{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Computing capstone exercise: Solutions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data\n", "\n", "The data set consists of a simulated (and highly contrived) gene expression levels for 100 subjects. 50 of the subjects are cases, and 50 are controls.\n", "\n", "- The expression level of 20,000 genes for each subject is found in a file `expr-XXX.txt` where `XXX` is the subject ID. Missing values are indicated by the string `nan`.\n", "- The file `cases.txt` contain the IDs of subjects who are in the cases group.\n", "- The file `controls.txt` contains the IDs of subjects who are in the controls group.\n", "- The file `outcomes.txt` contains the subject ID and blood sugar level for all subjects." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Unix shell/command line\n", "\n", "For this part - click on the `Kernel` menu item and select `Change Kernel | Bash`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Download the data from https://www.dropbox.com/s/vivut71p4bkurhw/data.tar.gz\n", "- You will need to quote the URL" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2017-07-27 10:06:58-- https://www.dropbox.com/s/vivut71p4bkurhw/data.tar.gz\n", "Resolving www.dropbox.com... 162.125.6.1, 2620:100:601c:1::a27d:601\n", "Connecting to www.dropbox.com|162.125.6.1|:443... connected.\n", "HTTP request sent, awaiting response... 302 Found\n", "Location: https://dl.dropboxusercontent.com/content_link/zTZFO6FdHTj3YjweRBYauZZ5YuV5Ej0gsr2rtOUPSdbCs0NQ40ADNWTJMcOm1wGd/file [following]\n", "--2017-07-27 10:06:58-- https://dl.dropboxusercontent.com/content_link/zTZFO6FdHTj3YjweRBYauZZ5YuV5Ej0gsr2rtOUPSdbCs0NQ40ADNWTJMcOm1wGd/file\n", "Resolving dl.dropboxusercontent.com... 162.125.6.6\n", "Connecting to dl.dropboxusercontent.com|162.125.6.6|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 3973292 (3.8M) [application/octet-stream]\n", "Saving to: ‘data.tar.gz.1’\n", "\n", "data.tar.gz.1 100%[===================>] 3.79M 7.28MB/s in 0.5s \n", "\n", "2017-07-27 10:07:00 (7.28 MB/s) - ‘data.tar.gz.1’ saved [3973292/3973292]\n", "\n" ] } ], "source": [ "wget \"https://www.dropbox.com/s/vivut71p4bkurhw/data.tar.gz\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Regenerate the original data folder from `data.tar.gz`" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tar -xzf data.tar.gz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Check if any files have been corrupted using the MDFSUM checksum file and note its " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cases.txt: OK\n", "controls.txt: OK\n", "expr-1001.txt: OK\n", "expr-1002.txt: OK\n", "expr-1003.txt: OK\n", "expr-1004.txt: OK\n", "expr-1005.txt: OK\n", "expr-1006.txt: OK\n", "expr-1007.txt: OK\n", "expr-1008.txt: OK\n", "expr-1009.txt: OK\n", "expr-1010.txt: OK\n", "expr-1011.txt: OK\n", "expr-1012.txt: OK\n", "expr-1013.txt: OK\n", "expr-1014.txt: OK\n", "expr-1015.txt: OK\n", "expr-1016.txt: OK\n", "expr-1017.txt: OK\n", "expr-1018.txt: OK\n", "expr-1019.txt: OK\n", "expr-1020.txt: OK\n", "expr-1021.txt: OK\n", "expr-1022.txt: OK\n", "expr-1023.txt: OK\n", "expr-1024.txt: OK\n", "expr-1025.txt: OK\n", "expr-1026.txt: OK\n", "expr-1027.txt: OK\n", "expr-1028.txt: OK\n", "expr-1029.txt: OK\n", "expr-1030.txt: OK\n", "expr-1031.txt: OK\n", "expr-1032.txt: OK\n", "expr-1033.txt: OK\n", "expr-1034.txt: OK\n", "expr-1035.txt: OK\n", "expr-1036.txt: OK\n", "expr-1037.txt: OK\n", "expr-1038.txt: OK\n", "expr-1039.txt: OK\n", "expr-1040.txt: OK\n", "expr-1041.txt: OK\n", "expr-1042.txt: OK\n", "expr-1043.txt: OK\n", "expr-1044.txt: OK\n", "expr-1045.txt: OK\n", "expr-1046.txt: OK\n", "expr-1047.txt: OK\n", "expr-1048.txt: OK\n", "expr-1049.txt: OK\n", "expr-1050.txt: OK\n", "expr-1051.txt: OK\n", "expr-1052.txt: OK\n", "expr-1053.txt: OK\n", "expr-1054.txt: OK\n", "expr-1055.txt: OK\n", "expr-1056.txt: OK\n", "expr-1057.txt: OK\n", "expr-1058.txt: OK\n", "expr-1059.txt: OK\n", "expr-1060.txt: OK\n", "expr-1061.txt: OK\n", "expr-1062.txt: OK\n", "expr-1063.txt: OK\n", "expr-1064.txt: OK\n", "expr-1065.txt: OK\n", "expr-1066.txt: OK\n", "expr-1067.txt: OK\n", "expr-1068.txt: OK\n", "expr-1069.txt: OK\n", "expr-1070.txt: OK\n", "expr-1071.txt: OK\n", "expr-1072.txt: OK\n", "expr-1073.txt: OK\n", "expr-1074.txt: FAILED\n", "expr-1075.txt: OK\n", "expr-1076.txt: OK\n", "expr-1077.txt: OK\n", "expr-1078.txt: OK\n", "expr-1079.txt: OK\n", "expr-1080.txt: OK\n", "expr-1081.txt: OK\n", "expr-1082.txt: OK\n", "expr-1083.txt: OK\n", "expr-1084.txt: OK\n", "expr-1085.txt: OK\n", "expr-1086.txt: OK\n", "expr-1087.txt: OK\n", "expr-1088.txt: OK\n", "expr-1089.txt: OK\n", "expr-1090.txt: OK\n", "expr-1091.txt: OK\n", "expr-1092.txt: OK\n", "expr-1093.txt: OK\n", "expr-1094.txt: OK\n", "expr-1095.txt: OK\n", "expr-1096.txt: OK\n", "expr-1097.txt: OK\n", "expr-1098.txt: OK\n", "expr-1099.txt: OK\n", "expr-1100.txt: OK\n", "outcomes.txt: OK\n", "md5sum: WARNING: 1 of 103 computed checksums did NOT match\n" ] }, { "ename": "", "evalue": "1", "output_type": "error", "traceback": [] } ], "source": [ "cd data\n", "md5sum -c MD5SUM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Delete the corrupted file and download a correct copy from https://www.dropbox.com/s/vf8qcoj07mcq7wn/FILENAME\n", "- You will need to replace FILENAME with the correct filename" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2017-07-27 10:07:04-- https://www.dropbox.com/s/vf8qcoj07mcq7wn/expr-1074.txt\n", "Resolving www.dropbox.com... 162.125.6.1, 2620:100:601c:1::a27d:601\n", "Connecting to www.dropbox.com|162.125.6.1|:443... connected.\n", "HTTP request sent, awaiting response... 302 Found\n", "Location: https://dl.dropboxusercontent.com/content_link/GMBFcFrWZQOBq8z3MtOT0haOIcLYErRBgLumqB7oFbhwdCbQbJtKU3kLjf2lArVp/file [following]\n", "--2017-07-27 10:07:05-- https://dl.dropboxusercontent.com/content_link/GMBFcFrWZQOBq8z3MtOT0haOIcLYErRBgLumqB7oFbhwdCbQbJtKU3kLjf2lArVp/file\n", "Resolving dl.dropboxusercontent.com... 162.125.6.6\n", "Connecting to dl.dropboxusercontent.com|162.125.6.6|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 111905 (109K) [text/plain]\n", "Saving to: ‘expr-1074.txt’\n", "\n", "expr-1074.txt 100%[===================>] 109.28K --.