN) : number of elements of dx * k_hat (IN) : maximum number of clumps * Q_map (IN) : the map Q computed by EquipartitionYAxis sorted in * increasing order by dx-values * P_map (OUT) : the map P. P_map must be a preallocated vector * of size n * p (OUT) : number of partitions in P_map * * Returns * 0 on success, 1 if an error occurs */ int GetSuperclumpsPartition(double *dx, int n, int k_hat, int *Q_map, int *P_map, int *p) { int i, ret; double *dp; /* clumps */ ret = GetClumpsPartition(dx, n, Q_map, P_map, p); if (ret) return 1; /* superclumps */ if (*p > k_hat) { dp = (double *) malloc (n * sizeof(double)); if (dp == NULL) return 1; for (i=0; i F_max) { I[t][2] = HQ + F; F_max = F; } } } /* * Inductively build the rest of the table of optimal partitions, * Algorithm 2 in SOM, lines 10-17 */ for (l=3; l<=x; l++) { for (t=l; t<=p; t++) { ct = (double) c[t-1]; F_max = -DBL_MAX; for (s=l-1; s<=t; s++) { cs = (double) c[s-1]; F = ((cs/ct) * (I[s][l-1]-HQ)) - (((ct-cs)/ct) * HP2Q[s][t]); if (F > F_max) { I[t][l] = HQ + F; F_max = F; } } } } /* Algorithm 2 in SOM, line 19 */ for (i=p+1; i<=x; i++) I[p][i] = I[p][p]; /* score */ for (i=2; i<=x; i++) score[i-2] = I[p][i] / MIN(log(i), log(q)); /* start frees */ for (i=0; i<=p; i++) free(HP2Q[i]); free(HP2Q); for (i=0; i<=p; i++) free(I[i]); free(I); for (i=0; ialpha > 0.0) && (param->alpha <= 1.0)) B = MAX(pow(prob->n, param->alpha), 4); else if (param->alpha >= 4) B = MIN(param->alpha, prob->n); else goto error_score; score = (mine_score *) malloc (sizeof(mine_score)); if (score == NULL) goto error_score; score->n = MAX((int) floor(B/2.0), 2) - 1; score->m = (int *) malloc(score->n * sizeof(int)); if (score->m == NULL) goto error_score_m; for (i=0; in; i++) score->m[i] = (int) floor((double) B / (double) (i+2)) - 1; score->M = (double **) malloc (score->n * sizeof(double *)); if (score->M == NULL) goto error_score_M; for (i=0; in; i++) { score->M[i] = (double *) malloc ((score->m[i]) * sizeof(double)); if (score->M[i] == NULL) { for (j=0; jM[j]); goto error_score_M_i; } } return score; error_score_M_i: free(score->M); error_score_M: free(score->m); error_score_m: free(score); error_score: return NULL; } /* See mine.h */ mine_score *mine_compute_score(mine_problem *prob, mine_parameter *param) { int i, j, k, p, q, ret; double *xx, *yy, *xy, *yx, *M_temp; int *ix, *iy; int *Q_map_temp, *Q_map, *P_map; mine_score *score; score = init_score(prob, param); if (score == NULL) goto error_score; xx = (double *) malloc (prob->n * sizeof(double)); if (xx == NULL) goto error_xx; yy = (double *) malloc (prob->n * sizeof(double)); if (yy == NULL) goto error_yy; xy = (double *) malloc (prob->n * sizeof(double)); if (xy == NULL) goto error_xy; yx = (double *) malloc (prob->n * sizeof(double)); if (yx == NULL) goto error_yx; Q_map_temp = (int *) malloc (prob->n * sizeof(int)); if (Q_map_temp == NULL) goto error_Q_temp; Q_map = (int *) malloc (prob->n * sizeof(int)); if (Q_map == NULL) goto error_Q; P_map = (int *) malloc (prob->n * sizeof(int)); if (P_map == NULL) goto error_P; ix = argsort(prob->x, prob->n); if (ix == NULL) goto error_ix; iy = argsort(prob->y, prob->n); if (iy == NULL) goto error_iy; M_temp = (double *)malloc ((score->m[0]) * sizeof(double)); if (M_temp == NULL) goto error_M_temp; /* build xx, yy, xy, yx */ for (i=0; in; i++) { xx[i] = prob->x[ix[i]]; yy[i] = prob->y[iy[i]]; xy[i] = prob->x[iy[i]]; yx[i] = prob->y[ix[i]]; } /* x vs. y */ for (i=0; in; i++) { k = MAX((int) (param->c * (score->m[i]+1)), 1); ret = EquipartitionYAxis(yy, prob->n, i+2, Q_map, &q); if (ret) goto error_0; /* sort Q by x */ for (j=0; jn; j++) Q_map_temp[iy[j]] = Q_map[j]; for (j=0; jn; j++) Q_map[j] = Q_map_temp[ix[j]]; ret = GetSuperclumpsPartition(xx, prob->n, k, Q_map, P_map, &p); if (ret) goto error_0; if (param->est == EST_MIC_APPROX) ret = OptimizeXAxis(xx, yx, prob->n, Q_map, q, P_map, p, score->m[i]+1, score->M[i]); else /* EST_MIC_E */ ret = OptimizeXAxis(xx, yx, prob->n, Q_map, q, P_map, p, MIN(i+2, score->m[i]+1), score->M[i]); if (ret) goto error_0; } /* y vs. x */ for (i=0; in; i++) { k = MAX((int) (param->c * (score->m[i]+1)), 1); ret = EquipartitionYAxis(xx, prob->n, i+2, Q_map, &q); if (ret) goto error_0; /* sort Q by y */ for (j=0; jn; j++) Q_map_temp[ix[j]] = Q_map[j]; for (j=0; jn; j++) Q_map[j] = Q_map_temp[iy[j]]; ret = GetSuperclumpsPartition(yy, prob->n, k, Q_map, P_map, &p); if (ret) goto error_0; if (param->est == EST_MIC_APPROX) ret = OptimizeXAxis(yy, xy, prob->n, Q_map, q, P_map, p, score->m[i]+1, M_temp); else /* EST_MIC_E */ ret = OptimizeXAxis(yy, xy, prob->n, Q_map, q, P_map, p, MIN(i+2, score->m[i]+1), M_temp); if (ret) goto error_0; if (param->est == EST_MIC_APPROX) for (j=0; jm[i]; j++) score->M[j][i] = MAX(M_temp[j], score->M[j][i]); else /* EST_MIC_E */ for (j=0; jm[i]); j++) score->M[j][i] = M_temp[j]; } free(M_temp); free(iy); free(ix); free(P_map); free(Q_map); free(Q_map_temp); free(yx); free(xy); free(yy); free(xx); return score; error_0: free(M_temp); error_M_temp: free(iy); error_iy: free(ix); error_ix: free(P_map); error_P: free(Q_map); error_Q: free(Q_map_temp); error_Q_temp: free(yx); error_yx: free(xy); error_xy: free(yy); error_yy: free(xx); error_xx: for (i=0; in; i++) free(score->M[i]); free(score->M); free(score->m); free(score); error_score: return NULL; } /* See mine.h */ char *mine_check_parameter(mine_parameter *param) { if (((param->alpha <= 0.0) || (param->alpha > 1.0)) && (param->alpha < 4.0)) return "alpha must be in (0.0, 1.0] or >= 4.0"; if (param->c <= 0.0) return "c must be > 0.0"; if ((param->est != EST_MIC_APPROX) && (param->est != EST_MIC_E)) return "unknown estimator"; return NULL; } /* See mine.h */ double mine_mic(mine_score *score) { int i, j; double score_max = 0.0; for (i=0; in; i++) for (j=0; jm[i]; j++) if (score->M[i][j] > score_max) score_max = score->M[i][j]; return score_max; } /* See mine.h */ double mine_mas(mine_score *score) { int i, j; double score_curr; double score_max = 0.0; for (i=0; in; i++) for (j=0; jm[i]; j++) { score_curr = fabs(score->M[i][j] - score->M[j][i]); if (score_curr > score_max) score_max = score_curr; } return score_max; } /* See mine.h */ double mine_mev(mine_score *score) { int i, j; double score_max = 0.0; for (i=0; in; i++) for (j=0; jm[i]; j++) if (((j==0) || (i==0)) && score->M[i][j] > score_max) score_max = score->M[i][j]; return score_max; } /* See mine.h */ double mine_mcn(mine_score *score, double eps) { int i, j; double log_xy; double score_min = DBL_MAX; double delta = 0.0001; /* avoids overestimation of mcn */ double mic = mine_mic(score); for (i=0; in; i++) for (j=0; jm[i]; j++) { log_xy = log((i+2) * (j+2)) / log(2.0); if (((score->M[i][j]+delta) >= ((1.0 - eps) * mic)) && (log_xy < score_min)) score_min = log_xy; } return score_min; } /* See mine.h */ double mine_mcn_general(mine_score *score) { int i, j; double log_xy; double score_min = DBL_MAX; double delta = 0.0001; /* avoids overestimation of mcn */ double mic = mine_mic(score); for (i=0; in; i++) for (j=0; jm[i]; j++) { log_xy = log((i+2) * (j+2)) / log(2.0); if (((score->M[i][j]+delta) >= (mic * mic)) && (log_xy < score_min)) score_min = log_xy; } return score_min; } /* See mine.h */ double mine_tic(mine_score *score, int norm) { int i, j, k=0; double tic = 0.0; for (i=0; in; i++) for (j=0; jm[i]; j++) { tic += score->M[i][j]; k++; } if (norm) tic /= k; return tic; } /* See mine.h */ double mine_gmic(mine_score *score, double p) { int i, j, k, Z, B; mine_score *score_sub, *C_star; double gmic; /* alloc score_sub */ score_sub = (mine_score *) malloc (sizeof(mine_score)); /* alloc C_star */ C_star = (mine_score *) malloc (sizeof(mine_score)); C_star->m = (int *) malloc(score->n * sizeof(int)); C_star->M = (double **) malloc (score->n * sizeof(double *)); for (i=0; in; i++) C_star->M[i] = (double *) malloc ((score->m[i]) * sizeof(double)); /* prepare score_sub */ score_sub->M = score->M; /* prepare C_star */ C_star->n = score->n; for (i=0; in; i++) C_star->m[i] = score->m[i]; /* compute C_star */ for (i=0; in; i++) for (j=0; jm[i]; j++) { B = (i+2) * (j+2); score_sub->n = MAX((int) floor(B/2.0), 2) - 1; score_sub->m = (int *) malloc(score_sub->n * sizeof(int)); for (k=0; kn; k++) score_sub->m[k] = (int) floor((double) B / (double) (k+2)) - 1; C_star->M[i][j] = mine_mic(score_sub); free(score_sub->m); } /* p=0 -> geometric mean */ if (p == 0.0) { Z = 0; gmic = 1.0; for (i=0; in; i++) for (j=0; jm[i]; j++) { gmic *= C_star->M[i][j]; Z++; } gmic = pow(gmic, (double) Z); } /* p!=0 -> generalized mean */ else { Z = 0; gmic = 0.0; for (i=0; in; i++) for (j=0; jm[i]; j++) { gmic += pow(C_star->M[i][j], p); Z++; } gmic /= (double) Z; gmic = pow(gmic, 1.0/p); } free(score_sub); if (C_star->n != 0) { free(C_star->m); for (i=0; in; i++) free(C_star->M[i]); free(C_star->M); } free(C_star); return gmic; } /* See mine.h */ void mine_free_score(mine_score **score) { int i; mine_score *score_ptr = *score; if (score_ptr != NULL) { if (score_ptr->n != 0) { free(score_ptr->m); for (i=0; in; i++) free(score_ptr->M[i]); free(score_ptr->M); } free(score_ptr); score_ptr = NULL; } } mine_pstats *mine_compute_pstats(mine_matrix *X, mine_parameter *param) { int i, j, k; mine_problem prob; mine_score *score; mine_pstats *stats; /* Allocate memory for stats */ stats = (mine_pstats *) malloc(sizeof(mine_pstats)); stats->n = (X->n * (X->n-1)) / 2; stats->mic = (double *) malloc(stats->n * sizeof(double)); stats->tic = (double *) malloc(stats->n * sizeof(double)); k = 0; prob.n = X->m; for (i=0; in-1; i++) { prob.x = &X->data[i*X->m]; for (j=i+1; jn; j++) { prob.y = &X->data[j*X->m]; score = mine_compute_score(&prob, param); stats->mic[k] = mine_mic(score); stats->tic[k] = mine_tic(score, TRUE); mine_free_score(&score); k++; } } return stats; } mine_cstats *mine_compute_cstats(mine_matrix *X, mine_matrix *Y, mine_parameter *param) { int i, j, k; mine_cstats *stats; mine_problem prob; mine_score *score; if (X->m != Y->m) return NULL; /* Allocate memory for stats */ stats = (mine_cstats *) malloc(sizeof(mine_cstats)); stats->n = X->n; stats->m = Y->n; stats->mic = (double *) malloc((stats->n * stats->m) * sizeof(double)); stats->tic = (double *) malloc((stats->n * stats->m) * sizeof(double)); k = 0; prob.n = X->m; for (i=0; in; i++) { prob.x = &X->data[i*X->m]; for (j=0; jn; j++) { prob.y = &Y->data[j*Y->m]; score = mine_compute_score(&prob, param); stats->mic[k] = mine_mic(score); stats->tic[k] = mine_tic(score, TRUE); mine_free_score(&score); k++; } } return stats; } minerva/src/mine_interface.c0000644000176200001440000001451313470715275015635 0ustar liggesusers/* This code is written by Michele Filosi Roberto Visintainer . 2012 This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . */ #include #include #include "mine.h" #include #include double pearson(mine_problem *myprobl){ double r=0.0, xmean=0.0, ymean=0.0; double sx=0.0, sy=0.0; int i; for (i=0; in; i++){ xmean += myprobl->x[i]; ymean += myprobl->y[i]; } xmean /= myprobl->n; ymean /= myprobl->n; for (i=0; in; i++){ sx += (myprobl->x[i] - xmean) * (myprobl->x[i] - xmean); sy += (myprobl->y[i] - ymean) * (myprobl->y[i] - ymean); } sx = sqrt((sx/myprobl->n)); sy = sqrt((sy/myprobl->n)); for (i=0; in; i++) r += (((myprobl->x[i] - xmean)/sx) * ((myprobl->y[i] - ymean)/sy)); r /= myprobl->n; return(r * r); } SEXP mineRonevar (SEXP x, SEXP y, SEXP alpha, SEXP C, SEXP eps, SEXP est, SEXP normalization){ double *restmp; double EPS; int nn; mine_problem *prob; mine_parameter *param; mine_score *minescore; SEXP res; /* normalization */ nn = asInteger(normalization); PROTECT(alpha = coerceVector(alpha,REALSXP)); PROTECT(C = coerceVector(C,INTSXP)); PROTECT(res=allocVector(REALSXP,7)); restmp=REAL(res); param = (mine_parameter *) Calloc(1,mine_parameter); param->alpha=asReal(alpha); param->c=asReal(C); param->est=asInteger(est); prob = (mine_problem *) Calloc(1,mine_problem); prob->n=length(x); prob->x=REAL(x); prob->y=REAL(y); minescore=mine_compute_score(prob,param); restmp[0]=mine_mic(minescore); restmp[1]=mine_mas(minescore); restmp[2]=mine_mev(minescore); if (!isNull(eps)){ EPS=asReal(eps); restmp[3]=mine_mcn(minescore,EPS); }else{ restmp[3]=mine_mcn_general(minescore); } restmp[4]=restmp[0] - pearson(prob); restmp[5]=mine_gmic(minescore, -1); restmp[6]=mine_tic(minescore, nn); /* Free */ Free(prob); Free(param); mine_free_score(&minescore); UNPROTECT(3); return(res); } SEXP mineRall (SEXP x, SEXP nrx, SEXP ncx, SEXP alpha, SEXP C, SEXP eps, SEXP est, SEXP normalization) { R_len_t i, j, rx, cx; int nn; double score, EPS; double **pointers; mine_problem *prob; mine_parameter *param; mine_score *minescore; SEXP res, mydim, resmic, resmas, resmev, resmcn, resmicmr, resgmic, restic, names; param = (mine_parameter *) Calloc(1,mine_parameter); param->alpha=asReal(alpha); param->c=asReal(C); param->est=asInteger(est); /* Matrix dimension */ rx=asInteger(nrx); cx=asInteger(ncx); /* normalization */ nn=asInteger(normalization); PROTECT(x=coerceVector(x,REALSXP)); /* Initialize data matrix */ pointers = (double **) Calloc(cx, double *); for (i = 0; in=rx; for (i = 0; ix=pointers[i]; for (j = i; jy=pointers[j]; /* Computing MINE scores */ minescore=mine_compute_score(prob,param); score=mine_mic(minescore); REAL(resmic)[(cx*j) + i] = score; REAL(resmic)[(cx*i) + j] = score; score-=pearson(prob); REAL(resmicmr)[(cx*j) + i] = score; REAL(resmicmr)[(cx*i) + j] = score; score=mine_mas(minescore); REAL(resmas)[(cx*j) + i] = score; REAL(resmas)[(cx*i) + j] = score; score=mine_mev(minescore); REAL(resmev)[(cx*j) + i] = score; REAL(resmev)[(cx*i) + j] = score; if (!isNull(eps)){ EPS=asReal(eps); score=mine_mcn(minescore,EPS); }else{ score=mine_mcn_general(minescore); } REAL(resmcn)[(cx*j) + i] = score; REAL(resmcn)[(cx*i) + j] = score; score=mine_gmic(minescore, -1); REAL(resgmic)[(cx*j) + i] = score; REAL(resgmic)[(cx*i) + j] = score; score=mine_tic(minescore, nn); REAL(restic)[(cx*j) + i] = score; REAL(restic)[(cx*i) + j] = score; /* Free score */ mine_free_score(&minescore); } } /* Set matrix dimension to be passed to R object */ PROTECT(mydim=allocVector(INTSXP, 2)); INTEGER(mydim)[0] = cx; INTEGER(mydim)[1] = cx; setAttrib(resmic, R_DimSymbol, mydim); setAttrib(resmas, R_DimSymbol, mydim); setAttrib(resmev, R_DimSymbol, mydim); setAttrib(resmcn, R_DimSymbol, mydim); setAttrib(resmicmr, R_DimSymbol, mydim); setAttrib(resgmic, R_DimSymbol, mydim); setAttrib(restic, R_DimSymbol, mydim); PROTECT(names=allocVector(STRSXP,7)); SET_STRING_ELT(names,0,mkChar("MIC")); SET_STRING_ELT(names,1,mkChar("MAS")); SET_STRING_ELT(names,2,mkChar("MEV")); SET_STRING_ELT(names,3,mkChar("MCN")); SET_STRING_ELT(names,4,mkChar("MICR2")); SET_STRING_ELT(names,5,mkChar("GMIC")); SET_STRING_ELT(names,6,mkChar("TIC")); setAttrib(res, R_NamesSymbol,names); /* Free memeory */ UNPROTECT(11); Free(pointers); Free(param); Free(prob); /* Return a named list of 4 matrix */ return(res); } minerva/src/mine_int.cpp0000644000176200001440000003270513472710256015026 0ustar liggesusers#include #include #include #include #include "mine.h" #include "mine_int.h" /* DEFINE CONSTANT MAPS FOR MEASURE AND EST */ //const std::map MEASURE=create_measure_map(); //({{"mic", 1}, {"mas", 2}, {"mev", 3}, {"mcn", 4}, {"tic", 5}, {"gmic", 6}}); //const std::map EST=create_est_map(); //({{"mic_approx", 0}, {"mic_e", 1}}); const std::map create_measure_map() { std::map MEASURE; // {{"mic", 1}, {"mas", 2}, {"mev", 3}, {"mcn", 4}, {"tic", 5}, {"gmic", 6}} MEASURE["mic"]=1; MEASURE["mas"]=2; MEASURE["mev"]=3; MEASURE["mcn"]=4; MEASURE["tic"]=5; MEASURE["gmic"]=6; MEASURE["MIC"]=1; MEASURE["MAS"]=2; MEASURE["MEV"]=3; MEASURE["MCN"]=4; MEASURE["TIC"]=5; MEASURE["GMIC"]=6; return(MEASURE); } const std::map create_est_map() { std::map EST; EST["mic_approx"]=0; EST["mic_e"]=1; return(EST); } // [[Rcpp::depends(RcppArmadillo)]] using namespace Rcpp; /* Switch between available measure : * mic; mas; mev; mcn; tic; gmic */ int switch_measure(String measure) { int res=0; if (MEASURE.find(measure)!=MEASURE.end()) res = MEASURE.find(measure)->second; return(res); } /* Switch between possible estimators * est = 0 -> est = 'mic_approx' * est = 1 -> est = 'mic_e' */ int switch_est(String est) { int res = -1; if (EST.find(est)!=EST.end()) res = EST.find(est)->second; return(res); } /* Handle eps parameter * if eps not in (0, 1) throws an error... */ char *check_eps(double eps) { if ((eps < 0.0) | (eps > 1.0)) return (char *)"'eps' must be > 0.0 and < 1.0"; return NULL; } /* Helper funtion to convert from R Matrix to mine_matrix structure * Not needed anymore. All the computation handled by pointers * between R memory and C memory */ mine_matrix *rMattomine(NumericMatrix x) { mine_matrix *X; X = (mine_matrix *) Calloc(1, mine_matrix); X->data=REAL(x); X->n=x.ncol(); X->m=x.nrow(); return(X); } //' This is an helper function to compute one \code{mine} statistic. //' It take two vectors of the same dimension as an input. //' //' @param x Numeric Vector of size \code{n} //' @param y Numeric Vector of size \code{n} //' @param alpha numeric value representing parameter for the mine statistic see \code{\link[minerva]{mine}} //' @param C c parameter for the mine statistic see \code{\link[minerva]{mine}} //' @param est character estimation parameter for the mine statistic. //' Possible values are \code{"mic_approx"} or \code{"mic_e"} //' @param measure integer indicating which measure to return //' available measures are: \code{mic, mas, mev, mcn, tic, gmic}. The string could be also uppercase. //' For measure \code{mic-r2} see details. //' @param eps eps value for MCN statistic should be in (0,1). If NA (default) is passed then the normal MCN statistic is returned. //' @param p probability for the generalized mic //' @param norm boolean if require normalization between 0 and 1 for the \code{tic} statistic //' @details This is a wrapper function to compute the mine statistic between two variables. //' for more details on the available measure and the meaning of the other parameters see also the //' documentation for the \code{\link[minerva]{mine}} function. //' //' For measure \code{mic-r2} use the Pearson R coefficient score \code{\link[stats]{cor}} and the measure \code{mic}. //' See the example below. //' @seealso \code{\link[minerva]{mine}} //' @examples //' x <- runif(10); y <- 3*x+2; //' mine_stat(x,y, measure="mic") //' //' ## Measure mic-r2 //' x <- matrix(rnorm(20), ncol=2, nrow=10) //' mmic <- mine_stat(x[,1], x[,2], measure="mic") //' r2 <- cor(x[,1], x[,2]) //' //' mmic - r2**2 //' //' @export // [[Rcpp::export]] double mine_stat(NumericVector x, NumericVector y, double alpha =0.6, double C=15, String est="mic_approx", String measure="mic", double eps=NA_REAL, double p=-1, bool norm=false) { mine_problem prob; mine_score *minescore; mine_parameter param; char *err; double res; int m, lest; // int nnorm = norm==true; /* Convert measure and est parameter */ lest = switch_est(est); m = switch_measure(measure); /* Allocate parameter class */ // param = (mine_parameter *) Calloc(1,mine_parameter); param.alpha=alpha; param.c=C; param.est=lest; /* Check parameteres for MINE statistic */ err = mine_check_parameter(¶m); if (err) stop(err); /* Check vector dimension compatibility */ if (x.length() != y.length()) stop("Not conformable arrays!"); /* Allocate problem structure for MINE */ // prob = (mine_problem *) Calloc(1,mine_problem); prob.n=x.size(); prob.x=x.begin(); prob.y=y.begin(); /* Compute MIC score */ minescore = mine_compute_score(&prob, ¶m); /* Check eps for MCN */ err = check_eps(eps); /* Switch between measure to return */ switch(m){ case 1: res = mine_mic(minescore); break; case 2: res = mine_mas(minescore); break; case 3: res = mine_mev(minescore); break; case 4: if (err){ stop(err); } else { if (!std::isnan(eps)) res = mine_mcn(minescore, eps); else res = mine_mcn_general(minescore); } break; case 5: res = mine_tic(minescore, norm); break; case 6: res = mine_gmic(minescore, p); break; case 0: res = NA_REAL; break; default: res = NA_REAL; }; /* Free memory */ mine_free_score(&minescore); return(res); } //' Compute pairwise statistics (MIC and normalized TIC) between variables //' (convenience function). //' //' For each statistic, the upper triangle of the matrix is stored by row //' (condensed matrix). If m is the number of variables, then for i < j < m, the //' statistic between (col) i and j is stored in k = m*i - i*(i+1)/2 - i - 1 + j. //' The length of the vectors is n = m*(m-1)/2. //' @inheritParams cstats //' @return //' A matrix of (n x (n-1)/2) rows and 4 columns. The first and second column are //' the indexes relative to the columns in the input matrix \code{x} for which the statistic is computed for. //' Column 3 contains the MIC statistic, while column 4 contains the normalized TIC statistic. //' @examples //' ## Create a matrix of random numbers //' ## 10 variables x 100 samples //' x <- matrix(rnorm(1000), ncol=10) //' res <- pstats(x) //' //' head(res) //' //' @export // [[Rcpp::export]] NumericMatrix pstats(NumericMatrix x, double alpha=0.6, double C=15, String est="mic_approx") { mine_parameter param; mine_matrix mmat; mine_pstats *pstatscore; int i, j; int k=0; int nr=x.nrow(); int nc=x.ncol(); int lest; char *err; /* Set EST parameter */ lest = switch_est(est); /* Allocate parameters for computation */ // param = (mine_parameter *) Calloc(1,mine_parameter); param.alpha=alpha; param.c=C; param.est=lest; /* Check parameteres for MINE statistic */ err = mine_check_parameter(¶m); if (err) stop(err); /*Prepare data structure */ // mmat = (mine_matrix *) Calloc (1, mine_matrix); mmat.data = x.begin(); mmat.m = nr; mmat.n = nc; pstatscore = mine_compute_pstats(&mmat, ¶m); NumericMatrix mres(pstatscore->n, 4); for (i=0; in; i++) { // Rcout << pstats->mic[i] << std::endl; mres(i,2) = pstatscore->mic[i]; mres(i,3) = pstatscore->tic[i]; } /* Fill index of variable computed 1-based*/ for (i=0; i=4 if alpha is in (0,1] then B will be max(n^alpha, 4) where n is the //' number of samples. If alpha is >=4 then alpha defines directly the B //' parameter. If alpha is higher than the number of samples (n) it will be //' limited to be n, so B = min(alpha, n). //' @param C number (> 0) determines how many more clumps there will be than columns in //' every partition. Default value is 15, meaning that when trying to //' draw x grid lines on the x-axis, the algorithm will start with at //' most 15*x clumps. //' @param est string ("mic_approx", "mic_e") estimator. //' With est="mic_approx" the original MINE statistics will //' be computed, with est="mic_e" the equicharacteristic matrix is //' is evaluated and MIC_e and TIC_e are returned. //' //' @return list of two elements: //' MIC: the MIC statistic matrix (n x p). //' TIC: the normalized TIC statistic matrix (n x p). //' @examples //' x <- matrix(rnorm(2560), ncol=8, nrow=320) //' y <- matrix(rnorm(1280), ncol=4, nrow=320) //' //' mictic <- cstats(x, y, alpha=9, C=5, est="mic_e") //' head(mictic) //' //' @export // [[Rcpp::export]] NumericMatrix cstats(NumericMatrix x, NumericMatrix y, double alpha=0.6, double C=15, String est="mic_approx") { mine_parameter param; mine_matrix x_, y_; mine_cstats *cres; char *err; int i, j, l; int lest; /* Set EST parameter */ lest = switch_est(est); /* Allocate parameters for computation */ param.alpha=alpha; param.c=C; param.est=lest; /* Check parameteres for MINE statistic */ err = mine_check_parameter(¶m); if (err) stop(err); /*Prepare data structure */ x_.data = x.begin(); x_.m = x.nrow(); x_.n = x.ncol(); y_.data = y.begin(); y_.m = y.nrow(); y_.n = y.ncol(); /* Compute pairwise statistic */ cres = mine_compute_cstats(&x_, &y_, ¶m); if (!cres) stop("Not conformable arrays"); /* Matrix for results */ NumericMatrix rres(x_.n * y_.n, 4); /* Put result in the result matrix to pass to R */ l = (cres->n * cres->m); for (i=0; imic[i]; rres(i, 3) = cres->tic[i]; } /* Fill Indices */ l = 0; for (i=0; in; i++) { for(j=0; jm; j++) { rres(l, 0) = i+1; rres(l, 1) = j+1; l++; } } /* Set colnames of the matrix */ StringVector resnames = StringVector::create("VarX", "VarY", "MIC", "TIC"); colnames(rres) = resnames; return(rres); } /* Do we need this function? guess not...you can use mine */ NumericMatrix mine_allvar_onemeasure(NumericMatrix x, double alpha=0.6, double C=15, String est="mic_approx", String measure="mic", double eps=0.0, double p=-1, bool norm=false) { int v; int i, j, it; double res; /* Set matrix dimension */ v = x.ncol(); NumericMatrix ret((v * (v-1))/2, 3); it=0; for (i=0; i<(v-1); i++) { for (j=i+1; jalpha=alpha; param->c=C; param->est=1; // est == "mic_e" /* Check Parameters */ err = mine_check_parameter(param); if (err){ stop(err); } /* Set up mine problem */ prob = (mine_problem *) Calloc(1,mine_problem); prob->n=n; /* set seed */ set_seed(seed); /* Run permutation */ for (i=0; ix=REAL(x1); prob->y=REAL(x2); /* Compute score */ minescore = mine_compute_score(prob, param); restic[i] = mine_tic(minescore, true); /* Free score struct */ mine_free_score(&minescore); } /* Free memory */ Free(param); Free(prob); return(restic); } minerva/src/RcppExports.cpp0000644000176200001440000000776213472677456015540 0ustar liggesusers// Generated by using Rcpp::compileAttributes() -> do not edit by hand // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 #include #include using namespace Rcpp; // mine_stat double mine_stat(NumericVector x, NumericVector y, double alpha, double C, String est, String measure, double eps, double p, bool norm); RcppExport SEXP _minerva_mine_stat(SEXP xSEXP, SEXP ySEXP, SEXP alphaSEXP, SEXP CSEXP, SEXP estSEXP, SEXP measureSEXP, SEXP epsSEXP, SEXP pSEXP, SEXP normSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< NumericVector >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); Rcpp::traits::input_parameter< double >::type C(CSEXP); Rcpp::traits::input_parameter< String >::type est(estSEXP); Rcpp::traits::input_parameter< String >::type measure(measureSEXP); Rcpp::traits::input_parameter< double >::type eps(epsSEXP); Rcpp::traits::input_parameter< double >::type p(pSEXP); Rcpp::traits::input_parameter< bool >::type norm(normSEXP); rcpp_result_gen = Rcpp::wrap(mine_stat(x, y, alpha, C, est, measure, eps, p, norm)); return rcpp_result_gen; END_RCPP } // pstats NumericMatrix pstats(NumericMatrix x, double alpha, double C, String est); RcppExport SEXP _minerva_pstats(SEXP xSEXP, SEXP alphaSEXP, SEXP CSEXP, SEXP estSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type x(xSEXP); Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); Rcpp::traits::input_parameter< double >::type C(CSEXP); Rcpp::traits::input_parameter< String >::type est(estSEXP); rcpp_result_gen = Rcpp::wrap(pstats(x, alpha, C, est)); return rcpp_result_gen; END_RCPP } // cstats NumericMatrix cstats(NumericMatrix x, NumericMatrix y, double alpha, double C, String est); RcppExport SEXP _minerva_cstats(SEXP xSEXP, SEXP ySEXP, SEXP alphaSEXP, SEXP CSEXP, SEXP estSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type x(xSEXP); Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); Rcpp::traits::input_parameter< double >::type C(CSEXP); Rcpp::traits::input_parameter< String >::type est(estSEXP); rcpp_result_gen = Rcpp::wrap(cstats(x, y, alpha, C, est)); return rcpp_result_gen; END_RCPP } // mictools_null NumericVector mictools_null(NumericMatrix x, double alpha, double C, int nperm, int seed); RcppExport SEXP _minerva_mictools_null(SEXP xSEXP, SEXP alphaSEXP, SEXP CSEXP, SEXP npermSEXP, SEXP seedSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type x(xSEXP); Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); Rcpp::traits::input_parameter< double >::type C(CSEXP); Rcpp::traits::input_parameter< int >::type nperm(npermSEXP); Rcpp::traits::input_parameter< int >::type seed(seedSEXP); rcpp_result_gen = Rcpp::wrap(mictools_null(x, alpha, C, nperm, seed)); return rcpp_result_gen; END_RCPP } RcppExport SEXP mineRall(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); RcppExport SEXP mineRonevar(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); static const R_CallMethodDef CallEntries[] = { {"_minerva_mine_stat", (DL_FUNC) &_minerva_mine_stat, 9}, {"_minerva_pstats", (DL_FUNC) &_minerva_pstats, 4}, {"_minerva_cstats", (DL_FUNC) &_minerva_cstats, 5}, {"_minerva_mictools_null", (DL_FUNC) &_minerva_mictools_null, 5}, {"mineRall", (DL_FUNC) &mineRall, 8}, {"mineRonevar", (DL_FUNC) &mineRonevar, 7}, {NULL, NULL, 0} }; RcppExport void R_init_minerva(DllInfo *dll) { R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); R_useDynamicSymbols(dll, FALSE); } minerva/NAMESPACE0000644000176200001440000000042613472710527013144 0ustar liggesusers# Generated by roxygen2: do not edit by hand export(cstats) export(mic_strength) export(mictools) export(mictools_null) export(mine) export(mine_stat) export(pstats) import(parallel) importFrom(Rcpp,sourceCpp) importFrom(stats,na.omit) importFrom(stats,var) useDynLib(minerva) minerva/NEWS0000644000176200001440000000272613472710454012430 0ustar liggesusersV. 1.5.8 ------ - Solved bug in choosing measure for mine_stat function - Solved default parameter handling for NA in the mine_stat function V. 1.5.7 ------ - Included new versione of libmine - Solved memory allocation issue causing i386 system error - Improve memory handling R interface V. 1.5.4 ------ - Added function for the mictools pipeline - Updated parameter in mine function (adding tic_e normalization) - Improve extraction of one variable at the time - mine_compute_pstat -> pstats - mine_compute_cstat -> cstats V. 1.5.0 ------ - Added support for extracting only one statistic without computing all of them - Update documentation - Added gmic and tic statistic - Added helper function to compute pairwise statistic of features in a matrix - Added helper function to compute pairwise statistic between features of two matrices V. 1.4.7 ------ - Added new mine estimator V. 1.4.5 ------ - Improve NA control in input - Added support to GMIC and TIC from upgrade of the original paper - Added citation for the package V. 1.4 ------ Added parameter epsilon to the computation of MCN measure v. 1.3 ------ Update core function for mine computation High speed improving v. 1.2 ------ * added measure MIC-R^2 v. 1.1 ------ * mine script optimized avoiding indexes preallocation * computation of maximum number of clumps fixed * now R>=2.14 is required: the Windows version will work only for 32 bit version available for R>=2.14 v. 1.0 ------ * minerva has been released minerva/data/0000755000176200001440000000000013271553720012631 5ustar liggesusersminerva/data/Spellman.csv.gz0000644000176200001440000047446713271553720015566 0ustar liggesuserst˒Hέ wa ›蜝nfzЃKE:%Rtw3R ea߷uƵt:ϵڵ\w{;:|\R{ _~{7߹-{\{jוo~=?ysyîpu+oqµrmuWj?o Wݮ?e;q?3uy=ϵ\+ukt|o~c+?oog{Z5__ ܸ<\\{\[ʕwmJMY+_ WmoRl\;Y;Wa?3ewWì0e>;#d PvƵ3kRî9 |}f:\ +?o\w~YZl, /\<Pkj[++߸gƸGrjHPEj繝ys;gm~ X{ ua\ں0޺^HjE>+Yϊ|V"qv{Exw>U|E*rVwƋUMPU3Wg___؁a!g ++/\g+v^^]y+iܧq+rx^Wg\u׹߰u׹_g|-o=s~ [߅]x߅-o~+[|o~+[Vwpq~w~b?<՟kul9wܧpa;~_ߟiqΕV\{\<23*ϨG\Fig4Fߘos5)seV;c3:#ܯ3-o~ [0-ge\+Y}Vc//yH*moc6s=71l|;xv3U+ع`9Xqafym\ye^s}!m>]V=}nou~?dv|r!տ_hٿвeBEZ/_=W7/_hٿвe+׹_~ [p-o~/ -Q_hٿвw5.r}6qqmƸ6kq۹xvƳ3!o~;yp`\9a)̵p\ׅW~yjS )ܧp}RV~} )ܧ}jg_^{oA~Kk\Mœ@R Ẑd$,daX VʛXef*3RKp7Z*;۹~^+?V~ϸɵ{Tvܧ3vp A9ׅ߯\7~a?Wiшz_;+߫| q_XY+o18:wӹO>,gaƳ0|y^0.|Xa+[|c6786g;{{<=vs0/9x~9_gvuw4ݿht|,~gSOaoA~K~o,hр 94G-zϕC\O>A~ [_+Yx ea< t^ (8_H#Wr?p+\6Ƈ<z D/@+wD ]d;!6DHv9`dHvAtZum q:IhfƂr?$d'ŕe:| 6ƃcND 2_ RDW>D_ N,|Ihh`Ci:g>HpE+\ Hpx4rE+n\7q?4rEn+r[wkEN+Xƍ k_C!g Mڐ|5,CZSCЀ ذ אkKr7!7 ݐ44`Cna!? 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It is a \code{data.frame} of 4 columns #' containing the histogram of the distribution of \code{tic_e} for each bin delimited by \code{BinStart} #' and \code{BinEnd}, the count for each bin \code{NullCount} and the cumulative distribution #' of the right tail area \code{NullCumSum}} #' \item{obstic}{\code{data.frame} with the observed \code{tic_e} values, the indexes of the variables between the tic is computed. #' If the input matrix has column names then the names are reported in the dataframe, otherwise "Var" is added for each variable.} #' \item{obsdists}{\code{data.frame} similar to \code{nulldist} but with observed values of \code{tic_e}} #' \item{pval}{data.frame with the pvalue computed for each comparison. The adjusted pvalue is also reported based #' on the method chosen with the parameter \code{p.adjust.method}} #' } #' @seealso \code{\link[stats]{p.adjust}}, \code{\link[graphics]{hist}}, \code{\link[minerva]{mine}} #' @references #' D. Albanese, S. Riccadonna, C. Donati, P. Franceschi (2018) #' _A practical tool for Maximal Information Coefficient Analysis_ #' GigaScience, 7, 4, \url{https://doi.org/10.1093/gigascience/giy032} #' @examples #' data(Spellman) #' Spellman <- as.matrix(Spellman) #' spellress <- mictools(Spellman[, 10:20], nperm=1000) #' #' ## Use a different pvalue correction method #' spellressb <- mictools(Spellman[,10:20], nperm=1000, seed=1234, p.adjust.method="bonferroni") #' #' ## Distribution of tic_e null #' hist(spellress$tic, breaks=100, main="Tic_e null distribution") #' barplot(spellress$nulldist$NullCount) #' #' ## Distribution of the observed tic #' hist(spellress$obstic$TIC) #' barplot(spellress$obsdist$Count) #' #' ## Distribution of empirical pvalues #' hist(spellress$pval$pval, breaks=50) #' #' @export mictools <- function(x, alpha=9, C=5, seed=0, nperm=200000, p.adjust.method="BH"){ ## Setup bins <- seq(0,1, length.out = 10000 + 1) varnames <- colnames(x) if (is.null(varnames)) varnames <- paste("Var", 1:ncol(x)) ## Compute null distribution for TIC ticnull <- mictools_null(x, alpha=alpha, C=C, nperm=nperm, seed=seed) ## Compute histogram of the null distribution and right tailed area histtic <- graphics::hist(ticnull, breaks = bins, plot = FALSE, right=FALSE) histcumsum <- rev(cumsum(rev(histtic$counts))) ## Compute observed values ## ticmicmat <- mictools_pstats(x, alpha=alpha, C=C, est="mic_e") ticmicmat <- pstats(x, alpha=alpha, C=C, est="mic_e") # ix <- t(utils::combn(1:ncol(x),2)) ticmicmat <- data.frame(TIC=ticmicmat[, 4], I1=ticmicmat[, 1], I2=ticmicmat[,2], VarNames1=varnames[ticmicmat[, 1]], VarNames2=varnames[ticmicmat[, 2]], stringsAsFactors = FALSE) nulldist <- data.frame(BinStart=bins[1:(length(bins)-1)], BinEnd=bins[2:(length(bins))], NullCount=histtic$counts, NullCumSum=histcumsum, stringsAsFactors = FALSE) ## Histogram of observed values histtictrue <- graphics::hist(ticmicmat[,1], breaks = bins, plot=FALSE, right=FALSE) obscumtic <- rev(cumsum(rev(histtictrue$counts))) obsdist <- data.frame(BinStart=bins[1:(length(bins)-1)], BinEnd=bins[2:(length(bins))], Count=histtictrue$counts, CountCum=obscumtic, stringsAsFactors = FALSE) ## One-dimensional linear interpolation pval <- stats::approx(bins[1:(length(bins)-1)], y = histcumsum, xout=ticmicmat[,1], method="linear", rule=2)$y / (histcumsum[1] + 1) pval.df <- data.frame(pval=pval, I1=ticmicmat[,2], I2=ticmicmat[,3], Var1=varnames[ticmicmat[,2]], Var2=varnames[ticmicmat[,3]], stringsAsFactors = FALSE) pval.df$adj.P.Val <- stats::p.adjust(pval.df$pval, method=p.adjust.method) ## Return values as list retlist <- list(tic=ticnull, nulldist=nulldist, obstic=ticmicmat, obsdist=obsdist, pval=pval.df) return(retlist) } #' Compute the association strengh #' #' This function uses the null distribution of the \code{tic_e} computed with the function \code{\link[minerva]{mictools}}. #' Based on the available pvalue and the permutation null distribution it identifies reliable association between variables. #' #' #' @param pval a data.frame with pvalues for each pair of association of the \code{x} input matrix. It should contain two colums with #' the indices of the computed association according to the x input matrix #' @param pthr threshold on pvalue for measure to consider for computing mic_e #' @param pval.col an integer or character or vector relative to the columns of \code{pval} dataframe respectively for \code{pvalue}, #' association between variable 1, variable 2 in the \code{x} input matrix. See Details for further information. #' @inheritParams mictools #' @details The method implemented here is a wrapper for the original method published by Albaese et al. (2018). The python version #' is available at \url{https://github.com/minepy/mictools}. #' #' This function should be called after the estimation of the null distribution of \code{tic_e} scores based on permutations of the input data. #' #' The \code{mic} association is computed only for the variables for which the pvalue in the \code{pval} \code{data.frame} is less then #' the threshold set with the \code{pthr} input parameter. #' We assume the first column of the \code{pval} \code{data.frame} contains the pvalue, this value can be changed using #' the \code{pval.col}[1] parameter. #' #' The \code{pval.col} parameter, by default takes the first three columns in the \code{pval} \code{data.frame}, in particular the first column containing the \code{pvalues} #' of the association between variable in column \code{pval.col[2]} and \code{pval.col[3]}. #' If a character vector is provided names in \code{pval.col} are matched with the names in \code{pval} \code{data.frame}. #' If \code{NULL} is passed it is assumed the first column contains pvalue, while the 2 and 3 the index or name of the variable in \code{x}. #' If one value is passed it refers to the \code{pvalue} column and the consecutive two columns are assume to contain variable indexes. #' #' @return A dataframe with the \code{tic_e} Pvalue, the \code{mic} value and the column identifier regarding the input matrix #' \code{x} of the variables of which the association is computed. #' @seealso \code{\link[minerva]{mine}}, \code{\link[minerva]{mictools}}, \code{\link[stats]{p.adjust}} #' @examples #' data(Spellman) #' mydata <- as.matrix(Spellman[, 10:20]) #' ticenull <- mictools(mydata, nperm=1000) #' #' ## Use the nominal pvalue: #' ms <- mic_strength(mydata, pval=ticenull$pval, alpha=NULL, pval.col = c(6, 4,5)) #' #' ## Use the adjusted pvalue: #' ms <- mic_strength(mydata, pval=ticenull$pval, alpha=NULL, pval.col = c(6, 4,5)) #' #' ms #' #' \dontrun{ #' ## Use qvalue #' require(qvalue) #' qobj <- qvalue(ticenull$pval$pval) #' ticenull$pval$qvalue <- qobj$qvalue #' ms <- mic_strength(mydata, pval=ticenull$pval, alpha=NULL, pval.col = c("qvalue", "Var1", "Var2")) #' #' ## Get the data from mictools repository #' #' lnf <- "https://raw.githubusercontent.com/minepy/mictools/master/examples/datasaurus.txt" #' datasaurus <- read.table(lnf, header=TRUE, row.names = 1, stringsAsFactors = FALSE) #' datasaurus <- t(datasaurus) #' ticenull <- mictools(datasaurus, nperm=200000) #' micres <- mic_strength(mydata, ticenull$pval, pval.col=c(6, 4, 5)) #' #' ## Plot distribution of pvalues #' hist(ticenull$pval, breaks=50, freq=FALSE) #' #' ## Plot distribution of tic_e values #' hist(ticenull$tic) #' #' ## Correct pvalues using qvalue package #' require(qvalue) #' require(ggplot2) #' qobj <- qvalue(ticenull$pval$pval) #' ticenull$pval$qvalue <- qobj$qvalue #' micres <- mic_strength(datasaurus, ticenull$pval, pval.col=c("qvalue", "Var1", "Var2")) #' #' hist(qobj$qvalue) #' #' df <- data.frame(pi0.labmda=qobj$pi0.lambda, lambda=qobj$lambda, pi0.smooth=qobj$pi0.smooth) #' gp0 <- ggplot(df, aes(lambda, pi0.labmda)) + geom_point() #' gp0 <- gp0 + geom_line(aes(lambda, pi0.smooth)) #' gp0 <- gp0 + geom_hline(yintercept = qobj$pi0, linetype="dashed", col="red") #' } #' @export mic_strength <- function(x, pval, alpha=NULL, C=5, pthr=0.05, pval.col=NULL) { nbins <- c(1, 25, 50, 250, 500, 1000, 2500, 5000, 10000, 40000) alphas <- c(0.85, 0.80, 0.75, 0.70, 0.65, 0.6, 0.55, 0.5, 0.45, 0.4) if (is.null(alpha)) alpha <- alphas[.bincode(nrow(x), nbins, include.lowest=TRUE)] ## Check parameters..... pval.col <- check_pvalcol(pval, pval.col) ## Select association by pvalue pix <- pval[, pval.col[1]] < 0.05 ## Compute mic in res[,2] res <- sapply(which(pix), function(y, x.sub, vartouse, alpha, C){ xtmp <- x.sub[,vartouse[y, 1]] ytmp <- x.sub[,vartouse[y, 2]] mine_stat(xtmp, ytmp, alpha=alpha, C=C, est="mic_e", measure = "mic") }, x.sub=x, alpha=alpha, C=C, vartouse=pval[, pval.col[2:3]]) ## Add variable index of the MIC computed res.df <- data.frame(TicePval=pval[pix, pval.col[1]], MIC=res, I1=pval[pix, pval.col[2]], I2=pval[pix, pval.col[3]], stringsAsFactors = FALSE) return(res.df) } ## Helper function to check for correct pval.col parameter ## Regarding pval dataframe check_pvalcol <- function(pval, pval.col) { if (length(pval.col) > 3) pval.col <- pval.col[1:3] if (is.character(pval.col)){ mx <- match(pval.col, names(pval)) pval.col <- mx[!is.na(mx)] } if ((length(pval.col) == 0) || (is.null(pval.col))) pval.col <- 1:3 if (length(pval.col) == 1) pval.col <- seq(pval.col, pval.col+2) if (length(pval.col)==2) pval.col <- c(pval.col, pval.col[length(pval.col)] + 1) if (max(pval.col) > ncol(pval)) stop(paste0("Not available column "), max(pval.col), call. = FALSE) return(pval.col) }minerva/R/RcppExports.R0000644000176200001440000001260513472710525014542 0ustar liggesusers# Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 #' This is an helper function to compute one \code{mine} statistic. #' It take two vectors of the same dimension as an input. #' #' @param x Numeric Vector of size \code{n} #' @param y Numeric Vector of size \code{n} #' @param alpha numeric value representing parameter for the mine statistic see \code{\link[minerva]{mine}} #' @param C c parameter for the mine statistic see \code{\link[minerva]{mine}} #' @param est character estimation parameter for the mine statistic. #' Possible values are \code{"mic_approx"} or \code{"mic_e"} #' @param measure integer indicating which measure to return #' available measures are: \code{mic, mas, mev, mcn, tic, gmic}. The string could be also uppercase. #' For measure \code{mic-r2} see details. #' @param eps eps value for MCN statistic should be in (0,1). If NA (default) is passed then the normal MCN statistic is returned. #' @param p probability for the generalized mic #' @param norm boolean if require normalization between 0 and 1 for the \code{tic} statistic #' @details This is a wrapper function to compute the mine statistic between two variables. #' for more details on the available measure and the meaning of the other parameters see also the #' documentation for the \code{\link[minerva]{mine}} function. #' #' For measure \code{mic-r2} use the Pearson R coefficient score \code{\link[stats]{cor}} and the measure \code{mic}. #' See the example below. #' @seealso \code{\link[minerva]{mine}} #' @examples #' x <- runif(10); y <- 3*x+2; #' mine_stat(x,y, measure="mic") #' #' ## Measure mic-r2 #' x <- matrix(rnorm(20), ncol=2, nrow=10) #' mmic <- mine_stat(x[,1], x[,2], measure="mic") #' r2 <- cor(x[,1], x[,2]) #' #' mmic - r2**2 #' #' @export mine_stat <- function(x, y, alpha = 0.6, C = 15, est = "mic_approx", measure = "mic", eps = NA_real_, p = -1, norm = FALSE) { .Call('_minerva_mine_stat', PACKAGE = 'minerva', x, y, alpha, C, est, measure, eps, p, norm) } #' Compute pairwise statistics (MIC and normalized TIC) between variables #' (convenience function). #' #' For each statistic, the upper triangle of the matrix is stored by row #' (condensed matrix). If m is the number of variables, then for i < j < m, the #' statistic between (col) i and j is stored in k = m*i - i*(i+1)/2 - i - 1 + j. #' The length of the vectors is n = m*(m-1)/2. #' @inheritParams cstats #' @return #' A matrix of (n x (n-1)/2) rows and 4 columns. The first and second column are #' the indexes relative to the columns in the input matrix \code{x} for which the statistic is computed for. #' Column 3 contains the MIC statistic, while column 4 contains the normalized TIC statistic. #' @examples #' ## Create a matrix of random numbers #' ## 10 variables x 100 samples #' x <- matrix(rnorm(1000), ncol=10) #' res <- pstats(x) #' #' head(res) #' #' @export pstats <- function(x, alpha = 0.6, C = 15, est = "mic_approx") { .Call('_minerva_pstats', PACKAGE = 'minerva', x, alpha, C, est) } #' Compute statistics (MIC and normalized TIC) between each pair of the two #' collections of variables (convenience function). #' If n and m are the number of variables in X and Y respectively, then the #' statistic between the (row) i (for X) and j (for Y) is stored in \code{mic[i, j]} #' and \code{tic[i, j]}. #' @param x Numeric Matrix of m-by-n with n variables and m samples. #' @param y Numeric Matrix of m-by-p with p variables and m samples. #' @param alpha number (0, 1.0] or >=4 if alpha is in (0,1] then B will be max(n^alpha, 4) where n is the #' number of samples. If alpha is >=4 then alpha defines directly the B #' parameter. If alpha is higher than the number of samples (n) it will be #' limited to be n, so B = min(alpha, n). #' @param C number (> 0) determines how many more clumps there will be than columns in #' every partition. Default value is 15, meaning that when trying to #' draw x grid lines on the x-axis, the algorithm will start with at #' most 15*x clumps. #' @param est string ("mic_approx", "mic_e") estimator. #' With est="mic_approx" the original MINE statistics will #' be computed, with est="mic_e" the equicharacteristic matrix is #' is evaluated and MIC_e and TIC_e are returned. #' #' @return list of two elements: #' MIC: the MIC statistic matrix (n x p). #' TIC: the normalized TIC statistic matrix (n x p). #' @examples #' x <- matrix(rnorm(2560), ncol=8, nrow=320) #' y <- matrix(rnorm(1280), ncol=4, nrow=320) #' #' mictic <- cstats(x, y, alpha=9, C=5, est="mic_e") #' head(mictic) #' #' @export cstats <- function(x, y, alpha = 0.6, C = 15, est = "mic_approx") { .Call('_minerva_cstats', PACKAGE = 'minerva', x, y, alpha, C, est) } #' This set of functions are helper function to compute null distribution of the \code{tic_e} and #' \code{tic_e} observed distribution from a matrix #' #' @inheritParams mine_stat #' @param x matrix N x M with M variables and N samples #' @param nperm numper of permutation #' @param seed integer to set the starting seed for random number generation (for reproducibility). #' @describeIn mictools_null compute the \code{tic_e} null distribution #' @return It returns a vector of \code{nperm} \code{tic_e} values. #' @seealso \code{\link[minerva]{mictools}} #' @export mictools_null <- function(x, alpha = 9, C = 5, nperm = 200000L, seed = 0L) { .Call('_minerva_mictools_null', PACKAGE = 'minerva', x, alpha, C, nperm, seed) } minerva/R/mine.R0000644000176200001440000006211413470715275013206 0ustar liggesusers# This code is written by Michele Filosi ## Roberto Visintainer . ## 2012 ## This program is free software: you can redistribute it and/or modify ## it under the terms of the GNU General Public License as published by ## the Free Software Foundation, either version 3 of the License, or ## (at your option) any later version. ## This program is distributed in the hope that it will be useful, ## but WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ## GNU General Public License for more details. ## You should have received a copy of the GNU General Public License ## along with this program. If not, see . #' MINE family statistics #' Maximal Information-Based Nonparametric Exploration (MINE) #' statistics. \code{mine} computes the MINE family measures between two variables. #' #' @aliases mine MINE MIC-R2 mic-r2 #' #' @param x a numeric vector (of size \emph{n}), matrix or data frame (which is coerced to matrix). #' @param y NULL (default) or a numeric vector of size \emph{n} (\emph{i.e.}, with compatible dimensions to x). #' @param master an optional vector of indices (numeric or character) to #' be given when \code{y} is not set, otherwise master is ignored. It can be #' either one column index to be used as reference for the comparison #' (versus all other columns) or a vector of column indices to be used for #' computing all mutual statistics. # If not specified it is set to \code{1:ncol(x)}. #' @param alpha float (0, 1.0] or >=4 if alpha is in (0,1] then B will be max(n^alpha, 4) where n is the #' number of samples. If alpha is >=4 then alpha defines directly the B #' parameter. If alpha is higher than the number of samples (n) it will be #' limited to be n, so B = min(alpha, n) Default value is 0.6 (see Details). #' @param C an optional number determining the starting point of the #' \emph{X-by-Y} search-grid. When trying to partition the \emph{x}-axis into #' \emph{X} columns, the algorithm will start with at most \code{C}\emph{X} #' \emph{clumps}. Default value is 15 (see Details). #' @param n.cores ooptional number of cores to be used in the #' computations, when master is specified. It requires the #' \pkg{parallel} package, which provides support for parallel #' computing, released with \R >= 2.14.0. Defaults is 1 (\emph{i.e.}, not performing parallel computing). #' @param var.thr minimum value allowed for the variance of the input #' variables, since \code{mine} can not be computed in case of variance #' close to 0. Default value is 1e-5. Information about failed check #' are reported in \emph{var_thr.log} file. #' @param eps integer in [0,1]. If 'NULL' (default) it is set to #' 1-MIC. It can be set to zero for noiseless functions, #' but the default choice is the most appropriate parametrization #' for general cases (as stated in Reshef et al. SOM). #' It provides robustness. #' @param est Default value is "mic_approx". With est="mic_approx" the original MINE statistics will #' be computed, with est="mic_e" the equicharacteristic matrix is #' is evaluated and the mic() and tic() methods will return MIC_e and #' TIC_e values respectively. #' @param na.rm boolean. This variable is passed directly to the #' \code{cor}-based functions. See \code{cor} for further details. #' @param use Default value is "all.obs". This variable is passed directly to the #' \code{cor}-based functions. See \code{cor} for further details. #' @param normalization logical whether to use normalization when computing \code{tic} measure. Ignored for other measures. #' Default to FALSE. #' @param \dots currently ignored #' @details \code{mine} is an R wrapper for the C engine \emph{cmine} #' (\url{http://minepy.readthedocs.io/en/latest/}), #' an implementation of Maximal Information-Based Nonparametric Exploration (MINE) #' statistics. The MINE statistics were firstly detailed in #' D. Reshef et al. (2011) \emph{Detecting novel associations in large datasets}. #' Science 334, 6062 (\url{http://www.exploredata.net}). #' #' Here we recall the main concepts of the MINE family statistics. #' Let \eqn{D={(x,y)}} be the set of \emph{n} ordered pairs of elements of \code{x} #' and \code{y}. The data space is partitioned in #' an \emph{X-by-Y} grid, grouping the \emph{x} and \emph{y} values #' in \emph{X} and \emph{Y} bins respectively.\cr #' #' The \strong{Maximal Information Coefficient (MIC)} is defined as #' \deqn{\textrm{MIC}(D)=\max_{XY= (1-\epsilon)MIC(D)}.} #' More details are provided in the supplementary material (SOM) of the original paper. #' #' The MINE statistics can be computed for two numeric vectors \code{x} and \code{y}. #' Otherwise a matrix (or data frame) can be provided and two options are available #' according to the value of \code{master}. If \code{master} is a column identifier, #' then the MINE statistics are computed for the \emph{master} variable versus the #' other matrix columns. If \code{master} is a set of column identifiers, then all #' mutual MINE statistics are computed among the column subset. #' \code{master}, \code{alpha}, and \code{C} refers respectively to the \emph{style}, #' \emph{exp}, and \emph{c} parameters of the original \emph{java} code. #' In the original article, the authors state that the default value \eqn{\alpha=0.6} #' (which is the exponent of the search-grid size \eqn{B(n)=n^{\alpha}}) has been #' empirically chosen. It is worthwhile noting that \code{alpha} and \code{C} are #' defined to obtain an heuristic approximation in a reasonable amount of time. In case #' of small sample size (\emph{n}) it is preferable to increase \code{alpha} to 1 to #' obtain a solution closer to the theoretical one. #' @return The Maximal Information-Based Nonparametric Exploration (MINE) statistics #' provide quantitative evaluations of different aspects of the relationship #' between two variables. #' In particular \code{mine} returns a list of 5 statistics: #' \item{MIC}{ \strong{Maximal Information Coefficient.