From dbdeac74c2b12763db5c150888a185856dcbdea0 Mon Sep 17 00:00:00 2001 From: Cameron Craddock Date: Wed, 25 Jan 2017 19:59:38 -0500 Subject: [PATCH] updated anat struct plot --- data_analysis/interactive_plots.ipynb | 503 +++++++++++++++++++------- 1 file changed, 366 insertions(+), 137 deletions(-) diff --git a/data_analysis/interactive_plots.ipynb b/data_analysis/interactive_plots.ipynb index a7c92a2..dbd573b 100644 --- a/data_analysis/interactive_plots.ipynb +++ b/data_analysis/interactive_plots.ipynb @@ -2,15 +2,40 @@ "cells": [ { "cell_type": "code", - "execution_count": 13, + "execution_count": 1, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Attaching package: ‘dplyr’\n", + "\n", + "The following objects are masked from ‘package:stats’:\n", + "\n", + " filter, lag\n", + "\n", + "The following objects are masked from ‘package:base’:\n", + "\n", + " intersect, setdiff, setequal, union\n", + "\n", + "\n", + "Attaching package: ‘tidyr’\n", + "\n", + "The following object is masked from ‘package:reshape2’:\n", + "\n", + " smiths\n", + "\n" + ] + } + ], "source": [ "source(\"qa_plot_functions.R\")\n", "library(reshape2)\n", - "library(dplyr)" + "library(tidyr)" ] }, { @@ -29,13 +54,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "pheno_df<-read.csv(\"http://s3.amazonaws.com/fcp-indi-new/data/Projects/ABIDE_Initiative/Phenotypic_V1_0b_preprocessed1.csv\")" + "pheno_df<-read.csv(\"http://s3.amazonaws.com/fcp-indi/data/Projects/ABIDE/Phenotypic_V1_0b_preprocessed1.csv\")" ] }, { @@ -47,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -132,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -152,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -179,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -232,7 +257,10 @@ "\n", "id.vars=c('Participant','Site','Session','Series','qc_anat')\n", "measure.vars=c('CNR','Cortical.Contrast','EFC','FBER','FWHM','Qi1','SNR')\n", - "abide_anat_spat_df=abide_anat_spat_df[c(id.vars,measure.vars)]\n", + "abide_anat_spat_df<-abide_anat_spat_df[c(id.vars,measure.vars)]\n", + "\n", + "# log transform FBER to give a range more similar to the other measures\n", + "# abide_anat_spat_df[,'FBER']<-log10(abide_anat_spat_df[,'FBER'])\n", "\n", "\n", "abide_anat_spat_df=melt(abide_anat_spat_df,\n", @@ -252,104 +280,68 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 7, "metadata": { "collapsed": false }, - "outputs": [ - { - "ename": "ERROR", - "evalue": "Error in eval(expr, envir, enclos): could not find function \"ddply\"\n", - "output_type": "error", - "traceback": [ - "Error in eval(expr, envir, enclos): could not find function \"ddply\"\nTraceback:\n" - ] - } - ], + "outputs": [], "source": [ - "df <- subset(abide_anat_spat_df)\n", - "df <- ddply(df, .(Measure), function(x) {\n", - " x <- x[!is.na(x$value),]\n", - " inds <- get_outlier_inds(x$value)\n", - " x[!inds,]\n", - "})" + "df <- abide_anat_spat_df %>% drop_na()" ] }, { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Attaching package: ‘gridExtra’\n", - "\n", - "The following object is masked from ‘package:dplyr’:\n", - "\n", - " combine\n", - "\n" - ] - } - ], + "cell_type": "markdown", + "metadata": {}, "source": [ - "library(grid)\n", - "library(gridExtra)\n" + "### Plots" ] }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 