-KB/s in 0.03s \n", "\n", "2017-07-27 10:07:05 (3.32 MB/s) - ‘expr-1074.txt’ saved [111905/111905]\n", "\n" ] } ], "source": [ "rm expr-1074.txt\n", "wget \"https://www.dropbox.com/s/vf8qcoj07mcq7wn/expr-1074.txt\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Check that there are no `md5sum` errors" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cases.txt: OK\n", "controls.txt: OK\n", "expr-1001.txt: OK\n", "expr-1002.txt: OK\n", "expr-1003.txt: OK\n", "expr-1004.txt: OK\n", "expr-1005.txt: OK\n", "expr-1006.txt: OK\n", "expr-1007.txt: OK\n", "expr-1008.txt: OK\n", "expr-1009.txt: OK\n", "expr-1010.txt: OK\n", "expr-1011.txt: OK\n", "expr-1012.txt: OK\n", "expr-1013.txt: OK\n", "expr-1014.txt: OK\n", "expr-1015.txt: OK\n", "expr-1016.txt: OK\n", "expr-1017.txt: OK\n", "expr-1018.txt: OK\n", "expr-1019.txt: OK\n", "expr-1020.txt: OK\n", "expr-1021.txt: OK\n", "expr-1022.txt: OK\n", "expr-1023.txt: OK\n", "expr-1024.txt: OK\n", "expr-1025.txt: OK\n", "expr-1026.txt: OK\n", "expr-1027.txt: OK\n", "expr-1028.txt: OK\n", "expr-1029.txt: OK\n", "expr-1030.txt: OK\n", "expr-1031.txt: OK\n", "expr-1032.txt: OK\n", "expr-1033.txt: OK\n", "expr-1034.txt: OK\n", "expr-1035.txt: OK\n", "expr-1036.txt: OK\n", "expr-1037.txt: OK\n", "expr-1038.txt: OK\n", "expr-1039.txt: OK\n", "expr-1040.txt: OK\n", "expr-1041.txt: OK\n", "expr-1042.txt: OK\n", "expr-1043.txt: OK\n", "expr-1044.txt: OK\n", "expr-1045.txt: OK\n", "expr-1046.txt: OK\n", "expr-1047.txt: OK\n", "expr-1048.txt: OK\n", "expr-1049.txt: OK\n", "expr-1050.txt: OK\n", "expr-1051.txt: OK\n", "expr-1052.txt: OK\n", "expr-1053.txt: OK\n", "expr-1054.txt: OK\n", "expr-1055.txt: OK\n", "expr-1056.txt: OK\n", "expr-1057.txt: OK\n", "expr-1058.txt: OK\n", "expr-1059.txt: OK\n", "expr-1060.txt: OK\n", "expr-1061.txt: OK\n", "expr-1062.txt: OK\n", "expr-1063.txt: OK\n", "expr-1064.txt: OK\n", "expr-1065.txt: OK\n", "expr-1066.txt: OK\n", "expr-1067.txt: OK\n", "expr-1068.txt: OK\n", "expr-1069.txt: OK\n", "expr-1070.txt: OK\n", "expr-1071.txt: OK\n", "expr-1072.txt: OK\n", "expr-1073.txt: OK\n", "expr-1074.txt: OK\n", "expr-1075.txt: OK\n", "expr-1076.txt: OK\n", "expr-1077.txt: OK\n", "expr-1078.txt: OK\n", "expr-1079.txt: OK\n", "expr-1080.txt: OK\n", "expr-1081.txt: OK\n", "expr-1082.txt: OK\n", "expr-1083.txt: OK\n", "expr-1084.txt: OK\n", "expr-1085.txt: OK\n", "expr-1086.txt: OK\n", "expr-1087.txt: OK\n", "expr-1088.txt: OK\n", "expr-1089.txt: OK\n", "expr-1090.txt: OK\n", "expr-1091.txt: OK\n", "expr-1092.txt: OK\n", "expr-1093.txt: OK\n", "expr-1094.txt: OK\n", "expr-1095.txt: OK\n", "expr-1096.txt: OK\n", "expr-1097.txt: OK\n", "expr-1098.txt: OK\n", "expr-1099.txt: OK\n", "expr-1100.txt: OK\n", "outcomes.txt: OK\n" ] } ], "source": [ "md5sum -c MD5SUM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data munging\n", "\n", "For this part - click on the `Kernel` menu item and select `Change Kernel | R`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Create a `data.frame` called `expr` where each row represents a gene, and each column represents a subject. Give meaningful row names of the form `geneX` (where X goes from 1:20000) and `ptX` (where X goes from 1001:1100, using the PID in the filename).\n", "- When loading the files, make sure you also handle missing data indicated by the string 'nan' correctly." ] }, { "cell_type": "code", "execution_count": 186, "metadata": {}, "outputs": [], "source": [ "suppressPackageStartupMessages(library(tidyverse))" ] }, { "cell_type": "code", "execution_count": 187, "metadata": {}, "outputs": [], "source": [ "files <- paste('data/', 'expr-', 1001:1100, '.txt', sep='')" ] }, { "cell_type": "code", "execution_count": 188, "metadata": {}, "outputs": [], "source": [ "data <- lapply(files, read.table)" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [], "source": [ "expr <- bind_cols(data)" ] }, { "cell_type": "code", "execution_count": 190, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 20000
    2. \n", "\t
  2. 100
    3. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 20000\n", "\\item 100\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 20000\n", "2. 100\n", "\n", "\n" ], "text/plain": [ "[1] 20000 100" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dim(expr)" ] }, { "cell_type": "code", "execution_count": 191, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rownames(expr) = paste('gene', 1:nrow(expr), sep='')" ] }, { "cell_type": "code", "execution_count": 192, "metadata": { "collapsed": true }, "outputs": [], "source": [ "colnames(expr) = paste('pt', 1001:1100, sep='')" ] }, { "cell_type": "code", "execution_count": 193, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
pt1001pt1002pt1003pt1004pt1005pt1006pt1007pt1008pt1009pt1010⋯pt1091pt1092pt1093pt1094pt1095pt1096pt1097pt1098pt1099pt1100
gene1 8.39 3.60 12.55 -2.15 -8.04-10.42-4.39 -1.82 7.38 5.70 ⋯ -6.29 -3.52 -4.81 0.37-2.22 -4.14 7.40 -1.34 -3.34 5.17
gene2 0.63 6.18 8.35 9.87 -4.97 0.86-3.28 -4.73 -14.21-7.32 ⋯ 4.05 2.45 -1.42 2.29-3.84 -6.12 -2.89 -5.94 15.15 12.76
gene3 1.40 9.47 2.42 -1.58 -8.86 -2.05-1.31 -1.64 9.68-3.50 ⋯ -4.38 0.96 5.34 7.45-6.63 4.84 -3.58 -8.33 3.76 -7.13
gene4 1.44 -2.81 4.60 1.22 4.14 10.04-0.99 12.57 -7.22-7.75 ⋯ -5.44 3.74 -6.08 1.96-2.47 0.66 7.34 -7.44 1.48 6.83
gene5-0.38 0.46 0.41 -12.49-11.06 3.19-4.40 3.55 -9.26 1.63 ⋯ 1.80 4.17 -7.01 0.80 2.02 0.30 3.85 -7.74 9.59 6.05
gene6-8.52 5.84 1.56 -1.52 4.73 5.90-5.81 -0.16 -0.62 0.91 ⋯ 9.14 -9.82 7.51 -11.27-2.70 -0.78 3.66 10.58 7.58 14.13
\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n", " & pt1001 & pt1002 & pt1003 & pt1004 & pt1005 & pt1006 & pt1007 & pt1008 & pt1009 & pt1010 & ⋯ & pt1091 & pt1092 & pt1093 & pt1094 & pt1095 & pt1096 & pt1097 & pt1098 & pt1099 & pt1100\\\\\n", "\\hline\n", "\tgene1 & 8.39 & 3.60 & 12.55 & -2.15 & -8.04 & -10.42 & -4.39 & -1.82 & 7.38 & 5.70 & ⋯ & -6.29 & -3.52 & -4.81 & 0.37 & -2.22 & -4.14 & 7.40 & -1.34 & -3.34 & 5.17 \\\\\n", "\tgene2 & 0.63 & 6.18 & 8.35 & 9.87 & -4.97 & 0.86 & -3.28 & -4.73 & -14.21 & -7.32 & ⋯ & 4.05 & 2.45 & -1.42 & 2.29 & -3.84 & -6.12 & -2.89 & -5.94 & 15.15 & 12.76 \\\\\n", "\tgene3 & 1.40 & 9.47 & 2.42 & -1.58 & -8.86 & -2.05 & -1.31 & -1.64 & 9.68 & -3.50 & ⋯ & -4.38 & 0.96 & 5.34 & 7.45 & -6.63 & 4.84 & -3.58 & -8.33 & 3.76 & -7.13 \\\\\n", "\tgene4 & 1.44 & -2.81 & 4.60 & 1.22 & 4.14 & 10.04 & -0.99 & 12.57 & -7.22 & -7.75 & ⋯ & -5.44 & 3.74 & -6.08 & 1.96 & -2.47 & 0.66 & 7.34 & -7.44 & 1.48 & 6.83 \\\\\n", "\tgene5 & -0.38 & 0.46 & 0.41 & -12.49 & -11.06 & 3.19 & -4.40 & 3.55 & -9.26 & 1.63 & ⋯ & 1.80 & 4.17 & -7.01 & 0.80 & 2.02 & 0.30 & 3.85 & -7.74 & 9.59 & 6.05 \\\\\n", "\tgene6 & -8.52 & 5.84 & 1.56 & -1.52 & 4.73 & 5.90 & -5.81 & -0.16 & -0.62 & 0.91 & ⋯ & 9.14 & -9.82 & 7.51 & -11.27 & -2.70 & -0.78 & 3.66 & 10.58 & 7.58 & 14.13 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | pt1001 | pt1002 | pt1003 | pt1004 | pt1005 | pt1006 | pt1007 | pt1008 | pt1009 | pt1010 | ⋯ | pt1091 | pt1092 | pt1093 | pt1094 | pt1095 | pt1096 | pt1097 | pt1098 | pt1099 | pt1100 | \n", "|---|---|---|---|---|---|\n", "| gene1 | 8.39 | 3.60 | 12.55 | -2.15 | -8.04 | -10.42 | -4.39 | -1.82 | 7.38 | 5.70 | ⋯ | -6.29 | -3.52 | -4.81 | 0.37 | -2.22 | -4.14 | 7.40 | -1.34 | -3.34 | 5.17 | \n", "| gene2 | 0.63 | 6.18 | 8.35 | 9.87 | -4.97 | 0.86 | -3.28 | -4.73 | -14.21 | -7.32 | ⋯ | 4.05 | 2.45 | -1.42 | 2.29 | -3.84 | -6.12 | -2.89 | -5.94 | 15.15 | 12.76 | \n", "| gene3 | 1.40 | 9.47 | 2.42 | -1.58 | -8.86 | -2.05 | -1.31 | -1.64 | 9.68 | -3.50 | ⋯ | -4.38 | 0.96 | 5.34 | 7.45 | -6.63 | 4.84 | -3.58 | -8.33 | 3.76 | -7.13 | \n", "| gene4 | 1.44 | -2.81 | 4.60 | 1.22 | 4.14 | 10.04 | -0.99 | 12.57 | -7.22 | -7.75 | ⋯ | -5.44 | 3.74 | -6.08 | 1.96 | -2.47 | 0.66 | 7.34 | -7.44 | 1.48 | 6.83 | \n", "| gene5 | -0.38 | 0.46 | 0.41 | -12.49 | -11.06 | 3.19 | -4.40 | 3.55 | -9.26 | 1.63 | ⋯ | 1.80 | 4.17 | -7.01 | 0.80 | 2.02 | 0.30 | 3.85 | -7.74 | 