} \cr #' It is related to the relationship strenght and it can be interpreted as a #' correlation measure. It is symmetric and it ranges in [0,1], where it #' tends to 0 for statistically independent data and it approaches 1 in #' probability for noiseless functional relationships (more details can #' ben found in the original paper). } #' \item{MAS}{ \strong{Maximum Asymmetry Score.} \cr #' It captures the deviation from monotonicity. Note that #' \eqn{\textrm{MAS} < \textrm{MIC}}{MAS < MIC}. \cr #' \emph{Note:} it can be useful for detecting periodic relationships #' (unknown frequencies). } #' \item{MEV}{ \strong{Maximum Edge Value.} \cr #' It measures the closeness to being a function. Note that #' \eqn{\textrm{MEV} \leq \textrm{MIC}}{MEV <= MIC}. } #' \item{MCN}{ \strong{Minimum Cell Number.} \cr #' It is a complexity measure. } #' \item{MIC-R2}{It is the difference between the MIC value and the Pearson #' correlation coefficient. } \cr #' #' When computing \code{mine} between two numeric vectors \code{x} and \code{y}, #' the output is a list of 5 numeric values. When \code{master} is provided, #' \code{mine} returns a list of 5 matrices having \code{ncol} equal to #' \emph{m}. In particular, if \code{master} is a single value, #' then \code{mine} returns a list of 5 matrices having 1 column, #' whose rows correspond to the MINE measures between the \emph{master} #' column versus all. Instead if \code{master} is a vector of \emph{m} indices, #' then \code{mine} output is a list of 5 \emph{m-by-m} matrices, whose element #' \emph{i,j} corresponds to the MINE statistics computed between the \emph{i} #' and \emph{j} columns of \code{x}. #' @references #' D. Reshef, Y. Reshef, H. Finucane, S. Grossman, G. McVean, P. Turnbaugh, #' E. Lander, M. Mitzenmacher, P. Sabeti. (2011) #' \emph{Detecting novel associations in large datasets}. #' Science 334, 6062\cr #' \url{http://www.exploredata.net}\cr #' (SOM: Supplementary Online Material at #' \url{http://www.sciencemag.org/content/suppl/2011/12/14/334.6062.1518.DC1}) #' #' D. Albanese, M. Filosi, R. Visintainer, S. Riccadonna, G. Jurman, #' C. Furlanello. #' \emph{minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers}. #' Bioinformatics (2013) 29(3): 407-408, \doi{doi:10.1093/bioinformatics/bts707}.\cr #' #' \emph{minepy. Maximal Information-based Nonparametric Exploration in C and Python.}\cr #' \url{http://minepy.sourceforge.net} #' #' @author #' Michele Filosi and Roberto Visintainer #' @examples #' A <- matrix(runif(50),nrow=5) #' mine(x=A, master=1) #' mine(x=A, master=c(1,3,5,7,8:10)) #' #' x <- runif(10); y <- 3*x+2; plot(x,y,type="l") #' mine(x,y) #' # MIC = 1 #' # MAS = 0 #' # MEV = 1 #' # MCN = 2 #' # MIC-R2 = 0 #' #' set.seed(100); x <- runif(10); y <- 3*x+2+rnorm(10,mean=2,sd=5); plot(x,y) #' mine(x,y) #' # rounded values of MINE statistics #' # MIC = 0.61 #' # MAS = 0 #' # MEV = 0.61 #' # MCN = 2 #' # MIC-R2 = 0.13 #' #' t <-seq(-2*pi,2*pi,0.2); y1 <- sin(2*t); plot(t,y1,type="l") #' mine(t,y1) #' # rounded values of MINE statistics #' # MIC = 0.66 #' # MAS = 0.37 #' # MEV = 0.66 #' # MCN = 3.58 #' # MIC-R2 = 0.62 #' #' y2 <- sin(4*t); plot(t,y2,type="l") #' mine(t,y2) #' # rounded values of MINE statistics #' # MIC = 0.32 #' # MAS = 0.18 #' # MEV = 0.32 #' # MCN = 3.58 #' # MIC-R2 = 0.31 #' #' # Note that for small n it is better to increase alpha #' mine(t,y1,alpha=1) #' # rounded values of MINE statistics #' # MIC = 1 #' # MAS = 0.59 #' # MEV = 1 #' # MCN = 5.67 #' # MIC-R2 = 0.96 #' #' mine(t,y2,alpha=1) #' # rounded values of MINE statistics #' # MIC = 1 #' # MAS = 0.59 #' # MEV = 1 #' # MCN = 5 #' # MIC-R2 = 0.99 #' #' # Some examples from SOM #' x <- runif(n=1000, min=0, max=1) #' #' # Linear relationship #' y1 <- x; plot(x,y1,type="l"); mine(x,y1) #' # MIC = 1 #' # MAS = 0 #' # MEV = 1 #' # MCN = 4 #' # MIC-R2 = 0 #' #' # Parabolic relationship #' y2 <- 4*(x-0.5)^2; plot(sort(x),y2[order(x)],type="l"); mine(x,y2) #' # rounded values of MINE statistics #' # MIC = 1 #' # MAS = 0.68 #' # MEV = 1 #' # MCN = 5.5 #' # MIC-R2 = 1 #' #' # Sinusoidal relationship (varying frequency) #' y3 <- sin(6*pi*x*(1+x)); plot(sort(x),y3[order(x)],type="l"); mine(x,y3) #' # rounded values of MINE statistics #' # MIC = 1 #' # MAS = 0.85 #' # MEV = 1 #' # MCN = 4.6 #' # MIC-R2 = 0.96 #' #' # Circle relationship #' t <- seq(from=0,to=2*pi,length.out=1000) #' x4 <- cos(t); y4 <- sin(t); plot(x4, y4, type="l",asp=1) #' mine(x4,y4) #' # rounded values of MINE statistics #' # MIC = 0.68 #' # MAS = 0.01 #' # MEV = 0.32 #' # MCN = 5.98 #' # MIC-R2 = 0.68 #' #' data(Spellman) #' res <- mine(Spellman,master=1,n.cores=1) #' #' \dontrun{## example of multicore computation #' res <- mine(Spellman,master=1,n.cores=parallel::detectCores()-1)} #' @export mine <- function(x, y=NULL, master=NULL, alpha=0.6, C=15, n.cores=1, var.thr=1e-5, eps=NULL, est="mic_approx", na.rm=FALSE, use="all.obs", normalization=FALSE, ...){ ## Control on input arguments checked <- check.inputs(x,y,alpha,C,n.cores,var.thr,eps, est, na.rm, use, normalization) x <- checked[[1]] y <- checked[[2]] alpha <- checked[[3]] C <- checked[[4]] n.cores <- checked[[5]] eps <- checked[[6]] est <- checked[[7]] var.idx <- checked[[8]] na.rm <- checked[[9]] use <- checked[[10]] normalization <- checked[[11]] ## only one matrix given if (is.null(y)){ s <- dim(x)[1] f <- dim(x)[2] ## Check for na and overwrite the input ## No need to store the input if (na.rm | use %in% c(2L, 3L)){ x <- na.omit(x) } ## If a master variable is passed check the type if (!is.null(master)){ if (!is.numeric(master)) stop("'master' must be numeric") if (max(master)>f) stop("Subscript out of bound!\nMaximum value allowed: ",f) if (min(master)<1) stop("Subscript out of bound!\nMinimum value allowed: ",1) } if (is.null(master)){ if (n.cores>1){ ## Launch parallel res <- .allvsallparall(x,alpha,C,n.cores,eps,est, normalization) ## return(.allvsallparall(x,alpha,C,n.cores,eps)) } else{ res <- .allvsall(x,alpha,C,eps,est, normalization) ## return(.allvsall(x,alpha,C,eps)) } } else { if (length(master)==1){ if (n.cores>1){ res <- .onevsallparall(x,master,alpha,C,n.cores,eps,est, normalization) ## return(.onevsallparall(x,master,alpha,C,n.cores,eps)) } else{ res <- .onevsall(x,master,alpha,C,exclude=FALSE,eps=eps, est=est, normalization=normalization) ## return(.onevsall(x,master,alpha,C,exclude=FALSE,eps)) } } if (length(master)>1){ newdata <- x[,master] if (n.cores>1){ ## Launch parallel res <- .allvsallparall(newdata,alpha,C,n.cores,eps,est, normalization) ## return(.allvsallparall(newdata,alpha,C,n.cores,eps)) } else { res <- .allvsall(newdata,alpha,C,eps,est, normalization) ## return(.allvsall(newdata,alpha,C,eps)) } } } } else { ## y is given ## pairwise.complete.obs // complete.obs if (na.rm | use%in% c(2L,3L)){ xidx <- (apply(!is.na(x), 1, sum)) yidx <- !is.na(y) idx <- xidx & yidx x <- as.matrix(x[idx,]) y <- as.matrix(y[idx,]) } ## two variables given if (ncol(x) == 1 & ncol(y) == 1){ res <- .Call("mineRonevar",as.double(x),as.double(y),alpha=alpha,C=C,eps=eps, est=est, normalization=normalization) names(res) <- c("MIC","MAS","MEV","MCN","MIC-R2", "GMIC", "TIC") res <- as.list(res) } else { newdata <- cbind(x,y) colnames(newdata)[ncol(newdata)] <- "Y" res <- .onevsall(newdata,ncol(newdata),alpha,C,exclude=TRUE,eps=eps,est=est, normalization=normalization) res <- lapply(res, function(x){dimnames(x) <- list(NULL, colnames(x)); return(x)}) } } ## Set NA variables with nearly 0 variance ## TODO: set NA with nearly 0 variance when y is passed as arguments if (!is.null(var.idx[["x"]])) res <- lapply(res, function(x,var.idx){ if(is.null(dim(x))){ x <- 0.0 } else { if (length(master)==1) { if (any(var.idx==master)){ x[,1] <- 0.0 } else { x[var.idx,] <- 0.0 } } else { x[var.idx,] <- 0.0 } } return(x)}, var.idx=var.idx[["x"]]) ## Return results return(res) } ##-------------------------------------------------- ## Functions for internal use! ##-------------------------------------------------- ## Checking input integrity ## x should be a matrix or a vector ## if x is a vector y should be given ## y should be a one dimensional vector check.inputs <- function(x,y,alpha,C,n.cores,var.thr,eps,est,na.rm,use, normalization) { ## MINE parameters check! if (!is.numeric(alpha)){ stop("'alpha should be numeric", call.=FALSE) } else { if ((alpha<=0.0 || alpha>1.0) && alpha <=4.0) stop("'alpha' must be in (0.0, 1.0] or >= 4.0",call.=FALSE) } if(C<=0.0 || !is.numeric(C)) stop("'C' must be > 0.0",call.=FALSE) ## use argument na.method <- pmatch(use, c("all.obs", "complete.obs", "pairwise.complete.obs", "everything")) if (is.na(na.method)) stop("invalid 'use' argument") ## Data check! if (is.data.frame(x)) x <- as.matrix(x) ## Check for NA/missing values if (any(is.na(x)) & !(na.rm | na.method %in% c(2L,3L))){ stop("Missing values present in input variable 'x'. Consider using use = 'pairwise.complete.obs'.", call.=FALSE) } if (!is.matrix(x) && is.null(y)) stop("supply both 'x' and 'y' or a matrix-like 'x'", call.=FALSE) if (!is.numeric(x)) stop("'x' must be numeric", call.=FALSE) stopifnot(is.atomic(x)) x <- as.matrix(x) ## Check variance var.idx <- list() myvar <- apply(x,2,var,na.rm=TRUE) if (sum(myvar 1){ if (detectCores()==1){ warning("Only 1 core available on this machine:'n.cores' will be ignored.\nPlease consider using a cluster.", immediate.=TRUE) n.cores <- 1 } if (n.cores>detectCores()) stop("Query for more cores then the available ones.\n Max number of cores for this machine is: ",detectCores()) } if (n.cores <= 0){ stop("You are trying to compute mic using ",n.cores," cores.. are you sure?", call.=FALSE) } ## Check epsilon parameter for MINE if(!is.null(eps)){ if( eps<0.0 || eps>1 || !is.numeric(eps) ) stop("'eps' must be > 0.0 and < 1.0",call.=FALSE) } ## Check est parameter for MIC ## mic_approx -> 0 ## mic_e -> 1 EST <- 0 if (est == "mic_e"){ EST <- 1 } ## Send a warning if variable with 0 variance have been found if (!is.null(var.idx[["x"]]) || !is.null(var.idx[["y"]])){ warning("Found variables with nearly 0 variance") } ## Check if na.rm is a boolean or a integer if (length(na.rm)>1 || (!is.logical(na.rm) && !is.integer(na.rm))) na.rm <- FALSE ## Check normalization parameter for tic if ((length(normalization) > 1) || (!is.logical(normalization) && !is.integer(normalization)) ) normalization <- FALSE normalization <- as.integer(normalization) return(list(x=x, y=y, alpha=alpha, C=C, n.cores=n.cores, eps=eps, est=EST, var.idx=var.idx, na.rm=na.rm, use=na.method, normalization=normalization)) } ## Calling all features vs all features using C implementation ## For the source see src/mine_interface.c .allvsall <- function(x, alpha, C,eps, est, normalization){ tmp <- .Call("mineRall",x,nrow(x),ncol(x),alpha,C, eps, est, normalization) tmp <- lapply(tmp,function(y,n){colnames(y) <- rownames(y) <- n return(y)}, n <- colnames(x)) return(tmp) } ## Calling feature x[,idx] vs all other features ## Using C implementation feature vs feature ## For the source see src/mine_interface.c .onevsall <- function(x,idx,alpha,C,eps,exclude,est,normalization, diagonal=FALSE){ if (exclude) f <- dim(x)[2]-1 else f <- dim(x)[2] if (diagonal) start <- idx else start <- 1 Mat1 <- matrix(0,nrow=f,ncol=1,dimnames=list(colnames(x)[1:f],colnames(x)[idx])) Mat2 <- matrix(0,nrow=f,ncol=1,dimnames=list(colnames(x)[1:f],colnames(x)[idx])) Mat3 <- matrix(0,nrow=f,ncol=1,dimnames=list(colnames(x)[1:f],colnames(x)[idx])) Mat4 <- matrix(0,nrow=f,ncol=1,dimnames=list(colnames(x)[1:f],colnames(x)[idx])) Mat5 <- matrix(0,nrow=f,ncol=1,dimnames=list(colnames(x)[1:f],colnames(x)[idx])) Mat6 <- matrix(0,nrow=f,ncol=1,dimnames=list(colnames(x)[1:f],colnames(x)[idx])) Mat7 <- matrix(0,nrow=f,ncol=1,dimnames=list(colnames(x)[1:f],colnames(x)[idx])) for (i in start:f){ res <- .Call("mineRonevar",as.double(x[,idx]),as.double(x[,i]), alpha=alpha,C=C,eps=eps, est=est, normalization=normalization) names(res) <- c("MIC","MAS","MEV","MCN","MIC-R2", "GMIC", "TIC") Mat1[i,1] <- res["MIC"] Mat2[i,1] <- res["MAS"] Mat3[i,1] <- res["MEV"] Mat4[i,1] <- res["MCN"] Mat5[i,1] <- res["MIC-R2"] Mat6[i,1] <- res["GMIC"] Mat7[i,1] <- res["TIC"] } return(list(MIC=Mat1,MAS=Mat2,MEV=Mat3,MCN=Mat4,MICR2=Mat5,GMIC=Mat6, TIC=Mat7)) } ## Parallel implementation of one vs all function ## NB using 'parallel' package from CRAN for R >= 2.14 ## If older version of R install multicore package .onevsallparall <- function(x,master,alpha,C,n.cores,eps, est, normalization){ f <- dim(x)[2] cl <- makeCluster(n.cores) res <- parLapply(cl,1:f,function(i,master,alpha,C,data,eps,est, normalization){ return(.Call("mineRonevar",as.double(data[,master]), as.double(data[,i]),alpha=alpha,C=C,eps=eps,est=est, normalization))}, master=master,alpha=alpha,C=C,eps=eps,est=est,normalization=normalization, data=x) stopCluster(cl) Mat1 <- matrix(0,nrow=f,ncol=1,dimnames=list(colnames(x)[1:f],colnames(x)[master])) Mat2 <- matrix(0,nrow=f,ncol=1,dimnames=list(colnames(x)[1:f],colnames(x)[master])) Mat3 <- matrix(0,nrow=f,ncol=1,dimnames=list(colnames(x)[1:f],colnames(x)[master])) Mat4 <- matrix(0,nrow=f,ncol=1,dimnames=list(colnames(x)[1:f],colnames(x)[master])) Mat5 <- matrix(0,nrow=f,ncol=1,dimnames=list(colnames(x)[1:f],colnames(x)[master])) Mat6 <- matrix(0,nrow=f,ncol=1,dimnames=list(colnames(x)[1:f],colnames(x)[master])) Mat7 <- matrix(0,nrow=f,ncol=1,dimnames=list(colnames(x)[1:f],colnames(x)[master])) for (i in 1:f){ Mat1[i,1] <- res[[i]][1] Mat2[i,1] <- res[[i]][2] Mat3[i,1] <- res[[i]][3] Mat4[i,1] <- res[[i]][4] Mat5[i,1] <- res[[i]][5] Mat6[i,1] <- res[[i]][6] Mat7[i,1] <- res[[i]][7] } return(list(MIC=Mat1,MAS=Mat2,MEV=Mat3,MCN=Mat4,MICR2=Mat5,GMIC=Mat6,TIC=Mat7)) } ## Parallel implementation of all vs all function ## NB using 'parallel' package from CRAN for R >= 2.14 ## If older version of R install multicore package .allvsallparall <- function(x, alpha, C, n.cores,eps,est, normalization){ f <- dim(x)[2] cl <- makeCluster(n.cores) res <- parLapply(cl,1:f,function(y,data,alpha,C,eps,est, normalization){ return(.onevsall(x=data,idx=y,alpha=alpha,C=C,eps=eps,est=est,normalization=normalization, exclude=FALSE,diagonal=TRUE))}, data=x,alpha=alpha,C=C,eps=eps,est=est, normalization=normalization) stopCluster(cl) Mat1 <- matrix(0,ncol=f,nrow=f,dimnames=list(colnames(x),colnames(x))) Mat2 <- matrix(0,ncol=f,nrow=f,dimnames=list(colnames(x),colnames(x))) Mat3 <- matrix(0,ncol=f,nrow=f,dimnames=list(colnames(x),colnames(x))) Mat4 <- matrix(0,ncol=f,nrow=f,dimnames=list(colnames(x),colnames(x))) Mat5 <- matrix(0,ncol=f,nrow=f,dimnames=list(colnames(x),colnames(x))) Mat6 <- matrix(0,ncol=f,nrow=f,dimnames=list(colnames(x),colnames(x))) Mat7 <- matrix(0,ncol=f,nrow=f,dimnames=list(colnames(x),colnames(x))) for (i in seq(length(res))){ Mat1[i,i:f] <- res[[i]][[1]][i:f,] Mat1[i:f,i] <- res[[i]][[1]][i:f,] Mat2[i,i:f] <- res[[i]][[2]][i:f,] Mat2[i:f,i] <- res[[i]][[2]][i:f,] Mat3[i,i:f] <- res[[i]][[3]][i:f,] Mat3[i:f,i] <- res[[i]][[3]][i:f,] Mat4[i,i:f] <- res[[i]][[4]][i:f,] Mat4[i:f,i] <- res[[i]][[4]][i:f,] Mat5[i,i:f] <- res[[i]][[5]][i:f,] Mat5[i:f,i] <- res[[i]][[5]][i:f,] Mat6[i,i:f] <- res[[i]][[6]][i:f,] Mat6[i:f,i] <- res[[i]][[6]][i:f,] Mat7[i,i:f] <- res[[i]][[7]][i:f,] Mat7[i:f,i] <- res[[i]][[7]][i:f,] } return(list(MIC=Mat1,MAS=Mat2,MEV=Mat3,MCN=Mat4,MICR2=Mat5,GMIC=Mat6,TIC=Mat7)) } # .onUnload <- function (libpath) { # library.dynam.unload("minerva", libpath) # }minerva/README.md0000644000176200001440000001412313470733035013200 0ustar liggesusers# minerva R package for Maximal Information-Based Nonparametric Exploration computation * Minepy [Homepage](http://minepy.readthedocs.io/en/latest/) * Minepy [Github](https://github.com/minepy/minepy) ## Install * Latest cran release ```r install.packages("minerva") ``` * Development version ```r devtools::install_github('rsamantha/minerva') ``` ## Usage - Basic usage with helper function `mine`. ```r library(minerva) x <- 0:200 / 200 y <- sin(10 * pi * x) + x mine(x,y, n.cores=1) ``` - Compute a single measure from the MINE suite using `mine_stat`. + Available mesures are: *mic*, *mas*, *mev*, *mcn*, *tic*, *gmic* ```r x <- 0:200 / 200 y <- sin(10 * pi * x) + x mine_stat(x, y, measure="mic") ``` ### Compute statistic on matrices - All features in a single matrix (`mine_compute_pstat`). - All possible combination of features between two matrices (`mine_compute_cstat`). + When comparing two matrices the function check for euquality of number of rows of the two matrices. If the matrices have different number of rows then an error is thrown. ```r x <- matrix(rnorm(1000), ncol=10, nrow=10) y <- as.matrix(rnorm(1000), ncol=10, nrow=20) ## Compare feature of the same matrix pstats(x) ## Compare features of matrix x with feature in matrix y cstats(x, y) ``` ---- ## Mictools pipeline This is inspired to the original implementation by Albanese et al. available in python here: [https://github.com/minepy/mictools](https://github.com/minepy/mictools). #### Reading the data from mictool repository ```r datasaurus <- read.table("https://raw.githubusercontent.com/minepy/mictools/master/examples/datasaurus.txt", header=TRUE, row.names=1, as.is=TRUE, stringsAsFactors=FALSE) datasaurus.m <- t(datasaurus) ``` ### Compute null distribution for tic_e Automatically compute: - `tic_e` null distribution based on permutations. - histogram of the distribution with cumulative distribution. - Observed values of `tic_e` for each pair of variable in `datasaurus`. - Observed distribution of `tic_e`. - P-value for each variable pair association. ```r ticnull <- mictools(datasaurus.m, nperm=100000, seed=1234) ## Get the names of the named list names(ticnull) ##[1] "tic" "nulldist" "obstic" "obsdist" "pval" str(ticnull) ticnull$nulldist ticnull$obsdist ``` ##### Null Distribution | BinStart| BinEnd| NullCount| NullCumSum| |--------:|------:|---------:|----------:| | 0e+00| 0| 0| 1e+05| | 1e-04| 0| 0| 1e+05| | 2e-04| 0| 0| 1e+05| | 3e-04| 0| 0| 1e+05| | 4e-04| 0| 0| 1e+05| | 5e-04| 0| 0| 1e+05| | ... | ... | .... | .... | ##### Observed distribution | BinStart| BinEnd| Count| CountCum| |--------:|------:|-----:|--------:| | 0e+00| 0| 0| 325| | 1e-04| 0| 0| 325| | 2e-04| 0| 0| 325| | 3e-04| 0| 0| 325| | 4e-04| 0| 0| 325| | 5e-04| 0| 0| 325| | ... | ... | .... | .... | Plot `tic_e` and pvalue distribution. ```r hist(ticnull$tic) hist(ticenull$pval, breaks=50, freq=FALSE) ``` Use `p.adjust.method` to use a different pvalue correction method, or use the `qvalue` package to use Storey's qvalue. ```r ## Correct pvalues using qvalue qobj <- qvalue(ticnull$pval$pval) ## Add column in the pval data.frame ticnull$pval$qvalue <- qobj$qvalue ticnull$pval ``` Same table as above with the qvalue column added at the end. | pval| I1| I2|Var1 |Var2 | adj.P.Val| qvalue| |------:|--:|--:|:------|:------------|---------:|------:| | 0.5202| 1| 2|away_x |bullseye_x | 0.95| 1| | 0.9533| 1| 3|away_x |circle_x | 0.99| 1| | 0.0442| 1| 4|away_x |dino_x | 0.52| 0| | 0.6219| 1| 5|away_x |dots_x | 0.95| 1| | 0.8922| 1| 6|away_x |h_lines_x | 0.98| 1| | 0.3972| 1| 7|away_x |high_lines_x | 0.91| 1| | ... |...|...| ... | ... | ... | .... | ### Strenght of the association (MIC) ```r ## Use columns of indexes and FDR adjusted pvalue micres <- mic_strength(datasaurus.m, ticnull$pval, pval.col=c(6, 2, 3)) ``` | TicePval| MIC| I1| I2| |:--------|:----|:--|:--| | 0.0457| 0.42| 2| 15| | 0.0000| 0.63| 3| 16| | 0.0196| 0.50| 5| 18| | 0.0162| 0.36| 9| 22| | 0.0000| 0.63| 10| 23| | 0.0000| 0.57| 13| 26| | ... | ... | ...|...| Association strength computed based on the `qvalue` adjusted pvalue ```r ## Use qvalue adjusted pvalue micresq <- mic_strength(datasaurus.m, ticnull$pval, pval.col=c("qvalue", "Var1", "Var2")) ``` | TicePval| MIC|I1 |I2 | |:--------|:----|:----------|:----------| | 0.0401| 0.42|bullseye_x |bullseye_y | | 0.0000| 0.63|circle_x |circle_y | | 0.0172| 0.50|dots_x |dots_y | | 0.0143| 0.36|slant_up_x |slant_up_y | | 0.0000| 0.63|star_x |star_y | | 0.0000| 0.57|x_shape_x |x_shape_y | | ... | ... | ... |... | ---- ## Citing minepy/minerva and mictools ||| |:----------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | [minepy2013](https://doi.org/10.1093/bioinformatics/bts707) | Davide Albanese, Michele Filosi, Roberto Visintainer, Samantha Riccadonna, Giuseppe Jurman and Cesare Furlanello. *minerva and minepy:a C engine for the MINE suite and its R, Python and MATLAB wrappers*. Bioinformatics (2013) 29(3): 407-408 first published online December 14, 2012 | | [mictools2018](https://academic.oup.com/gigascience/article/7/4/giy032/4958979) | Davide Albanese, Samantha Riccadonna, Claudio Donati, Pietro Franceschi. *A practical tool for maximal information coefficient analysis*. 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*ESU:4T ROu[.F[z1wjMsWy+;syKm5/ul`n8o~}u`Pͽs1vPws b3c=_[¹ ;7bxW^p`oؓ+~޳/=6~j S{sey|Zڗ~D;sg#wv|4l߭~Cُ1=9qKW`cks̕[,r/{w 0Ԫpns`nfu9I/WmWNaUﭓSboSW'6nyuXk0R-/f6i\6>e߆Z4k Ұ-LsuU3sxTZݞϭzA5)~OU[C۟wKs028յt6|͆jڊ&熟v}Md6B0^/V`j褵xU(rpᑳܕTj`\^r07.bgIm{ٺmDߺi]MFݘy׮yUNWxamZZܘ'h(̼e4pR&;eo +;iW8'ƞ=[WrP{7TtZ7RMQ뛆7E#͕m`7oիܥlQvbzlvJezFO<?Ga ?~ u8|Bco:>{p;ItDMhH;5`dBqh\iaˣ:r$|+sߍ X`D5VoqrIxm}?{ڈ&p΋ KrNX\HSP:7OPr%^pJY!7J*7ɕJRminerva/DESCRIPTION0000644000176200001440000000306013472720023013420 0ustar liggesusersPackage: minerva Version: 1.5.8 Date: 2019-05-24 Title: Maximal Information-Based Nonparametric Exploration for Variable Analysis Authors@R: c(person("Michele", "Filosi", role = c("aut","cre" ), email = "michele.filosi@gmail.com"), person("Roberto", "Visintainer", role = "aut", email = "roberto.visintainer@unitn.eu"), person("Davide", "Albanese", role = "aut", email = "davide.albanese@fmach.it"), person("Samantha", "Riccadonna", role = "ctb", email = "samantha.riccadonna@fmach.it"), person("Giuseppe", "Jurman", role = "ctb", email = "jurman@fbk.eu"), person("Cesare", "Furlanello", role = "ctb", email = "furlan@fbk.eu")) Depends: R (>= 3.3.0) Imports: parallel, Rcpp, stats, graphics LinkingTo: Rcpp, RcppArmadillo Suggests: testthat Description: Wrapper for 'minepy' implementation of Maximal Information-based Nonparametric Exploration statistics (MIC and MINE family). Detailed information of the ANSI C implementation of 'minepy' can be found at . URL: https://www.r-project.org, http://minepy.readthedocs.io/en/latest, http://www.exploredata.net License: GPL-3 Packaged: 2019-05-27 07:27:43 UTC; michele Author: Michele Filosi [aut, cre], Roberto Visintainer [aut], Davide Albanese [aut], Samantha Riccadonna [ctb], Giuseppe Jurman [ctb], Cesare Furlanello [ctb] Maintainer: Michele Filosi Repository: CRAN Date/Publication: 2019-05-27 08:30:11 UTC RoxygenNote: 6.1.1 Encoding: UTF-8 NeedsCompilation: yes minerva/man/0000755000176200001440000000000013472710256012475 5ustar liggesusersminerva/man/minerva-package.Rd0000644000176200001440000000407213271553720016017 0ustar liggesusers\encoding{UTF-8} \name{minerva-package} \alias{minerva-package} \alias{minerva} \docType{package} \title{The minerva package} \description{ Maximal Information-Based Nonparametric Exploration R Package for Variable Analysis. The package provides the \code{\link{mine}} function allowing the computation of Maximal Information-based Nonparametric Exploration statistics, firstly introduced in D. Reshef et al. (2011) \emph{Detecting novel associations in large datasets}. Science 334, 6062 (\url{http://www.exploredata.net}). In particular, the package is an R wrapper for the C engine \emph{cmine} (\url{http://minepy.readthedocs.io/en/latest/}). } \details{ Summary: \tabular{ll}{ Package: \tab minerva\cr Version: \tab 1.4.3\cr Date: \tab 2014-10-08\cr Depends: \tab R >= (2.14.0)\cr Enshances: \tab parallel\cr URL: \tab http://www.r-project.org, \cr \tab http://minepy.readthedocs.io/en/latest/, \cr \tab http://www.exploredata.net\cr License: \tab GPL-3\cr } Index: \tabular{ll}{ \code{\link{Spellman}} \tab Yeast Gene Expression Dataset\cr \code{\link{mine}} \tab MINE-family statistics\cr \code{\link{minerva-package}} \tab The minerva package\cr } } \author{ Michele Filosi [aut, cre], Roberto Visintainer [aut], Davide Albanese [aut], Samantha Riccadonna [ctb], Giuseppe Jurman [ctb], Cesare Furlanello [ctb] Maintainer: Michele Filosi } \references{ D. Reshef, Y. Reshef, H. Finucane, S. Grossman, G. McVean, P. Turnbaugh, E. Lander, M. Mitzenmacher, P. Sabeti. (2011) \emph{Detecting novel associations in large datasets}. Science 334, 6062 (\href{http://www.exploredata.net}{http://www.exploredata.net}). D. Albanese, M. Filosi, R. Visintainer, S. Riccadonna, G. Jurman, C. Furlanello. \emph{cmine, minerva & minepy: a C engine for the MINE suite an its R and Python wrappers.} \url{http://minepy.readthedocs.io/en/latest/} \emph{minepy. Maximal Information-based Nonparametric Exploration in C and Python.}\cr (\url{http://minepy.sourceforge.net}) } \keyword{ package } minerva/man/mine_stat.Rd0000644000176200001440000000376313472710527014761 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/RcppExports.R \name{mine_stat} \alias{mine_stat} \title{This is an helper function to compute one \code{mine} statistic. It take two vectors of the same dimension as an input.} \usage{ mine_stat(x, y, alpha = 0.6, C = 15, est = "mic_approx", measure = "mic", eps = NA_real_, p = -1, norm = FALSE) } \arguments{ \item{x}{Numeric Vector of size \code{n}} \item{y}{Numeric Vector of size \code{n}} \item{alpha}{numeric value representing parameter for the mine statistic see \code{\link[minerva]{mine}}} \item{C}{c parameter for the mine statistic see \code{\link[minerva]{mine}}} \item{est}{character estimation parameter for the mine statistic. Possible values are \code{"mic_approx"} or \code{"mic_e"}} \item{measure}{integer indicating which measure to return available measures are: \code{mic, mas, mev, mcn, tic, gmic}. The string could be also uppercase. For measure \code{mic-r2} see details.