142, "metadata": { "collapsed": false }, "outputs": [], "source": [ + "library(grid)\n", + "library(gridExtra)\n", "library(repr)\n", - "options(repr.plot.width=7, repr.plot.height=3)" + "library(Hmisc)\n", + "options(repr.plot.width=7, repr.plot.height=3)\n", + "\n", + "mean_sdlow<-function(x){\n", + " return(mean(x)-sd(x))\n", + "}\n", + "mean_sdhigh<-function(x){\n", + " return(mean(x)+sd(x))\n", + "}" ] }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 193, "metadata": { - "collapsed": false + "collapsed": false, + "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 2% 98% \n", - " 5.255683 18.078164 \n", - "[1] \"CNR, 1, 1, 1\"\n", - " 2% 98% \n", - "0.2398590 0.4317548 \n", - "[1] \"Cortical.Contrast, 2, 1, 2\"\n", - " 2% 98% \n", - "0.3484598 0.5345501 \n", - "[1] \"EFC, 3, 1, 3\"\n", - " 2% 98% \n", - " 119.1084 1315.2591 \n", - "[1] \"FBER, 4, 1, 4\"\n", - " 2% 98% \n", - "2.594859 5.196846 \n", - "[1] \"FWHM, 5, 1, 5\"\n", - " 2% 98% \n", - "6.894695e-05 3.546160e-01 \n", - "[1] \"Qi1, 6, 1, 6\"\n", - " 2% 98% \n", - "12.19066 36.22605 \n", - "[1] \"SNR, 7, 1, 7\"\n" + "[1] \"CNR *** pval 1.920039e-13\"\n", + "[1] \"Cortical Contrast *** pval 5.340859e-07\"\n", + "[1] \"EFC ** pval 1.580392e-03\"\n", + "[1] \"FBER ** pval 2.463082e-03\"\n", + "[1] \"Smoothness (FWHM) pval 3.906959e-01\"\n", + "[1] \"Fraction of Artifact Voxels *** pval 6.163532e-11\"\n", + "[1] \"SNR *** pval 5.487315e-04\"\n" ] }, { "data": { - "image/png": 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+vo8BJasmTJcKd5jASyhYD2MgrFR0u4B/B1112nHsAfffSRtG7dOhCSoVOnTvLa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StXlqFDh6qQDy+++KJ07949JC3iGb7yyitBx8uUKaNm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\nhueff75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BFwGQJQ7vHBAoVdiVU5VtuBNseiTpRB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\nu3pNQvrhXbxEquz8Vc4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NAwaQ4mMFWG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\n2IPiHTtRK0m6PBXgU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"text/plain": [ "plot without title" ] @@ -367,67 +359,169 @@ "#print(a, vp = vplayout(1, 1:2)) # key is to define vplayout\n", "#print(b, vp = vplayout(2, 1))\n", "#print(c, vp = vplayout(2, 2))\n", + "label_strings=c('CNR','Cortical Contrast','EFC','FBER','Smoothness (FWHM)','Fraction of Artifact Voxels','SNR')\n", + "pval_thresh=c(.05,.01,.001)\n", + " \n", + "for (i in seq(1,length(measure.vars)))\n", + "{\n", + " # get only the rows corresponding to this measure\n", + " sdf<- df %>% filter(Measure == measure.vars[i])\n", + " sdf_ranges <- sdf %>% \n", + " group_by(qc_anat) %>% \n", + " summarise(y=mean(value),y.max=mean_sdhigh(value),y.min=mean_sdlow(value)) %>% \n", + " ungroup\n", + "\n", + " # get the lable and annotate to indicate statistical significance of the difference\n", + " pval<-t.test(value~as.factor(qc_anat),data=sdf)$p.value\n", + " \n", + " pval_string=\"\"\n", + " for (pt in pval_thresh)\n", + " {\n", + " if(pval < pt)\n", + " {\n", + " pval_string=paste(pval_string,'*',sep='')\n", + " }\n", + " else\n", + " {\n", + " break \n", + " }\n", + " }\n", + " \n", + " print(paste(label_strings[i],pval_string,sprintf('pval %e',pval)))\n", + " \n", + " p <- ggplot(sdf, aes(x=as.factor(qc_anat), y=value, fill=as.factor(qc_anat))) + \n", + " geom_boxplot(outlier.shape = NA, width=.5, fill='white') + \n", + " geom_point(data=sdf_ranges,aes(x=qc_anat,y=y),color=\"red\",fill=\"red\",size=1)+\n", + " geom_point(data=sdf_ranges,aes(x=qc_anat,y=y.max),shape=24,color=\"red\",fill=\"red\",size=1)+\n", + " geom_point(data=sdf_ranges,aes(x=qc_anat,y=y.min),shape=25,color=\"red\",fill=\"red\",size=1)+\n", + " theme_bw() +\n", + " ggtitle(pval_string)+\n", + " xlab(\"\")+\n", + " ylab(label_strings[i])+\n", + " theme(legend.position = \"none\",\n", + " plot.title = element_text(family = \"ArialMT\",\n", + " face = \"plain\",\n", + " size = 10,\n", + " vjust = 0),\n", + " axis.text.x = element_text(family = \"ArialMT\",\n", + " face = \"plain\",\n", + " size = 8, \n", + " angle = 0, \n", + " hjust = 0.5),\n", + " axis.text.y = element_text(family = \"ArialMT\", \n", + " face = \"plain\", \n", + " size = 8, \n", + " angle = 90, \n", + " hjust = 0.5),\n", + " axis.title.y = element_text(family = \"ArialMT\", \n", + " face = \"plain\", \n", + " size = 10, \n", + " angle = 90, \n", + " vjust = 0.9),\n", + " plot.margin = unit(c(.25, .25, 0.25, .25), \n", + " \"lines\")) +\n", "\n", - "for (i in seq(1,length(measure.vars))){\n", - " x=ceiling(i/ncol)\n", - " y=((i-1) %% ncol)+1\n", - " m=measure.vars[i]\n", - " sdf<-subset(df,Measure==m)\n", - " rating.means <- with(sdf, tapply(value, qc_anat, mean))\n", - " rating.sds <- with(sdf, tapply(value, qc_anat, sd))\n", - " rating.ranges<- with(sdf, quantile(value, probs = c(0.02,.98))) #sort(rating.means) + (rating.sds[order(rating.means)] * c(-1,1))\n", - " print(rating.ranges)\n", - " p <- ggplot(sdf, aes(x=as.factor(qc_anat), y=value, fill=as.factor(qc_anat))) + \n", - " #geom_violin(trim = TRUE) + \n", - " geom_boxplot(outlier.shape = NA,width=1) + \n", - " theme_bw() +\n", - " theme(legend.position = \"none\",\n", - " axis.text.x = element_text(family = \"Times\", face = \"plain\",size=8, angle=45, hjust=0.75),\n", - " axis.text.y = element_text(family = \"Times\", face = \"plain\",size=8, angle=0, hjust=0.75),\n", - " axis.title.y = element_text(family = \"Times\", face = \"plain\", size=10, angle=90, vjust=0.9),\n", - " plot.margin = unit(c(.25, .25, 0.25, .25), \"lines\")) +\n", - " xlab(\"\")+\n", - " ylab(m)+\n", + " # reduce the range to between the 2% and 98% quartiles to make the plot\n", + " # range more readible, do this without censoring so that the bars will go\n", + " # out of frame\n", + " coord_cartesian(ylim=with(sdf, quantile(value, probs = c(0.02,.98))))\n", + " \n", "\n", - " #geom_bar(stat=\"identity\") + \n", - " #stat_summary(fun.y=\"mean\", geom=\"bar\") + \n", - "# facet_grid(measure ~ ., scale=\"free_y\") +\n", - " coord_cartesian(ylim=rating.ranges)\n", - " #ylab(as.character(sdf$measure[1])) +\n", - " #xlab(\"\") + \n", - " #theme_bw() #+ \n", - " #theme(panel.border=element_blank()) +\n", - " #theme(axis.line=element_line(), axis.line.y=element_blank()) +\n", - " #theme(panel.grid.major.x= element_blank()) + \n", - " #theme(panel.grid.minor.x= element_blank()) + \n", - " #theme(panel.grid.major.y= element_line(color=\"grey50\")) + \n", - " #theme(panel.grid.minor.y= element_blank()) + \n", - " #theme(axis.title.x = element_blank()) + \n", - " #theme(axis.title.y = element_text(family = \"Century Gothic\", face = \"plain\", \n", - " # size=18, angle=90, vjust=0.9)) + \n", - " #theme(axis.text.x = element_blank()) +\n", - " #theme(axis.text.y = element_text(family = \"Times\", face = \"plain\", \n", - " # size=16, angle=0, hjust=0.75)) + \n", - " #theme(axis.ticks.y = element_line(color=\"grey50\")) + \n", - " #theme(axis.ticks.length = unit(.15, \"lines\")) + \n", - " #theme(axis.ticks.margin = unit(.3, \"lines\")) + \n", - " #theme(axis.ticks.x = element_blank()) +\n", - " #theme(plot.margin = unit(c(1, 1, 0.25, 1), \"lines\")) +\n", - " #theme(legend.position = \"none\")\n", - " print(sprintf(\"%s, %d, %d, %d\",m,i,x,y))\n", - " print(p, vp = vplayout(x, y))\n", + " # get the location of this plot in the grid\n", + " x=ceiling(i/ncol)\n", + " y=((i-1) %% ncol)+1\n", + " \n", + " # add the plot to the grid\n", + " print(p, vp = vplayout(x, y))\n", "}" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 169, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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tn93AMCBAgQIECAAAECBAgMViAXASlN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\nnHPOiQ984APx+OOP91z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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "grid.arrange(pp, ncol = 2, main = \"Main title\")" + "p1=ggplot()+\n", + " geom_point(data=sdf_ranges,aes(x=qc_anat,y=y),color=\"red\",fill=\"red\")+\n", + " geom_point(data=sdf_ranges,aes(x=qc_anat,y=y.max),shape=24,color=\"red\",fill=\"red\")+\n", + " geom_point(data=sdf_ranges,aes(x=qc_anat,y=y.min),shape=25,color=\"red\",fill=\"red\")\n", + "print(p1)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tWelch Two Sample t-test\n", + "\n", + "data: value by as.factor(qc_anat)\n", + "t = -3.0543, df = 292.75, p-value = 0.002463\n", + "alternative hypothesis: true difference in means is not equal to 0\n", + "95 percent confidence interval:\n", + " -109.45350 -23.67234\n", + "sample estimates:\n", + "mean in group Fail mean in group OK \n", + " 434.8864 501.4493 \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
qc_anatavgsdq25q50q75
1Fail 434.886410728436244.250138655491231.653178865525385.50348214805 580.05019595755
2OK 501.449331879401334.534785667283268.338257593875357.15760786555 722.5044658498
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|llllll}\n", + " & qc\\_anat & avg & sd & q25 & q50 & q75\\\\\n", + "\\hline\n", + "\t1 & Fail & 434.886410728436 & 244.250138655491 & 231.653178865525 & 385.50348214805 & 580.05019595755 \\\\\n", + "\t2 & OK & 501.449331879401 & 334.534785667283 & 268.338257593875 & 357.15760786555 & 722.5044658498 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + " qc_anat avg sd q25 q50 q75\n", + "1 Fail 434.8864 244.2501 231.6532 385.5035 580.0502\n", + "2 OK 501.4493 334.5348 268.3383 357.1576 722.5045" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i=4\n", + "sdf<- df %>% filter(Measure == measure.vars[i])\n", + "t.test(value~as.factor(qc_anat),data=sdf)\n", + "sdf %>% group_by(qc_anat) %>% summarise(avg=mean(value),sd=sd(value),q25=quantile(value,probs=(0.25)),q50=quantile(value,probs=(0.5)),q75=quantile(value,probs=(0.75)))\n" ] }, { @@ -443,18 +537,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 141, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "rm(list=\"p\")" + "?smean.sdl\n" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -462,16 +556,35 @@ { "data": { "text/html": [ - "2" + "0.519966214327838" ], "text/latex": [ - "2" + "0.519966214327838" ], "text/markdown": [ - "2" + "0.519966214327838" ], "text/plain": [ - "[1] 2" + "[1] 0.5199662" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "\tWelch Two Sample t-test\n", + "\n", + "data: x and y\n", + "t = 1.1627, df = 9.1884, p-value = 0.2743\n", + "alternative hypothesis: true difference in means is not equal to 0\n", + "95 percent confidence interval:\n", + " -2.654682 8.305496\n", + "sample estimates:\n", + "mean of x mean of y \n", + " 9.818199 6.992792 \n" ] }, "metadata": {}, @@ -479,21 +592,137 @@ } ], "source": [ - "ceiling(3/2)\n" + "library(lsr)\n", + "set.seed(45)\n", + "x <- rnorm(10, 10, 1)\n", + "y <- rnorm(10, 5, 5)\n", + "cohensD(x,y)\n", + "t.test(x,y)" ] }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
qc_anatavgsdq25q50q75
1Fail 19.17726206799055.3262694241613415.41765250435 18.13440677625 21.90767288585
2OK 20.783515046774 6.2468480180916516.45474154165 18.867179952 23.9282242726
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|llllll}\n", + " & qc\\_anat & avg & sd & q25 & q50 & q75\\\\\n", + "\\hline\n", + "\t1 & Fail & 19.1772620679905 & 5.32626942416134 & 15.41765250435 & 18.13440677625 & 21.90767288585 \\\\\n", + "\t2 & OK & 20.783515046774 & 6.24684801809165 & 16.45474154165 & 18.867179952 & 23.9282242726 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + " qc_anat avg sd q25 q50 q75\n", + "1 Fail 19.17726 5.326269 15.41765 18.13441 21.90767\n", + "2 OK 20.78352 6.246848 16.45474 18.86718 23.92822" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sdf %>% group_by(qc_anat) %>% summarise(avg=mean(value),sd=sd(value),q25=quantile(value,probs=(0.25)),q50=quantile(value,probs=(0.5)),q75=quantile(value,probs=(0.75)))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "also installing the dependency ‘acepack’\n", + "\n", + "Updating HTML index of packages in '.Library'\n", + "Making 'packages.html' ... done\n" + ] + } + ], + "source": [ + "install.packages(\"Hmisc\")" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ERROR", + "evalue": "Error in eval(expr, envir, enclos): could not find function \"ddply\"\n", + "output_type": "error", + "traceback": [ + "Error in eval(expr, envir, enclos): could not find function \"ddply\"\nTraceback:\n" + ] + } + ], + "source": [ + "ddply(sdf, \"qc_anat\", summarise, avg = mean(value), sd=sd(value), q=quantile(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "?stat_summary" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
qc_anatavg
1Fail 8.51334176030536
2OK 10.25409805617
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|ll}\n", + " & qc\\_anat & avg\\\\\n", + "\\hline\n", + "\t1 & Fail & 8.51334176030536\\\\\n", + "\t2 & OK & 10.25409805617\\\\\n", + "\\end{tabular}\n" + ], "text/plain": [ - "No vignettes or demos or help files found with alias or concept or\n", - "title matching ‘precentiles’ using fuzzy matching." + " qc_anat avg\n", + "1 Fail 8.513342\n", + "2 OK 10.254098" ] }, "metadata": {}, @@ -501,7 +730,7 @@ } ], "source": [ - "??precentiles" + "ungroup(means)\n" ] }, {