9.59 | 6.05 | \n", "| gene6 | -8.52 | 5.84 | 1.56 | -1.52 | 4.73 | 5.90 | -5.81 | -0.16 | -0.62 | 0.91 | ⋯ | 9.14 | -9.82 | 7.51 | -11.27 | -2.70 | -0.78 | 3.66 | 10.58 | 7.58 | 14.13 | \n", "\n", "\n" ], "text/plain": [ " pt1001 pt1002 pt1003 pt1004 pt1005 pt1006 pt1007 pt1008 pt1009 pt1010 ⋯\n", "gene1 8.39 3.60 12.55 -2.15 -8.04 -10.42 -4.39 -1.82 7.38 5.70 ⋯\n", "gene2 0.63 6.18 8.35 9.87 -4.97 0.86 -3.28 -4.73 -14.21 -7.32 ⋯\n", "gene3 1.40 9.47 2.42 -1.58 -8.86 -2.05 -1.31 -1.64 9.68 -3.50 ⋯\n", "gene4 1.44 -2.81 4.60 1.22 4.14 10.04 -0.99 12.57 -7.22 -7.75 ⋯\n", "gene5 -0.38 0.46 0.41 -12.49 -11.06 3.19 -4.40 3.55 -9.26 1.63 ⋯\n", "gene6 -8.52 5.84 1.56 -1.52 4.73 5.90 -5.81 -0.16 -0.62 0.91 ⋯\n", " pt1091 pt1092 pt1093 pt1094 pt1095 pt1096 pt1097 pt1098 pt1099 pt1100\n", "gene1 -6.29 -3.52 -4.81 0.37 -2.22 -4.14 7.40 -1.34 -3.34 5.17 \n", "gene2 4.05 2.45 -1.42 2.29 -3.84 -6.12 -2.89 -5.94 15.15 12.76 \n", "gene3 -4.38 0.96 5.34 7.45 -6.63 4.84 -3.58 -8.33 3.76 -7.13 \n", "gene4 -5.44 3.74 -6.08 1.96 -2.47 0.66 7.34 -7.44 1.48 6.83 \n", "gene5 1.80 4.17 -7.01 0.80 2.02 0.30 3.85 -7.74 9.59 6.05 \n", "gene6 9.14 -9.82 7.51 -11.27 -2.70 -0.78 3.66 10.58 7.58 14.13 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(expr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Remove any gene(row) whose values are all zero. How many genes were dropped?" ] }, { "cell_type": "code", "execution_count": 194, "metadata": { "collapsed": true }, "outputs": [], "source": [ "idx <- rowSums(abs(expr)) == 0\n", "expr <- expr[!idx,]" ] }, { "cell_type": "code", "execution_count": 195, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 19998
    2. \n", "\t
  2. 100
    3. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 19998\n", "\\item 100\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 19998\n", "2. 100\n", "\n", "\n" ], "text/plain": [ "[1] 19998 100" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dim(expr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Remove any genes (row) with missing data. How many genes were dropped?" ] }, { "cell_type": "code", "execution_count": 196, "metadata": {}, "outputs": [], "source": [ "expr <- expr %>% drop_na()" ] }, { "cell_type": "code", "execution_count": 197, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 19993
    2. \n", "\t
  2. 100
    3. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 19993\n", "\\item 100\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 19993\n", "2. 100\n", "\n", "\n" ], "text/plain": [ "[1] 19993 100" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dim(expr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Scale all genes to have zero mean and unit standard deviation" ] }, { "cell_type": "code", "execution_count": 198, "metadata": { "collapsed": true }, "outputs": [], "source": [ "expr <- t(scale(t(expr)))" ] }, { "cell_type": "code", "execution_count": 199, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\t
gene1
\n", "\t\t
1
\n", "\t
gene2
\n", "\t\t
1
\n", "\t
gene3
\n", "\t\t
1
\n", "
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[gene1] 1\n", "\\item[gene2] 1\n", "\\item[gene3] 1\n", "\\end{description*}\n" ], "text/markdown": [ "gene1\n", ": 1gene2\n", ": 1gene3\n", ": 1\n", "\n" ], "text/plain": [ "gene1 gene2 gene3 \n", " 1 1 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(round(apply(expr, 1, sd), 2), 3)" ] }, { "cell_type": "code", "execution_count": 200, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\t
gene1
\n", "\t\t
0
\n", "\t
gene2
\n", "\t\t
0
\n", "\t
gene3
\n", "\t\t
0
\n", "
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[gene1] 0\n", "\\item[gene2] 0\n", "\\item[gene3] 0\n", "\\end{description*}\n" ], "text/markdown": [ "gene1\n", ": 0gene2\n", ": 0gene3\n", ": 0\n", "\n" ], "text/plain": [ "gene1 gene2 gene3 \n", " 0 0 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(round(apply(expr, 1, mean), 2), 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Create a data frame `group` with two columns `PID` and `Group` that indicates whether each patient is a case or control. Use the information from the `cases.txt` and `controls.txt` files." ] }, { "cell_type": "code", "execution_count": 201, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cases <- read.table('data/cases.txt')\n", "ctrls <- read.table('data/controls.txt')" ] }, { "cell_type": "code", "execution_count": 202, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n1 <- nrow(cases)\n", "n2 <- nrow(ctrls)" ] }, { "cell_type": "code", "execution_count": 203, "metadata": { "collapsed": true }, "outputs": [], "source": [ "grp <- c(rep('case', n1), rep('ctrl', n2))" ] }, { "cell_type": "code", "execution_count": 204, "metadata": { "collapsed": true }, "outputs": [], "source": [ "group <- data.frame(PID=bind_rows(cases, ctrls), grp)" ] }, { "cell_type": "code", "execution_count": 205, "metadata": { "collapsed": true }, "outputs": [], "source": [ "colnames(group) <- c('PID', 'Group')" ] }, { "cell_type": "code", "execution_count": 206, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
PIDGroup
1088case
1022case
1064case
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " PID & Group\\\\\n", "\\hline\n", "\t 1088 & case\\\\\n", "\t 1022 & case\\\\\n", "\t 1064 & case\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "PID | Group | \n", "|---|---|---|\n", "| 1088 | case | \n", "| 1022 | case | \n", "| 1064 | case | \n", "\n", "\n" ], "text/plain": [ " PID Group\n", "1 1088 case \n", "2 1022 case \n", "3 1064 case " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(group, 3)" ] }, { "cell_type": "code", "execution_count": 207, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
PIDGroup
981096ctrl
991072ctrl
1001038ctrl
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " & PID & Group\\\\\n", "\\hline\n", "\t98 & 1096 & ctrl\\\\\n", "\t99 & 1072 & ctrl\\\\\n", "\t100 & 1038 & ctrl\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | PID | Group | \n", "|---|---|---|\n", "| 98 | 1096 | ctrl | \n", "| 99 | 1072 | ctrl | \n", "| 100 | 1038 | ctrl | \n", "\n", "\n" ], "text/plain": [ " PID Group\n", "98 1096 ctrl \n", "99 1072 ctrl \n", "100 1038 ctrl " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tail(group, 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Unsupervised Learning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Use classic MDS (multi-dimensional scaling) to embed the data in 2D and make a scatter plot with `ggplot2`" ] }, { "cell_type": "code", "execution_count": 208, "metadata": {}, "outputs": [], "source": [ "mds <- as.data.frame(cmdscale(dist(t(expr)), k=2))" ] }, { "cell_type": "code", "execution_count": 209, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'data.frame':\t100 obs. of 2 variables:\n", " $ V1: num -19.5 21.9 19.5 -22 -21.1 ...\n", " $ V2: num -7.44 13.636 -36.314 5.243 -0.752 ...\n" ] } ], "source": [ "str(mds)" ] }, { "cell_type": "code", "execution_count": 210, "metadata": { "collapsed": true }, "outputs": [], "source": [ "options(repr.plot.width=4, repr.plot.height=3)" ] }, { "cell_type": "code", "execution_count": 211, "metadata": {}, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAFoCAYAAACPNyggAAAEDWlDQ1BJQ0MgUHJvZmlsZQAA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\nQAREQAREIDcE/g+Optdee6HmywAAAABJRU5ErkJggg==", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ggplot(mds, aes(x=V1, y=V2)) + geom_point()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Use a t-test to find all genes differentially expressed in the cases and controls group with a False Discovery Rate (FDR) of 0.05 using the Benjamini–Hochberg (BH) procedure. Save the filtered genes in a `data.frame` called `hits`." ] }, { "cell_type": "code", "execution_count": 212, "metadata": { "collapsed": true }, "outputs": [], "source": [ "suppressPackageStartupMessages(library(genefilter))" ] }, { "cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