} \item{eps}{eps value for MCN statistic should be in (0,1). If NA (default) is passed then the normal MCN statistic is returned.} \item{p}{probability for the generalized mic} \item{norm}{boolean if require normalization between 0 and 1 for the \code{tic} statistic} } \description{ This is an helper function to compute one \code{mine} statistic. It take two vectors of the same dimension as an input. } \details{ This is a wrapper function to compute the mine statistic between two variables. for more details on the available measure and the meaning of the other parameters see also the documentation for the \code{\link[minerva]{mine}} function. For measure \code{mic-r2} use the Pearson R coefficient score \code{\link[stats]{cor}} and the measure \code{mic}. See the example below. } \examples{ x <- runif(10); y <- 3*x+2; mine_stat(x,y, measure="mic") ## Measure mic-r2 x <- matrix(rnorm(20), ncol=2, nrow=10) mmic <- mine_stat(x[,1], x[,2], measure="mic") r2 <- cor(x[,1], x[,2]) mmic - r2**2 } \seealso{ \code{\link[minerva]{mine}} } minerva/man/mictools_null.Rd0000644000176200001440000000215013471776166015660 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/RcppExports.R \name{mictools_null} \alias{mictools_null} \title{This set of functions are helper function to compute null distribution of the \code{tic_e} and \code{tic_e} observed distribution from a matrix} \usage{ mictools_null(x, alpha = 9, C = 5, nperm = 200000L, seed = 0L) } \arguments{ \item{x}{matrix N x M with M variables and N samples} \item{alpha}{numeric value representing parameter for the mine statistic see \code{\link[minerva]{mine}}} \item{C}{c parameter for the mine statistic see \code{\link[minerva]{mine}}} \item{nperm}{numper of permutation} \item{seed}{integer to set the starting seed for random number generation (for reproducibility).} } \value{ It returns a vector of \code{nperm} \code{tic_e} values. } \description{ This set of functions are helper function to compute null distribution of the \code{tic_e} and \code{tic_e} observed distribution from a matrix } \section{Functions}{ \itemize{ \item \code{mictools_null}: compute the \code{tic_e} null distribution }} \seealso{ \code{\link[minerva]{mictools}} } minerva/man/pstats.Rd0000644000176200001440000000343613470715275014314 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/RcppExports.R \name{pstats} \alias{pstats} \title{Compute pairwise statistics (MIC and normalized TIC) between variables (convenience function).} \usage{ pstats(x, alpha = 0.6, C = 15, est = "mic_approx") } \arguments{ \item{x}{Numeric Matrix of m-by-n with n variables and m samples.} \item{alpha}{number (0, 1.0] or >=4 if alpha is in (0,1] then B will be max(n^alpha, 4) where n is the number of samples. If alpha is >=4 then alpha defines directly the B parameter. If alpha is higher than the number of samples (n) it will be limited to be n, so B = min(alpha, n).} \item{C}{number (> 0) determines how many more clumps there will be than columns in every partition. Default value is 15, meaning that when trying to draw x grid lines on the x-axis, the algorithm will start with at most 15*x clumps.} \item{est}{string ("mic_approx", "mic_e") estimator. With est="mic_approx" the original MINE statistics will be computed, with est="mic_e" the equicharacteristic matrix is is evaluated and MIC_e and TIC_e are returned.} } \value{ A matrix of (n x (n-1)/2) rows and 4 columns. The first and second column are the indexes relative to the columns in the input matrix \code{x} for which the statistic is computed for. Column 3 contains the MIC statistic, while column 4 contains the normalized TIC statistic. } \description{ For each statistic, the upper triangle of the matrix is stored by row (condensed matrix). If m is the number of variables, then for i < j < m, the statistic between (col) i and j is stored in k = m*i - i*(i+1)/2 - i - 1 + j. The length of the vectors is n = m*(m-1)/2. } \examples{ ## Create a matrix of random numbers ## 10 variables x 100 samples x <- matrix(rnorm(1000), ncol=10) res <- pstats(x) head(res) } minerva/man/mine.Rd0000644000176200001440000002604713470715275013731 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mine.R \name{mine} \alias{mine} \alias{MINE} \alias{MIC-R2} \alias{mic-r2} \title{MINE family statistics Maximal Information-Based Nonparametric Exploration (MINE) statistics. \code{mine} computes the MINE family measures between two variables.} \usage{ mine(x, y = NULL, master = NULL, alpha = 0.6, C = 15, n.cores = 1, var.thr = 1e-05, eps = NULL, est = "mic_approx", na.rm = FALSE, use = "all.obs", normalization = FALSE, ...) } \arguments{ \item{x}{a numeric vector (of size \emph{n}), matrix or data frame (which is coerced to matrix).} \item{y}{NULL (default) or a numeric vector of size \emph{n} (\emph{i.e.}, with compatible dimensions to x).} \item{master}{an optional vector of indices (numeric or character) to be given when \code{y} is not set, otherwise master is ignored. It can be either one column index to be used as reference for the comparison (versus all other columns) or a vector of column indices to be used for computing all mutual statistics.} \item{alpha}{float (0, 1.0] or >=4 if alpha is in (0,1] then B will be max(n^alpha, 4) where n is the number of samples. If alpha is >=4 then alpha defines directly the B parameter. If alpha is higher than the number of samples (n) it will be limited to be n, so B = min(alpha, n) Default value is 0.6 (see Details).} \item{C}{an optional number determining the starting point of the \emph{X-by-Y} search-grid. When trying to partition the \emph{x}-axis into \emph{X} columns, the algorithm will start with at most \code{C}\emph{X} \emph{clumps}. Default value is 15 (see Details).} \item{n.cores}{ooptional number of cores to be used in the computations, when master is specified. It requires the \pkg{parallel} package, which provides support for parallel computing, released with \R >= 2.14.0. Defaults is 1 (\emph{i.e.}, not performing parallel computing).} \item{var.thr}{minimum value allowed for the variance of the input variables, since \code{mine} can not be computed in case of variance close to 0. Default value is 1e-5. Information about failed check are reported in \emph{var_thr.log} file.} \item{eps}{integer in [0,1]. If 'NULL' (default) it is set to 1-MIC. It can be set to zero for noiseless functions, but the default choice is the most appropriate parametrization for general cases (as stated in Reshef et al. SOM). It provides robustness.} \item{est}{Default value is "mic_approx". With est="mic_approx" the original MINE statistics will be computed, with est="mic_e" the equicharacteristic matrix is is evaluated and the mic() and tic() methods will return MIC_e and TIC_e values respectively.} \item{na.rm}{boolean. This variable is passed directly to the \code{cor}-based functions. See \code{cor} for further details.} \item{use}{Default value is "all.obs". This variable is passed directly to the \code{cor}-based functions. See \code{cor} for further details.} \item{normalization}{logical whether to use normalization when computing \code{tic} measure. Ignored for other measures. Default to FALSE.} \item{\dots}{currently ignored} } \value{ The Maximal Information-Based Nonparametric Exploration (MINE) statistics provide quantitative evaluations of different aspects of the relationship between two variables. In particular \code{mine} returns a list of 5 statistics: \item{MIC}{ \strong{Maximal Information Coefficient.} \cr It is related to the relationship strenght and it can be interpreted as a correlation measure. It is symmetric and it ranges in [0,1], where it tends to 0 for statistically independent data and it approaches 1 in probability for noiseless functional relationships (more details can ben found in the original paper). } \item{MAS}{ \strong{Maximum Asymmetry Score.} \cr It captures the deviation from monotonicity. Note that \eqn{\textrm{MAS} < \textrm{MIC}}{MAS < MIC}. \cr \emph{Note:} it can be useful for detecting periodic relationships (unknown frequencies). } \item{MEV}{ \strong{Maximum Edge Value.} \cr It measures the closeness to being a function. Note that \eqn{\textrm{MEV} \leq \textrm{MIC}}{MEV <= MIC}. } \item{MCN}{ \strong{Minimum Cell Number.} \cr It is a complexity measure. } \item{MIC-R2}{It is the difference between the MIC value and the Pearson correlation coefficient. } \cr When computing \code{mine} between two numeric vectors \code{x} and \code{y}, the output is a list of 5 numeric values. When \code{master} is provided, \code{mine} returns a list of 5 matrices having \code{ncol} equal to \emph{m}. In particular, if \code{master} is a single value, then \code{mine} returns a list of 5 matrices having 1 column, whose rows correspond to the MINE measures between the \emph{master} column versus all. Instead if \code{master} is a vector of \emph{m} indices, then \code{mine} output is a list of 5 \emph{m-by-m} matrices, whose element \emph{i,j} corresponds to the MINE statistics computed between the \emph{i} and \emph{j} columns of \code{x}. } \description{ MINE family statistics Maximal Information-Based Nonparametric Exploration (MINE) statistics. \code{mine} computes the MINE family measures between two variables. } \details{ \code{mine} is an R wrapper for the C engine \emph{cmine} (\url{http://minepy.readthedocs.io/en/latest/}), an implementation of Maximal Information-Based Nonparametric Exploration (MINE) statistics. The MINE statistics were firstly detailed in D. Reshef et al. (2011) \emph{Detecting novel associations in large datasets}. Science 334, 6062 (\url{http://www.exploredata.net}). Here we recall the main concepts of the MINE family statistics. Let \eqn{D={(x,y)}} be the set of \emph{n} ordered pairs of elements of \code{x} and \code{y}. The data space is partitioned in an \emph{X-by-Y} grid, grouping the \emph{x} and \emph{y} values in \emph{X} and \emph{Y} bins respectively.\cr The \strong{Maximal Information Coefficient (MIC)} is defined as \deqn{\textrm{MIC}(D)=\max_{XY= (1-\epsilon)MIC(D)}.} More details are provided in the supplementary material (SOM) of the original paper. The MINE statistics can be computed for two numeric vectors \code{x} and \code{y}. Otherwise a matrix (or data frame) can be provided and two options are available according to the value of \code{master}. If \code{master} is a column identifier, then the MINE statistics are computed for the \emph{master} variable versus the other matrix columns. If \code{master} is a set of column identifiers, then all mutual MINE statistics are computed among the column subset. \code{master}, \code{alpha}, and \code{C} refers respectively to the \emph{style}, \emph{exp}, and \emph{c} parameters of the original \emph{java} code. In the original article, the authors state that the default value \eqn{\alpha=0.6} (which is the exponent of the search-grid size \eqn{B(n)=n^{\alpha}}) has been empirically chosen. It is worthwhile noting that \code{alpha} and \code{C} are defined to obtain an heuristic approximation in a reasonable amount of time. In case of small sample size (\emph{n}) it is preferable to increase \code{alpha} to 1 to obtain a solution closer to the theoretical one. } \examples{ A <- matrix(runif(50),nrow=5) mine(x=A, master=1) mine(x=A, master=c(1,3,5,7,8:10)) x <- runif(10); y <- 3*x+2; plot(x,y,type="l") mine(x,y) # MIC = 1 # MAS = 0 # MEV = 1 # MCN = 2 # MIC-R2 = 0 set.seed(100); x <- runif(10); y <- 3*x+2+rnorm(10,mean=2,sd=5); plot(x,y) mine(x,y) # rounded values of MINE statistics # MIC = 0.61 # MAS = 0 # MEV = 0.61 # MCN = 2 # MIC-R2 = 0.13 t <-seq(-2*pi,2*pi,0.2); y1 <- sin(2*t); plot(t,y1,type="l") mine(t,y1) # rounded values of MINE statistics # MIC = 0.66 # MAS = 0.37 # MEV = 0.66 # MCN = 3.58 # MIC-R2 = 0.62 y2 <- sin(4*t); plot(t,y2,type="l") mine(t,y2) # rounded values of MINE statistics # MIC = 0.32 # MAS = 0.18 # MEV = 0.32 # MCN = 3.58 # MIC-R2 = 0.31 # Note that for small n it is better to increase alpha mine(t,y1,alpha=1) # rounded values of MINE statistics # MIC = 1 # MAS = 0.59 # MEV = 1 # MCN = 5.67 # MIC-R2 = 0.96 mine(t,y2,alpha=1) # rounded values of MINE statistics # MIC = 1 # MAS = 0.59 # MEV = 1 # MCN = 5 # MIC-R2 = 0.99 # Some examples from SOM x <- runif(n=1000, min=0, max=1) # Linear relationship y1 <- x; plot(x,y1,type="l"); mine(x,y1) # MIC = 1 # MAS = 0 # MEV = 1 # MCN = 4 # MIC-R2 = 0 # Parabolic relationship y2 <- 4*(x-0.5)^2; plot(sort(x),y2[order(x)],type="l"); mine(x,y2) # rounded values of MINE statistics # MIC = 1 # MAS = 0.68 # MEV = 1 # MCN = 5.5 # MIC-R2 = 1 # Sinusoidal relationship (varying frequency) y3 <- sin(6*pi*x*(1+x)); plot(sort(x),y3[order(x)],type="l"); mine(x,y3) # rounded values of MINE statistics # MIC = 1 # MAS = 0.85 # MEV = 1 # MCN = 4.6 # MIC-R2 = 0.96 # Circle relationship t <- seq(from=0,to=2*pi,length.out=1000) x4 <- cos(t); y4 <- sin(t); plot(x4, y4, type="l",asp=1) mine(x4,y4) # rounded values of MINE statistics # MIC = 0.68 # MAS = 0.01 # MEV = 0.32 # MCN = 5.98 # MIC-R2 = 0.68 data(Spellman) res <- mine(Spellman,master=1,n.cores=1) \dontrun{## example of multicore computation res <- mine(Spellman,master=1,n.cores=parallel::detectCores()-1)} } \references{ D. Reshef, Y. Reshef, H. Finucane, S. Grossman, G. McVean, P. Turnbaugh, E. Lander, M. Mitzenmacher, P. Sabeti. (2011) \emph{Detecting novel associations in large datasets}. Science 334, 6062\cr \url{http://www.exploredata.net}\cr (SOM: Supplementary Online Material at \url{http://www.sciencemag.org/content/suppl/2011/12/14/334.6062.1518.DC1}) D. Albanese, M. Filosi, R. Visintainer, S. Riccadonna, G. Jurman, C. Furlanello. \emph{minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers}. Bioinformatics (2013) 29(3): 407-408, \doi{doi:10.1093/bioinformatics/bts707}.