PIDGroup
1088case
1022case
1064case
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " PID & Group\\\\\n", "\\hline\n", "\t 1088 & case\\\\\n", "\t 1022 & case\\\\\n", "\t 1064 & case\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "PID | Group | \n", "|---|---|---|\n", "| 1088 | case | \n", "| 1022 | case | \n", "| 1064 | case | \n", "\n", "\n" ], "text/plain": [ " PID Group\n", "1 1088 case \n", "2 1022 case \n", "3 1064 case " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(group, 3)" ] }, { "cell_type": "code", "execution_count": 214, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
PIDGroup
1001case
1002ctrl
1003ctrl
1004case
1005case
1006case
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " PID & Group\\\\\n", "\\hline\n", "\t 1001 & case\\\\\n", "\t 1002 & ctrl\\\\\n", "\t 1003 & ctrl\\\\\n", "\t 1004 & case\\\\\n", "\t 1005 & case\\\\\n", "\t 1006 & case\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "PID | Group | \n", "|---|---|---|---|---|---|\n", "| 1001 | case | \n", "| 1002 | ctrl | \n", "| 1003 | ctrl | \n", "| 1004 | case | \n", "| 1005 | case | \n", "| 1006 | case | \n", "\n", "\n" ], "text/plain": [ " PID Group\n", "1 1001 case \n", "2 1002 ctrl \n", "3 1003 ctrl \n", "4 1004 case \n", "5 1005 case \n", "6 1006 case " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "group <- group %>% arrange(PID)\n", "head(group)" ] }, { "cell_type": "code", "execution_count": 215, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 19993
    2. \n", "\t
  2. 100
    3. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 19993\n", "\\item 100\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 19993\n", "2. 100\n", "\n", "\n" ], "text/plain": [ "[1] 19993 100" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dim(expr)" ] }, { "cell_type": "code", "execution_count": 216, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p.values <- rowttests(expr, fac = group$Group)$p.value" ] }, { "cell_type": "code", "execution_count": 217, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 0.810340264500016
    2. \n", "\t
  2. 0.0204475853560852
    3. \n", "\t
  3. 0.233159743584127
    4. \n", "\t
  4. 0.412635280852636
    5. \n", "\t
  5. 0.165387577174332
    6. \n", "\t
  6. 0.933613549615968
    7. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 0.810340264500016\n", "\\item 0.0204475853560852\n", "\\item 0.233159743584127\n", "\\item 0.412635280852636\n", "\\item 0.165387577174332\n", "\\item 0.933613549615968\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 0.810340264500016\n", "2. 0.0204475853560852\n", "3. 0.233159743584127\n", "4. 0.412635280852636\n", "5. 0.165387577174332\n", "6. 0.933613549615968\n", "\n", "\n" ], "text/plain": [ "[1] 0.81034026 0.02044759 0.23315974 0.41263528 0.16538758 0.93361355" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(p.values)" ] }, { "cell_type": "code", "execution_count": 218, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p.bh <- p.adjust(p.values, method='BH')" ] }, { "cell_type": "code", "execution_count": 219, "metadata": { "collapsed": true }, "outputs": [], "source": [ "idx <- p.bh < 0.01" ] }, { "cell_type": "code", "execution_count": 220, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 'gene160'
    2. \n", "\t
  2. 'gene393'
    3. \n", "\t
  3. 'gene412'
    4. \n", "\t
  4. 'gene439'
    5. \n", "\t
  5. 'gene452'
    6. \n", "\t
  6. 'gene490'
    7. \n", "\t
  7. 'gene506'
    8. \n", "\t
  8. 'gene601'
    9. \n", "\t
  9. 'gene683'
    10. \n", "\t
  10. 'gene748'
    11. \n", "\t
  11. 'gene766'
    12. \n", "\t
  12. 'gene771'
    13. \n", "\t
  13. 'gene808'
    14. \n", "\t
  14. 'gene830'
    15. \n", "\t
  15. 'gene834'
    16. \n", "\t
  16. 'gene862'
    17. \n", "\t
  17. 'gene888'
    18. \n", "\t
  18. 'gene914'
    19. \n", "\t
  19. 'gene1013'
    20. \n", "\t
  20. 'gene1035'
    21. \n", "\t
  21. 'gene1057'
    22. \n", "\t
  22. 'gene1082'
    23. \n", "\t
  23. 'gene1138'
    24. \n", "\t
  24. 'gene1172'
    25. \n", "\t
  25. 'gene1194'
    26. \n", "\t
  26. 'gene1322'
    27. \n", "\t
  27. 'gene1338'
    28. \n", "\t
  28. 'gene1462'
    29. \n", "\t
  29. 'gene1563'
    30. \n", "\t
  30. 'gene1582'
    31. \n", "\t
  31. 'gene1594'
    32. \n", "\t
  32. 'gene1775'
    33. \n", "\t
  33. 'gene1844'
    34. \n", "\t
  34. 'gene1857'
    35. \n", "\t
  35. 'gene1868'
    36. \n", "\t
  36. 'gene1910'
    37. \n", "\t
  37. 'gene1950'
    38. \n", "\t
  38. 'gene1957'
    39. \n", "\t
  39. 'gene2094'
    40. \n", "\t
  40. 'gene2106'
    41. \n", "\t
  41. 'gene2119'
    42. \n", "\t
  42. 'gene2148'
    43. \n", "\t
  43. 'gene2233'
    44. \n", "\t
  44. 'gene2253'
    45. \n", "\t
  45. 'gene2300'
    46. \n", "\t
  46. 'gene2358'
    47. \n", "\t
  47. 'gene2407'
    48. \n", "\t
  48. 'gene2450'
    49. \n", "\t
  49. 'gene2454'
    50. \n", "\t
  50. 'gene2461'
    51. \n", "\t
  51. 'gene2508'
    52. \n", "\t
  52. 'gene2548'
    53. \n", "\t
  53. 'gene2560'
    54. \n", "\t
  54. 'gene2608'
    55. \n", "\t
  55. 'gene2630'
    56. \n", "\t
  56. 'gene2642'
    57. \n", "\t
  57. 'gene2671'
    58. \n", "\t
  58. 'gene2736'
    59. \n", "\t
  59. 'gene2772'
    60. \n", "\t
  60. 'gene2818'
    61. \n", "\t
  61. 'gene2822'
    62. \n", "\t
  62. 'gene2941'
    63. \n", "\t
  63. 'gene2971'
    64. \n", "\t
  64. 'gene3012'
    65. \n", "\t
  65. 'gene3072'
    66. \n", "\t
  66. 'gene3116'
    67. \n", "\t
  67. 'gene3215'
    68. \n", "\t
  68. 'gene3262'
    69. \n", "\t
  69. 'gene3269'
    70. \n", "\t
  70. 'gene3306'
    71. \n", "\t
  71. 'gene3403'
    72. \n", "\t
  72. 'gene3455'
    73. \n", "\t
  73. 'gene3490'
    74. \n", "\t
  74. 'gene3492'
    75. \n", "\t
  75. 'gene3546'
    76. \n", "\t
  76. 'gene3574'
    77. \n", "\t
  77. 'gene3647'
    78. \n", "\t
  78. 'gene3693'
    79. \n", "\t
  79. 'gene3731'
    80. \n", "\t
  80. 'gene3748'
    81. \n", "\t
  81. 'gene3769'
    82. \n", "\t
  82. 'gene3802'
    83. \n", "\t
  83. 'gene3964'
    84. \n", "\t
  84. 'gene3971'
    85. \n", "\t
  85. 'gene4007'
    86. \n", "\t
  86. 'gene4028'
    87. \n", "\t
  87. 'gene4059'
    88. \n", "\t
  88. 'gene4097'
    89. \n", "\t
  89. 'gene4117'
    90. \n", "\t