\cr \emph{minepy. Maximal Information-based Nonparametric Exploration in C and Python.}\cr \url{http://minepy.sourceforge.net} } \author{ Michele Filosi and Roberto Visintainer } minerva/man/mic_strength.Rd0000644000176200001440000001131513470721630015447 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mictools.R \name{mic_strength} \alias{mic_strength} \title{Compute the association strengh} \usage{ mic_strength(x, pval, alpha = NULL, C = 5, pthr = 0.05, pval.col = NULL) } \arguments{ \item{x}{a numeric matrix with N samples on the rows and M variables on the columns (NxM).} \item{pval}{a data.frame with pvalues for each pair of association of the \code{x} input matrix. It should contain two colums with the indices of the computed association according to the x input matrix} \item{alpha}{float (0, 1.0] or >=4 if alpha is in (0,1] then B will be max(n^alpha, 4) where n is the number of samples. If alpha is >=4 then alpha defines directly the B parameter. If alpha is higher than the number of samples (n) it will be limited to be n, so B = min(alpha, n) Default value is 0.6 (see Details).} \item{C}{a positive integer number, the \code{C} parameter of the \code{mine} statistic. See \code{\link[minerva]{mine}} function for further details.} \item{pthr}{threshold on pvalue for measure to consider for computing mic_e} \item{pval.col}{an integer or character or vector relative to the columns of \code{pval} dataframe respectively for \code{pvalue}, association between variable 1, variable 2 in the \code{x} input matrix. See Details for further information.} } \value{ A dataframe with the \code{tic_e} Pvalue, the \code{mic} value and the column identifier regarding the input matrix \code{x} of the variables of which the association is computed. } \description{ This function uses the null distribution of the \code{tic_e} computed with the function \code{\link[minerva]{mictools}}. Based on the available pvalue and the permutation null distribution it identifies reliable association between variables. } \details{ The method implemented here is a wrapper for the original method published by Albaese et al. (2018). The python version is available at \url{https://github.com/minepy/mictools}. This function should be called after the estimation of the null distribution of \code{tic_e} scores based on permutations of the input data. The \code{mic} association is computed only for the variables for which the pvalue in the \code{pval} \code{data.frame} is less then the threshold set with the \code{pthr} input parameter. We assume the first column of the \code{pval} \code{data.frame} contains the pvalue, this value can be changed using the \code{pval.col}[1] parameter. The \code{pval.col} parameter, by default takes the first three columns in the \code{pval} \code{data.frame}, in particular the first column containing the \code{pvalues} of the association between variable in column \code{pval.col[2]} and \code{pval.col[3]}. If a character vector is provided names in \code{pval.col} are matched with the names in \code{pval} \code{data.frame}. If \code{NULL} is passed it is assumed the first column contains pvalue, while the 2 and 3 the index or name of the variable in \code{x}. If one value is passed it refers to the \code{pvalue} column and the consecutive two columns are assume to contain variable indexes. } \examples{ data(Spellman) mydata <- as.matrix(Spellman[, 10:20]) ticenull <- mictools(mydata, nperm=1000) ## Use the nominal pvalue: ms <- mic_strength(mydata, pval=ticenull$pval, alpha=NULL, pval.col = c(6, 4,5)) ## Use the adjusted pvalue: ms <- mic_strength(mydata, pval=ticenull$pval, alpha=NULL, pval.col = c(6, 4,5)) ms \dontrun{ ## Use qvalue require(qvalue) qobj <- qvalue(ticenull$pval$pval) ticenull$pval$qvalue <- qobj$qvalue ms <- mic_strength(mydata, pval=ticenull$pval, alpha=NULL, pval.col = c("qvalue", "Var1", "Var2")) ## Get the data from mictools repository lnf <- "https://raw.githubusercontent.com/minepy/mictools/master/examples/datasaurus.txt" datasaurus <- read.table(lnf, header=TRUE, row.names = 1, stringsAsFactors = FALSE) datasaurus <- t(datasaurus) ticenull <- mictools(datasaurus, nperm=200000) micres <- mic_strength(mydata, ticenull$pval, pval.col=c(6, 4, 5)) ## Plot distribution of pvalues hist(ticenull$pval, breaks=50, freq=FALSE) ## Plot distribution of tic_e values hist(ticenull$tic) ## Correct pvalues using qvalue package require(qvalue) require(ggplot2) qobj <- qvalue(ticenull$pval$pval) ticenull$pval$qvalue <- qobj$qvalue micres <- mic_strength(datasaurus, ticenull$pval, pval.col=c("qvalue", "Var1", "Var2")) hist(qobj$qvalue) df <- data.frame(pi0.labmda=qobj$pi0.lambda, lambda=qobj$lambda, pi0.smooth=qobj$pi0.smooth) gp0 <- ggplot(df, aes(lambda, pi0.labmda)) + geom_point() gp0 <- gp0 + geom_line(aes(lambda, pi0.smooth)) gp0 <- gp0 + geom_hline(yintercept = qobj$pi0, linetype="dashed", col="red") } } \seealso{ \code{\link[minerva]{mine}}, \code{\link[minerva]{mictools}}, \code{\link[stats]{p.adjust}} } minerva/man/Spellman.Rd0000644000176200001440000000300213271553720014530 0ustar liggesusers\encoding{UTF-8} \name{Spellman} \docType{data} \alias{Spellman} \alias{spellman} \title{CDC15 Yeast Gene Expression Dataset} \description{ The Spellman dataset provides the gene expression data measured (on a custom platform) in \emph{Saccharomyces cerevisiae} cell cultures that have been synchronized at different points of the cell cycle by using a temperature-sensitive mutation (\emph{cdc15-2}), which arrestes cells late in mitosis at the restrictive temperature (it can cause heat-shock). } \usage{Spellman} \format{23 rows x 4382 columns: 4381 transcripts (columns 2:4382) measured at 23 timepoints (column 1).} \source{The original data were published by Spellman and colleagues in Mol. Biol. Cell (1998) as the Botstein dataset. Here we include the version of the dataset as processed by Reshef and colleagues for the MINE statistics original article published in Science (2011) (details are provided in the supplementary material).} \references{ D. Reshef, Y. Reshef, H. Finucane, S. Grossman, G. McVean, P. Turnbaugh, E. Lander, M. Mitzenmacher, P. Sabeti. (2011) \emph{Detecting novel associations in large datasets}. Science 334, 6062 (\url{http://www.exploredata.net}). P. T. Spellman, G. Sherlock, M. Q. Zhang, V. R. Iyer, K. Anders, M. B. Eisen, P. O. Brown, D. Botstein, B. Futcher. (1998) \emph{Comprehensive Identification of Cell Cycle–regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization}. Mol. Biol. Cell, 9:12 3273--3297. } \keyword{datasets} minerva/man/mictools.Rd0000644000176200001440000000671013470721601014613 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mictools.R \name{mictools} \alias{mictools} \title{Function that implements the \code{mictools} pipeline. In particular it computes the null and observed distribution of the \code{tic_e} measure} \usage{ mictools(x, alpha = 9, C = 5, seed = 0, nperm = 2e+05, p.adjust.method = "BH") } \arguments{ \item{x}{a numeric matrix with N samples on the rows and M variables on the columns (NxM).} \item{alpha}{float (0, 1.0] or >=4 if alpha is in (0,1] then B will be max(n^alpha, 4) where n is the number of samples. If alpha is >=4 then alpha defines directly the B parameter. If alpha is higher than the number of samples (n) it will be limited to be n, so B = min(alpha, n) Default value is 0.6 (see Details).} \item{C}{a positive integer number, the \code{C} parameter of the \code{mine} statistic. See \code{\link[minerva]{mine}} function for further details.} \item{seed}{seed for random number generation reproducibility} \item{nperm}{integer, number of permutation to perform} \item{p.adjust.method}{method for pvalue adjustment, see \code{\link[stats]{p.adjust}} for available methods.} } \value{ A list of 5 named elements containing the following information of the computed statistic: \describe{ \item{tic}{This is a vector with the null distribution of tic_e values based on the permutation.} \item{nulldist}{Null distribution of the \code{tic_e} measure. It is a \code{data.frame} of 4 columns containing the histogram of the distribution of \code{tic_e} for each bin delimited by \code{BinStart} and \code{BinEnd}, the count for each bin \code{NullCount} and the cumulative distribution of the right tail area \code{NullCumSum}} \item{obstic}{\code{data.frame} with the observed \code{tic_e} values, the indexes of the variables between the tic is computed. If the input matrix has column names then the names are reported in the dataframe, otherwise "Var" is added for each variable.} \item{obsdists}{\code{data.frame} similar to \code{nulldist} but with observed values of \code{tic_e}} \item{pval}{data.frame with the pvalue computed for each comparison. The adjusted pvalue is also reported based on the method chosen with the parameter \code{p.adjust.method}} } } \description{ Function that implements the \code{mictools} pipeline. In particular it computes the null and observed distribution of the \code{tic_e} measure } \details{ This is a function to implement the `mictools` pipeline. Differently from the python pipeline available on github we consider a data matrix of NxM with N samples by rows and M variables by columns as standard for R. } \examples{ data(Spellman) Spellman <- as.matrix(Spellman) spellress <- mictools(Spellman[, 10:20], nperm=1000) ## Use a different pvalue correction method spellressb <- mictools(Spellman[,10:20], nperm=1000, seed=1234, p.adjust.method="bonferroni") ## Distribution of tic_e null hist(spellress$tic, breaks=100, main="Tic_e null distribution") barplot(spellress$nulldist$NullCount) ## Distribution of the observed tic hist(spellress$obstic$TIC) barplot(spellress$obsdist$Count) ## Distribution of empirical pvalues hist(spellress$pval$pval, breaks=50) } \references{ D. Albanese, S. Riccadonna, C. Donati, P. Franceschi (2018) _A practical tool for Maximal Information Coefficient Analysis_ GigaScience, 7, 4, \url{https://doi.org/10.1093/gigascience/giy032} } \seealso{ \code{\link[stats]{p.adjust}}, \code{\link[graphics]{hist}}, \code{\link[minerva]{mine}} } minerva/man/cstats.Rd0000644000176200001440000000370413471734163014273 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/RcppExports.R \name{cstats} \alias{cstats} \title{Compute statistics (MIC and normalized TIC) between each pair of the two collections of variables (convenience function). If n and m are the number of variables in X and Y respectively, then the statistic between the (row) i (for X) and j (for Y) is stored in \code{mic[i, j]} and \code{tic[i, j]}.} \usage{ cstats(x, y, alpha = 0.6, C = 15, est = "mic_approx") } \arguments{ \item{x}{Numeric Matrix of m-by-n with n variables and m samples.} \item{y}{Numeric Matrix of m-by-p with p variables and m samples.} \item{alpha}{number (0, 1.0] or >=4 if alpha is in (0,1] then B will be max(n^alpha, 4) where n is the number of samples. If alpha is >=4 then alpha defines directly the B parameter. If alpha is higher than the number of samples (n) it will be limited to be n, so B = min(alpha, n).} \item{C}{number (> 0) determines how many more clumps there will be than columns in every partition. Default value is 15, meaning that when trying to draw x grid lines on the x-axis, the algorithm will start with at most 15*x clumps.} \item{est}{string ("mic_approx", "mic_e") estimator. With est="mic_approx" the original MINE statistics will be computed, with est="mic_e" the equicharacteristic matrix is is evaluated and MIC_e and TIC_e are returned.} } \value{ list of two elements: MIC: the MIC statistic matrix (n x p). TIC: the normalized TIC statistic matrix (n x p). } \description{ Compute statistics (MIC and normalized TIC) between each pair of the two collections of variables (convenience function). If n and m are the number of variables in X and Y respectively, then the statistic between the (row) i (for X) and j (for Y) is stored in \code{mic[i, j]} and \code{tic[i, j]}. } \examples{ x <- matrix(rnorm(2560), ncol=8, nrow=320) y <- matrix(rnorm(1280), ncol=4, nrow=320) mictic <- cstats(x, y, alpha=9, C=5, est="mic_e") head(mictic) }