  90. 'gene4233'
    91. \n", "\t
  91. 'gene4293'
    92. \n", "\t
  92. 'gene4427'
    93. \n", "\t
  93. 'gene4438'
    94. \n", "\t
  94. 'gene4501'
    95. \n", "\t
  95. 'gene4527'
    96. \n", "\t
  96. 'gene4634'
    97. \n", "\t
  97. 'gene4652'
    98. \n", "\t
  98. 'gene4723'
    99. \n", "\t
  99. 'gene4846'
    100. \n", "\t
  100. 'gene4890'
    101. \n", "\t
  101. 'gene5212'
    102. \n", "\t
  102. 'gene5233'
    103. \n", "\t
  103. 'gene5272'
    104. \n", "\t
  104. 'gene5375'
    105. \n", "\t
  105. 'gene5478'
    106. \n", "\t
  106. 'gene5520'
    107. \n", "\t
  107. 'gene5664'
    108. \n", "\t
  108. 'gene5770'
    109. \n", "\t
  109. 'gene5828'
    110. \n", "\t
  110. 'gene5936'
    111. \n", "\t
  111. 'gene5975'
    112. \n", "\t
  112. 'gene5987'
    113. \n", "\t
  113. 'gene6025'
    114. \n", "\t
  114. 'gene6029'
    115. \n", "\t
  115. 'gene6096'
    116. \n", "\t
  116. 'gene6097'
    117. \n", "\t
  117. 'gene6102'
    118. \n", "\t
  118. 'gene6104'
    119. \n", "\t
  119. 'gene6210'
    120. \n", "\t
  120. 'gene6412'
    121. \n", "\t
  121. 'gene6478'
    122. \n", "\t
  122. 'gene6534'
    123. \n", "\t
  123. 'gene6650'
    124. \n", "\t
  124. 'gene6672'
    125. \n", "\t
  125. 'gene6678'
    126. \n", "\t
  126. 'gene6756'
    127. \n", "\t
  127. 'gene6779'
    128. \n", "\t
  128. 'gene6908'
    129. \n", "\t
  129. 'gene7116'
    130. \n", "\t
  130. 'gene7123'
    131. \n", "\t
  131. 'gene7215'
    132. \n", "\t
  132. 'gene7220'
    133. \n", "\t
  133. 'gene7250'
    134. \n", "\t
  134. 'gene7252'
    135. \n", "\t
  135. 'gene7363'
    136. \n", "\t
  136. 'gene7374'
    137. \n", "\t
  137. 'gene7496'
    138. \n", "\t
  138. 'gene7568'
    139. \n", "\t
  139. 'gene7578'
    140. \n", "\t
  140. 'gene7648'
    141. \n", "\t
  141. 'gene7692'
    142. \n", "\t
  142. 'gene7704'
    143. \n", "\t
  143. 'gene7713'
    144. \n", "\t
  144. 'gene7766'
    145. \n", "\t
  145. 'gene7783'
    146. \n", "\t
  146. 'gene7873'
    147. \n", "\t
  147. 'gene8102'
    148. \n", "\t
  148. 'gene8163'
    149. \n", "\t
  149. 'gene8304'
    150. \n", "\t
  150. 'gene8559'
    151. \n", "\t
  151. 'gene8758'
    152. \n", "\t
  152. 'gene8843'
    153. \n", "\t
  153. 'gene8906'
    154. \n", "\t
  154. 'gene8924'
    155. \n", "\t
  155. 'gene8993'
    156. \n", "\t
  156. 'gene8995'
    157. \n", "\t
  157. 'gene9104'
    158. \n", "\t
  158. 'gene9192'
    159. \n", "\t
  159. 'gene9203'
    160. \n", "\t
  160. 'gene9227'
    161. \n", "\t
  161. 'gene9326'
    162. \n", "\t
  162. 'gene9403'
    163. \n", "\t
  163. 'gene9506'
    164. \n", "\t
  164. 'gene9516'
    165. \n", "\t
  165. 'gene9652'
    166. \n", "\t
  166. 'gene9653'
    167. \n", "\t
  167. 'gene9679'
    168. \n", "\t
  168. 'gene9725'
    169. \n", "\t
  169. 'gene9741'
    170. \n", "\t
  170. 'gene9817'
    171. \n", "\t
  171. 'gene9834'
    172. \n", "\t
  172. 'gene9848'
    173. \n", "\t
  173. 'gene9857'
    174. \n", "\t
  174. 'gene9906'
    175. \n", "\t
  175. 'gene10003'
    176. \n", "\t
  176. 'gene10057'
    177. \n", "\t
  177. 'gene10105'
    178. \n", "\t
  178. 'gene10119'
    179. \n", "\t
  179. 'gene10190'
    180. \n", "\t
  180. 'gene10210'
    181. \n", "\t
  181. 'gene10211'
    182. \n", "\t
  182. 'gene10283'
    183. \n", "\t
  183. 'gene10326'
    184. \n", "\t
  184. 'gene10437'
    185. \n", "\t
  185. 'gene10446'
    186. \n", "\t
  186. 'gene10529'
    187. \n", "\t
  187. 'gene10587'
    188. \n", "\t
  188. 'gene10639'
    189. \n", "\t
  189. 'gene10657'
    190. \n", "\t
  190. 'gene10667'
    191. \n", "\t
  191. 'gene10674'
    192. \n", "\t
  192. 'gene10682'
    193. \n", "\t
  193. 'gene10696'
    194. \n", "\t
  194. 'gene10706'
    195. \n", "\t
  195. 'gene10737'
    196. \n", "\t
  196. 'gene10778'
    197. \n", "\t
  197. 'gene10812'
    198. \n", "\t
  198. 'gene10876'
    199. \n", "\t
  199. 'gene10918'
    200. \n", "\t
  200. 'gene10949'
    201. \n", "\t
  201. 'gene10956'
    202. \n", "\t
  202. 'gene11212'
    203. \n", "\t
  203. 'gene11218'
    204. \n", "\t
  204. 'gene11295'
    205. \n", "\t
  205. 'gene11357'
    206. \n", "\t
  206. 'gene11360'
    207. \n", "\t
  207. 'gene11366'
    208. \n", "\t
  208. 'gene11384'
    209. \n", "\t
  209. 'gene11429'
    210. \n", "\t
  210. 'gene11433'
    211. \n", "\t
  211. 'gene11478'
    212. \n", "\t
  212. 'gene11527'
    213. \n", "\t
  213. 'gene11553'
    214. \n", "\t
  214. 'gene11564'
    215. \n", "\t
  215. 'gene11631'
    216. \n", "\t
  216. 'gene11824'
    217. \n", "\t
  217. 'gene11931'
    218. \n", "\t
  218. 'gene12102'
    219. \n", "\t
  219. 'gene12211'
    220. \n", "\t
  220. 'gene12253'
    221. \n", "\t
  221. 'gene12354'
    222. \n", "\t
  222. 'gene12370'
    223. \n", "\t
  223. 'gene12377'
    224. \n", "\t
  224. 'gene12425'
    225. \n", "\t
  225. 'gene12508'
    226. \n", "\t
  226. 'gene12547'
    227. \n", "\t
  227. 'gene12559'
    228. \n", "\t
  228. 'gene12567'
    229. \n", "\t
  229. 'gene12674'
    230. \n", "\t
  230. 'gene12675'
    231. \n", "\t
  231. 'gene12694'
    232. \n", "\t
  232. 'gene12704'
    233. \n", "\t
  233. 'gene12723'
    234. \n", "\t
  234. 'gene12769'
    235. \n", "\t
  235. 'gene12777'
    236. \n", "\t
  236. 'gene12809'
    237. \n", "\t
  237. 'gene12819'
    238. \n", "\t
  238. 'gene12825'
    239. \n", "\t
  239. 'gene12864'
    240. \n", "\t
  240. 'gene12902'
    241. \n", "\t
  241. 'gene12927'
    242. \n", "\t
  242. 'gene12947'
    243. \n", "\t
  243. 'gene13148'
    244. \n", "\t
  244. 'gene13170'
    245. \n", "\t
  245. 'gene13179'
    246. \n", "\t
  246. 'gene13233'
    247. \n", "\t
  247. 'gene13381'
    248. \n", "\t
  248. 'gene13461'
    249. \n", "\t
  249. 'gene13567'
    250. \n", "\t
  250. 'gene13607'
    251. \n", "\t
  251. 'gene13649'
    252. \n", "\t
  252. 'gene13711'
    253. \n", "\t
  253. 'gene13723'
    254. \n", "\t
  254. 'gene13742'
    255. \n", "\t
  255. 'gene13780'
    256. \n", "\t
  256. 'gene13823'
    257. \n", "\t
  257. 'gene13832'
    258. \n", "\t
  258. 'gene13872'
    259. \n", "\t
  259. 'gene14038'
    260. \n", "\t
  260. 'gene14154'
    261. \n", "\t
  261. 'gene14161'
    262. \n", "\t
  262. 'gene14178'
    263. \n", "\t
  263. 'gene14434'
    264. \n", "\t
  264. 'gene14443'
    265. \n", "\t
  265. 'gene14508'
    266. \n", "\t
  266. 'gene14539'
    267. \n", "\t
  267. 'gene14560'
    268. \n", "\t
  268. 'gene14590'
    269. \n", "\t
  269. 'gene14604'
    270. \n", "\t
  270. 'gene14623'
    271. \n", "\t
  271. 'gene14656'
    272. \n", "\t
  272. 'gene14660'
    273. \n", "\t
  273. 'gene14782'
    274. \n", "\t
  274. 'gene14784'
    275. \n", "\t
  275. 'gene14808'
    276. \n", "\t
  276. 'gene14889'
    277. \n", "\t
  277. 'gene14924'
    278. \n", "\t
  278. 'gene15062'
    279. \n", "\t
  279. 'gene15069'
    280. \n", "\t
  280. 'gene15083'
    281. \n", "\t
  281. 'gene15087'
    282. \n", "\t
  282. 'gene15101'
    283. \n", "\t
  283. 'gene15128'
    284. \n", "\t
  284. 'gene15173'
    285. \n", "\t
  285. 'gene15177'
    286. \n", "\t
  286. 'gene15180'
    287. \n", "\t
  287. 'gene15232'
    288. \n", "\t
  288. 'gene15263'
    289. \n", "\t
  289. 'gene15361'
    290. \n", "\t
  290. 'gene15415'
    291. \n", "\t
  291. 'gene15454'
    292. \n", "\t
  292. 'gene15523'
    293. \n", "\t
  293. 'gene15530'
    294. \n", "\t
  294. 'gene15592'
    295. \n", "\t
  295. 'gene15596'
    296. \n", "\t
  296. 'gene15619'
    297. \n", "\t
  297. 'gene15709'
    298. \n", "\t
  298. 'gene15714'
    299. \n", "\t
  299. 'gene15725'
    300. \n", "\t
  300. 'gene15743'
    301. \n", "\t
  301. 'gene15892'
    302. \n", "\t
  302. 'gene16056'
    303. \n", "\t
  303. 'gene16088'
    304. \n", "\t
  304. 'gene16095'
    305. \n", "\t
  305. 'gene16122'
    306. \n", "\t
  306. 'gene16164'
    307. \n", "\t
  307. 'gene16197'
    308. \n", "\t
  308. 'gene16213'
    309. \n", "\t
  309. 'gene16240'
    310. \n", "\t
  310. 'gene16256'
    311. \n", "\t
  311. 'gene16482'
    312. \n", "\t
  312. 'gene16647'
    313. \n", "\t
  313. 'gene16701'
    314. \n", "\t
  314. 'gene16705'
    315. \n", "\t
  315. 'gene16788'
    316. \n", "\t
  316. 'gene16810'
    317. \n", "\t
  317. 'gene16830'
    318. \n", "\t
  318. 'gene16870'
    319. \n", "\t
  319. 'gene16967'
    320. \n", "\t
  320. 'gene16978'
    321. \n", "\t
  321. 'gene16987'
    322. \n", "\t
  322. 'gene17113'
    323. \n", "\t
  323. 'gene17129'
    324. \n", "\t
  324. 'gene17146'
    325. \n", "\t
  325. 'gene17224'
    326. \n", "\t
  326. 'gene17233'
    327. \n", "\t
  327. 'gene17396'
    328. \n", "\t
  328. 'gene17432'
    329. \n", "\t
  329. 'gene17562'
    330. \n", "\t
  330. 'gene17582'
    331. \n", "\t
  331. 'gene17605'
    332. \n", "\t
  332. 'gene17625'
    333. \n", "\t
  333. 'gene17862'
    334. \n", "\t
  334. 'gene17910'
    335. \n", "\t
  335. 'gene18009'
    336. \n", "\t
  336. 'gene18055'
    337. \n", "\t
  337. 'gene18169'
    338. \n", "\t
  338. 'gene18206'
    339. \n", "\t
  339. 'gene18207'
    340. \n", "\t
  340. 'gene18316'
    341. \n", "\t
  341. 'gene18329'
    342. \n", "\t
  342. 'gene18341'
    343. \n", "\t
  343. 'gene18375'
    344. \n", "\t
  344. 'gene18410'
    345. \n", "\t
  345. 'gene18453'
    346. \n", "\t
  346. 'gene18487'
    347. \n", "\t
  347. 'gene18509'
    348. \n", "\t
  348. 'gene18530'
    349. \n", "\t
  349. 'gene18722'
    350. \n", "\t
  350. 'gene18811'
    351. \n", "\t
  351. 'gene18847'
    352. \n", "\t
  352. 'gene18905'
    353. \n", "\t
  353. 'gene18957'
    354. \n", "\t
  354. 'gene19008'
    355. \n", "\t
  355. 'gene19066'
    356. \n", "\t
  356. 'gene19080'
    357. \n", "\t
  357. 'gene19086'
    358. \n", "\t
  358. 'gene19137'
    359. \n", "\t
  359. 'gene19216'
    360. \n", "\t
  360. 'gene19251'
    361. \n", "\t
  361. 'gene19287'
    362. \n", "\t
  362. 'gene19564'
    363. \n", "\t
  363. 'gene19666'
    364. \n", "\t
  364. 'gene19701'
    365. \n", "\t
  365. 'gene19757'
    366. \n", "\t
  366. 'gene19771'
    367. \n", "\t
  367. 'gene19795'
    368. \n", "\t
  368. 'gene19930'
    369. \n", "\t
  369. 'gene19978'
    370. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'gene160'\n", "\\item 'gene393'\n", "\\item 'gene412'\n", "\\item 'gene439'\n", "\\item 'gene452'\n", "\\item 'gene490'\n", "\\item 'gene506'\n", "\\item 'gene601'\n", "\\item 'gene683'\n", "\\item 'gene748'\n", "\\item 'gene766'\n", "\\item 'gene771'\n", "\\item 'gene808'\n", "\\item 'gene830'\n", "\\item 'gene834'\n", "\\item 'gene862'\n", "\\item 'gene888'\n", "\\item 'gene914'\n", "\\item 'gene1013'\n", "\\item 'gene1035'\n", "\\item 'gene1057'\n", "\\item 'gene1082'\n", "\\item 'gene1138'\n", "\\item 'gene1172'\n", "\\item 'gene1194'\n", "\\item 'gene1322'\n", "\\item 'gene1338'\n", "\\item 'gene1462'\n", "\\item 'gene1563'\n", "\\item 'gene1582'\n", "\\item 'gene1594'\n", "\\item 'gene1775'\n", "\\item 'gene1844'\n", "\\item 'gene1857'\n", "\\item 'gene1868'\n", "\\item 'gene1910'\n", "\\item 'gene1950'\n", "\\item 'gene1957'\n", "\\item 'gene2094'\n", "\\item 'gene2106'\n", "\\item 'gene2119'\n", "\\item 'gene2148'\n", "\\item 'gene2233'\n", "\\item 'gene2253'\n", "\\item 'gene2300'\n", "\\item 'gene2358'\n", "\\item 'gene2407'\n", "\\item 'gene2450'\n", "\\item 'gene2454'\n", "\\item 'gene2461'\n", "\\item 'gene2508'\n", "\\item 'gene2548'\n", "\\item 'gene2560'\n", "\\item 'gene2608'\n", "\\item 'gene2630'\n", "\\item 'gene2642'\n", "\\item 'gene2671'\n", "\\item 'gene2736'\n", "\\item 'gene2772'\n", "\\item 'gene2818'\n", "\\item 'gene2822'\n", "\\item 'gene2941'\n", "\\item 'gene2971'\n", "\\item 'gene3012'\n", "\\item 'gene3072'\n", "\\item 'gene3116'\n", "\\item 'gene3215'\n", "\\item 'gene3262'\n", "\\item 'gene3269'\n", "\\item 'gene3306'\n", "\\item 'gene3403'\n", "\\item 'gene3455'\n", "\\item 'gene3490'\n", "\\item 'gene3492'\n", "\\item 'gene3546'\n", "\\item 'gene3574'\n", "\\item 'gene3647'\n", "\\item 'gene3693'\n", "\\item 'gene3731'\n", "\\item 'gene3748'\n", "\\item 'gene3769'\n", "\\item 'gene3802'\n", "\\item 'gene3964'\n", "\\item 'gene3971'\n", "\\item 'gene4007'\n", "\\item 'gene4028'\n", "\\item 'gene4059'\n", "\\item 'gene4097'\n", "\\item 'gene4117'\n", "\\item 'gene4233'\n", "\\item 'gene4293'\n", "\\item 'gene4427'\n", "\\item 'gene4438'\n", "\\item 'gene4501'\n", "\\item 'gene4527'\n", "\\item 'gene4634'\n", "\\item 'gene4652'\n", "\\item 'gene4723'\n", "\\item 'gene4846'\n", "\\item 'gene4890'\n", "\\item 'gene5212'\n", "\\item 'gene5233'\n", "\\item 'gene5272'\n", "\\item 'gene5375'\n", "\\item 'gene5478'\n", "\\item 'gene5520'\n", "\\item 'gene5664'\n", "\\item 'gene5770'\n", "\\item 'gene5828'\n", "\\item 'gene5936'\n", "\\item 'gene5975'\n", "\\item 'gene5987'\n", "\\item 'gene6025'\n", "\\item 'gene6029'\n", "\\item 'gene6096'\n", "\\item 'gene6097'\n", "\\item 'gene6102'\n", "\\item 'gene6104'\n", "\\item 'gene6210'\n", "\\item 'gene6412'\n", "\\item 'gene6478'\n", "\\item 'gene6534'\n", "\\item 'gene6650'\n", "\\item 'gene6672'\n", "\\item 'gene6678'\n", "\\item 'gene6756'\n", "\\item 'gene6779'\n", "\\item 'gene6908'\n", "\\item 'gene7116'\n", "\\item 'gene7123'\n", "\\item 'gene7215'\n", "\\item 'gene7220'\n", "\\item 'gene7250'\n", "\\item 'gene7252'\n", "\\item 'gene7363'\n", "\\item 'gene7374'\n", "\\item 'gene7496'\n", "\\item 'gene7568'\n", "\\item 'gene7578'\n", "\\item 'gene7648'\n", "\\item 'gene7692'\n", "\\item 'gene7704'\n", "\\item 'gene7713'\n", "\\item 'gene7766'\n", "\\item 'gene7783'\n", "\\item 'gene7873'\n", "\\item 'gene8102'\n", "\\item 'gene8163'\n", "\\item 'gene8304'\n", "\\item 'gene8559'\n", "\\item 'gene8758'\n", "\\item 'gene8843'\n", "\\item 'gene8906'\n", "\\item 'gene8924'\n", "\\item 'gene8993'\n", "\\item 'gene8995'\n", "\\item 'gene9104'\n", "\\item 'gene9192'\n", "\\item 'gene9203'\n", "\\item 'gene9227'\n", "\\item 'gene9326'\n", "\\item 'gene9403'\n", "\\item 'gene9506'\n", "\\item 'gene9516'\n", "\\item 'gene9652'\n", "\\item 'gene9653'\n", "\\item 'gene9679'\n", "\\item 'gene9725'\n", "\\item 'gene9741'\n", "\\item 'gene9817'\n", "\\item 'gene9834'\n", "\\item 'gene9848'\n", "\\item 'gene9857'\n", "\\item 'gene9906'\n", "\\item 'gene10003'\n", "\\item 'gene10057'\n", "\\item 'gene10105'\n", "\\item 'gene10119'\n", "\\item 'gene10190'\n", "\\item 'gene10210'\n", "\\item 'gene10211'\n", "\\item 'gene10283'\n", "\\item 'gene10326'\n", "\\item 'gene10437'\n", "\\item 'gene10446'\n", "\\item 'gene10529'\n", "\\item 'gene10587'\n", "\\item 'gene10639'\n", "\\item 'gene10657'\n", "\\item 'gene10667'\n", "\\item 'gene10674'\n", "\\item 'gene10682'\n", "\\item 'gene10696'\n", "\\item 'gene10706'\n", "\\item 'gene10737'\n", "\\item 'gene10778'\n", "\\item 'gene10812'\n", "\\item 'gene10876'\n", "\\item 'gene10918'\n", "\\item 'gene10949'\n", "\\item 'gene10956'\n", "\\item 'gene11212'\n", "\\item 'gene11218'\n", "\\item 'gene11295'\n", "\\item 'gene11357'\n", "\\item 'gene11360'\n", "\\item 'gene11366'\n", "\\item 'gene11384'\n", "\\item 'gene11429'\n", "\\item 'gene11433'\n", "\\item 'gene11478'\n", "\\item 'gene11527'\n", "\\item 'gene11553'\n", "\\item 'gene11564'\n", "\\item 'gene11631'\n", "\\item 'gene11824'\n", "\\item 'gene11931'\n", "\\item 'gene12102'\n", "\\item 'gene12211'\n", "\\item 'gene12253'\n", "\\item 'gene12354'\n", "\\item 'gene12370'\n", "\\item 'gene12377'\n", "\\item 'gene12425'\n", "\\item 'gene12508'\n", "\\item 'gene12547'\n", "\\item 'gene12559'\n", "\\item 'gene12567'\n", "\\item 'gene12674'\n", "\\item 'gene12675'\n", "\\item 'gene12694'\n", "\\item 'gene12704'\n", "\\item 'gene12723'\n", "\\item 'gene12769'\n", "\\item 'gene12777'\n", "\\item 'gene12809'\n", "\\item 'gene12819'\n", "\\item 'gene12825'\n", "\\item 'gene12864'\n", "\\item 'gene12902'\n", "\\item 'gene12927'\n", "\\item 'gene12947'\n", "\\item 'gene13148'\n", "\\item 'gene13170'\n", "\\item 'gene13179'\n", "\\item 'gene13233'\n", "\\item 'gene13381'\n", "\\item 'gene13461'\n", "\\item 'gene13567'\n", "\\item 'gene13607'\n", "\\item 'gene13649'\n", "\\item 'gene13711'\n", "\\item 'gene13723'\n", "\\item 'gene13742'\n", "\\item 'gene13780'\n", "\\item 'gene13823'\n", "\\item 'gene13832'\n", "\\item 'gene13872'\n", "\\item 'gene14038'\n", "\\item 'gene14154'\n", "\\item 'gene14161'\n", "\\item 'gene14178'\n", "\\item 'gene14434'\n", "\\item 'gene14443'\n", "\\item 'gene14508'\n", "\\item 'gene14539'\n", "\\item 'gene14560'\n", "\\item 'gene14590'\n", "\\item 'gene14604'\n", "\\item 'gene14623'\n", "\\item 'gene14656'\n", "\\item 'gene14660'\n", "\\item 'gene14782'\n", "\\item 'gene14784'\n", "\\item 'gene14808'\n", "\\item 'gene14889'\n", "\\item 'gene14924'\n", "\\item 'gene15062'\n", "\\item 'gene15069'\n", "\\item 'gene15083'\n", "\\item 'gene15087'\n", "\\item 'gene15101'\n", "\\item 'gene15128'\n", "\\item 'gene15173'\n", "\\item 'gene15177'\n", "\\item 'gene15180'\n", "\\item 'gene15232'\n", "\\item 'gene15263'\n", "\\item 'gene15361'\n", "\\item 'gene15415'\n", "\\item 'gene15454'\n", "\\item 'gene15523'\n", "\\item 'gene15530'\n", "\\item 'gene15592'\n", "\\item 'gene15596'\n", "\\item 'gene15619'\n", "\\item 'gene15709'\n", "\\item 'gene15714'\n", "\\item 'gene15725'\n", "\\item 'gene15743'\n", "\\item 'gene15892'\n", "\\item 'gene16056'\n", "\\item 'gene16088'\n", "\\item 'gene16095'\n", "\\item 'gene16122'\n", "\\item 'gene16164'\n", "\\item 'gene16197'\n", "\\item 'gene16213'\n", "\\item 'gene16240'\n", "\\item 'gene16256'\n", "\\item 'gene16482'\n", "\\item 'gene16647'\n", "\\item 'gene16701'\n", "\\item 'gene16705'\n", "\\item 'gene16788'\n", "\\item 'gene16810'\n", "\\item 'gene16830'\n", "\\item 'gene16870'\n", "\\item 'gene16967'\n", "\\item 'gene16978'\n", "\\item 'gene16987'\n", "\\item 'gene17113'\n", "\\item 'gene17129'\n", "\\item 'gene17146'\n", "\\item 'gene17224'\n", "\\item 'gene17233'\n", "\\item 'gene17396'\n", "\\item 'gene17432'\n", "\\item 'gene17562'\n", "\\item 'gene17582'\n", "\\item 'gene17605'\n", "\\item 'gene17625'\n", "\\item 'gene17862'\n", "\\item 'gene17910'\n", "\\item 'gene18009'\n", "\\item 'gene18055'\n", "\\item 'gene18169'\n", "\\item 'gene18206'\n", "\\item 'gene18207'\n", "\\item 'gene18316'\n", "\\item 'gene18329'\n", "\\item 'gene18341'\n", "\\item 'gene18375'\n", "\\item 'gene18410'\n", "\\item 'gene18453'\n", "\\item 'gene18487'\n", "\\item 'gene18509'\n", "\\item 'gene18530'\n", "\\item 'gene18722'\n", "\\item 'gene18811'\n", "\\item 'gene18847'\n", "\\item 'gene18905'\n", "\\item 'gene18957'\n", "\\item 'gene19008'\n", "\\item 'gene19066'\n", "\\item 'gene19080'\n", "\\item 'gene19086'\n", "\\item 'gene19137'\n", "\\item 'gene19216'\n", "\\item 'gene19251'\n", "\\item 'gene19287'\n", "\\item 'gene19564'\n", "\\item 'gene19666'\n", "\\item 'gene19701'\n", "\\item 'gene19757'\n", "\\item 'gene19771'\n", "\\item 'gene19795'\n", "\\item 'gene19930'\n", "\\item 'gene19978'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'gene160'\n", "2. 'gene393'\n", "3. 'gene412'\n", "4. 'gene439'\n", "5. 'gene452'\n", "6. 'gene490'\n", "7. 'gene506'\n", "8. 'gene601'\n", "9. 'gene683'\n", "10. 'gene748'\n", "11. 'gene766'\n", "12. 'gene771'\n", "13. 'gene808'\n", "14. 'gene830'\n", "15. 'gene834'\n", "16. 'gene862'\n", "17. 'gene888'\n", "18. 'gene914'\n", "19. 'gene1013'\n", "20. 'gene1035'\n", "21. 'gene1057'\n", "22. 'gene1082'\n", "23. 'gene1138'\n", "24. 'gene1172'\n", "25. 'gene1194'\n", "26. 'gene1322'\n", "27. 'gene1338'\n", "28. 'gene1462'\n", "29. 'gene1563'\n", "30. 'gene1582'\n", "31. 'gene1594'\n", "32. 'gene1775'\n", "33. 'gene1844'\n", "34. 'gene1857'\n", "35. 'gene1868'\n", "36. 'gene1910'\n", "37. 'gene1950'\n", "38. 'gene1957'\n", "39. 'gene2094'\n", "40. 'gene2106'\n", "41. 'gene2119'\n", "42. 'gene2148'\n", "43. 'gene2233'\n", "44. 'gene2253'\n", "45. 'gene2300'\n", "46. 'gene2358'\n", "47. 'gene2407'\n", "48. 'gene2450'\n", "49. 'gene2454'\n", "50. 'gene2461'\n", "51. 'gene2508'\n", "52. 'gene2548'\n", "53. 'gene2560'\n", "54. 'gene2608'\n", "55. 'gene2630'\n", "56. 'gene2642'\n", "57. 'gene2671'\n", "58. 'gene2736'\n", "59. 'gene2772'\n", "60. 'gene2818'\n", "61. 'gene2822'\n", "62. 'gene2941'\n", "63. 'gene2971'\n", "64. 'gene3012'\n", "65. 'gene3072'\n", "66. 'gene3116'\n", "67. 'gene3215'\n", "68. 'gene3262'\n", "69. 'gene3269'\n", "70. 'gene3306'\n", "71. 'gene3403'\n", "72. 'gene3455'\n", "73. 'gene3490'\n", "74. 'gene3492'\n", "75. 'gene3546'\n", "76. 'gene3574'\n", "77. 'gene3647'\n", "78. 'gene3693'\n", "79. 'gene3731'\n", "80. 'gene3748'\n", "81. 'gene3769'\n", "82. 'gene3802'\n", "83. 'gene3964'\n", "84. 'gene3971'\n", "85. 'gene4007'\n", "86. 'gene4028'\n", "87. 'gene4059'\n", "88. 'gene4097'\n", "89. 'gene4117'\n", "90. 'gene4233'\n", "91. 'gene4293'\n", "92. 'gene4427'\n", "93. 'gene4438'\n", "94. 'gene4501'\n", "95. 'gene4527'\n", "96. 'gene4634'\n", "97. 'gene4652'\n", "98. 'gene4723'\n", "99. 'gene4846'\n", "100. 'gene4890'\n", "101. 'gene5212'\n", "102. 'gene5233'\n", "103. 'gene5272'\n", "104. 'gene5375'\n", "105. 'gene5478'\n", "106. 'gene5520'\n", "107. 'gene5664'\n", "108. 'gene5770'\n", "109. 'gene5828'\n", "110. 'gene5936'\n", "111. 'gene5975'\n", "112. 'gene5987'\n", "113. 'gene6025'\n", "114. 'gene6029'\n", "115. 'gene6096'\n", "116. 'gene6097'\n", "117. 'gene6102'\n", "118. 'gene6104'\n", "119. 'gene6210'\n", "120. 'gene6412'\n", "121. 'gene6478'\n", "122. 'gene6534'\n", "123. 'gene6650'\n", "124. 'gene6672'\n", "125. 'gene6678'\n", "126. 'gene6756'\n", "127. 'gene6779'\n", "128. 'gene6908'\n", "129. 'gene7116'\n", "130. 'gene7123'\n", "131. 'gene7215'\n", "132. 'gene7220'\n", "133. 'gene7250'\n", "134. 'gene7252'\n", "135. 'gene7363'\n", "136. 'gene7374'\n", "137. 'gene7496'\n", "138. 'gene7568'\n", "139. 'gene7578'\n", "140. 'gene7648'\n", "141. 'gene7692'\n", "142. 'gene7704'\n", "143. 'gene7713'\n", "144. 'gene7766'\n", "145. 'gene7783'\n", "146. 'gene7873'\n", "147. 'gene8102'\n", "148. 'gene8163'\n", "149. 'gene8304'\n", "150. 'gene8559'\n", "151. 'gene8758'\n", "152. 'gene8843'\n", "153. 'gene8906'\n", "154. 'gene8924'\n", "155. 'gene8993'\n", "156. 'gene8995'\n", "157. 'gene9104'\n", "158. 'gene9192'\n", "159. 'gene9203'\n", "160. 'gene9227'\n", "161. 'gene9326'\n", "162. 'gene9403'\n", "163. 'gene9506'\n", "164. 'gene9516'\n", "165. 'gene9652'\n", "166. 'gene9653'\n", "167. 'gene9679'\n", "168. 'gene9725'\n", "169. 'gene9741'\n", "170. 'gene9817'\n", "171. 'gene9834'\n", "172. 'gene9848'\n", "173. 'gene9857'\n", "174. 'gene9906'\n", "175. 'gene10003'\n", "176. 'gene10057'\n", "177. 'gene10105'\n", "178. 'gene10119'\n", "179. 'gene10190'\n", "180. 'gene10210'\n", "181. 'gene10211'\n", "182. 'gene10283'\n", "183. 'gene10326'\n", "184. 'gene10437'\n", "185. 'gene10446'\n", "186. 'gene10529'\n", "187. 'gene10587'\n", "188. 'gene10639'\n", "189. 'gene10657'\n", "190. 'gene10667'\n", "191. 'gene10674'\n", "192. 'gene10682'\n", "193. 'gene10696'\n", "194. 'gene10706'\n", "195. 'gene10737'\n", "196. 'gene10778'\n", "197. 'gene10812'\n", "198. 'gene10876'\n", "199. 'gene10918'\n", "200. 'gene10949'\n", "201. 'gene10956'\n", "202. 'gene11212'\n", "203. 'gene11218'\n", "204. 'gene11295'\n", "205. 'gene11357'\n", "206. 'gene11360'\n", "207. 'gene11366'\n", "208. 'gene11384'\n", "209. 'gene11429'\n", "210. 'gene11433'\n", "211. 'gene11478'\n", "212. 'gene11527'\n", "213. 'gene11553'\n", "214. 'gene11564'\n", "215. 'gene11631'\n", "216. 'gene11824'\n", "217. 'gene11931'\n", "218. 'gene12102'\n", "219. 'gene12211'\n", "220. 'gene12253'\n", "221. 'gene12354'\n", "222. 'gene12370'\n", "223. 'gene12377'\n", "224. 'gene12425'\n", "225. 'gene12508'\n", "226. 'gene12547'\n", "227. 'gene12559'\n", "228. 'gene12567'\n", "229. 'gene12674'\n", "230. 'gene12675'\n", "231. 'gene12694'\n", "232. 'gene12704'\n", "233. 'gene12723'\n", "234. 'gene12769'\n", "235. 'gene12777'\n", "236. 'gene12809'\n", "237. 'gene12819'\n", "238. 'gene12825'\n", "239. 'gene12864'\n", "240. 'gene12902'\n", "241. 'gene12927'\n", "242. 'gene12947'\n", "243. 'gene13148'\n", "244. 'gene13170'\n", "245. 'gene13179'\n", "246. 'gene13233'\n", "247. 'gene13381'\n", "248. 'gene13461'\n", "249. 'gene13567'\n", "250. 'gene13607'\n", "251. 'gene13649'\n", "252. 'gene13711'\n", "253. 'gene13723'\n", "254. 'gene13742'\n", "255. 'gene13780'\n", "256. 'gene13823'\n", "257. 'gene13832'\n", "258. 'gene13872'\n", "259. 'gene14038'\n", "260. 'gene14154'\n", "261. 'gene14161'\n", "262. 'gene14178'\n", "263. 'gene14434'\n", "264. 'gene14443'\n", "265. 'gene14508'\n", "266. 'gene14539'\n", "267. 'gene14560'\n", "268. 'gene14590'\n", "269. 'gene14604'\n", "270. 'gene14623'\n", "271. 'gene14656'\n", "272. 'gene14660'\n", "273. 'gene14782'\n", "274. 'gene14784'\n", "275. 'gene14808'\n", "276. 'gene14889'\n", "277. 'gene14924'\n", "278. 'gene15062'\n", "279. 'gene15069'\n", "280. 'gene15083'\n", "281. 'gene15087'\n", "282. 'gene15101'\n", "283. 'gene15128'\n", "284. 'gene15173'\n", "285. 'gene15177'\n", "286. 'gene15180'\n", "287. 'gene15232'\n", "288. 'gene15263'\n", "289. 'gene15361'\n", "290. 'gene15415'\n", "291. 'gene15454'\n", "292. 'gene15523'\n", "293. 'gene15530'\n", "294. 'gene15592'\n", "295. 'gene15596'\n", "296. 'gene15619'\n", "297. 'gene15709'\n", "298. 'gene15714'\n", "299. 'gene15725'\n", "300. 'gene15743'\n", "301. 'gene15892'\n", "302. 'gene16056'\n", "303. 'gene16088'\n", "304. 'gene16095'\n", "305. 'gene16122'\n", "306. 'gene16164'\n", "307. 'gene16197'\n", "308. 'gene16213'\n", "309. 'gene16240'\n", "310. 'gene16256'\n", "311. 'gene16482'\n", "312. 'gene16647'\n", "313. 'gene16701'\n", "314. 'gene16705'\n", "315. 'gene16788'\n", "316. 'gene16810'\n", "317. 'gene16830'\n", "318. 'gene16870'\n", "319. 'gene16967'\n", "320. 'gene16978'\n", "321. 'gene16987'\n", "322. 'gene17113'\n", "323. 'gene17129'\n", "324. 'gene17146'\n", "325. 'gene17224'\n", "326. 'gene17233'\n", "327. 'gene17396'\n", "328. 'gene17432'\n", "329. 'gene17562'\n", "330. 'gene17582'\n", "331. 'gene17605'\n", "332. 'gene17625'\n", "333. 'gene17862'\n", "334. 'gene17910'\n", "335. 'gene18009'\n", "336. 'gene18055'\n", "337. 'gene18169'\n", "338. 'gene18206'\n", "339. 'gene18207'\n", "340. 'gene18316'\n", "341. 'gene18329'\n", "342. 'gene18341'\n", "343. 'gene18375'\n", "344. 'gene18410'\n", "345. 'gene18453'\n", "346. 'gene18487'\n", "347. 'gene18509'\n", "348. 'gene18530'\n", "349. 'gene18722'\n", "350. 'gene18811'\n", "351. 'gene18847'\n", "352. 'gene18905'\n", "353. 'gene18957'\n", "354. 'gene19008'\n", "355. 'gene19066'\n", "356. 'gene19080'\n", "357. 'gene19086'\n", "358. 'gene19137'\n", "359. 'gene19216'\n", "360. 'gene19251'\n", "361. 'gene19287'\n", "362. 'gene19564'\n", "363. 'gene19666'\n", "364. 'gene19701'\n", "365. 'gene19757'\n", "366. 'gene19771'\n", "367. 'gene19795'\n", "368. 'gene19930'\n", "369. 'gene19978'\n", "\n", "\n" ], "text/plain": [ " [1] \"gene160\" \"gene393\" \"gene412\" \"gene439\" \"gene452\" \"gene490\" \n", " [7] \"gene506\" \"gene601\" \"gene683\" \"gene748\" \"gene766\" \"gene771\" \n", " [13] \"gene808\" \"gene830\" \"gene834\" \"gene862\" \"gene888\" \"gene914\" \n", " [19] \"gene1013\" \"gene1035\" \"gene1057\" \"gene1082\" \"gene1138\" \"gene1172\" \n", " [25] \"gene1194\" \"gene1322\" \"gene1338\" \"gene1462\" \"gene1563\" \"gene1582\" \n", " [31] \"gene1594\" \"gene1775\" \"gene1844\" \"gene1857\" \"gene1868\" \"gene1910\" \n", " [37] \"gene1950\" \"gene1957\" \"gene2094\" \"gene2106\" \"gene2119\" \"gene2148\" \n", " [43] \"gene2233\" \"gene2253\" \"gene2300\" \"gene2358\" \"gene2407\" \"gene2450\" \n", " [49] \"gene2454\" \"gene2461\" \"gene2508\" \"gene2548\" \"gene2560\" \"gene2608\" \n", " [55] \"gene2630\" \"gene2642\" \"gene2671\" \"gene2736\" \"gene2772\" \"gene2818\" \n", " [61] \"gene2822\" \"gene2941\" \"gene2971\" \"gene3012\" \"gene3072\" \"gene3116\" \n", " [67] \"gene3215\" \"gene3262\" \"gene3269\" \"gene3306\" \"gene3403\" \"gene3455\" \n", " [73] \"gene3490\" \"gene3492\" \"gene3546\" \"gene3574\" \"gene3647\" \"gene3693\" \n", " [79] \"gene3731\" \"gene3748\" \"gene3769\" \"gene3802\" \"gene3964\" \"gene3971\" \n", " [85] \"gene4007\" \"gene4028\" \"gene4059\" \"gene4097\" \"gene4117\" \"gene4233\" \n", " [91] \"gene4293\" \"gene4427\" \"gene4438\" \"gene4501\" \"gene4527\" \"gene4634\" \n", " [97] \"gene4652\" \"gene4723\" \"gene4846\" \"gene4890\" \"gene5212\" \"gene5233\" \n", "[103] \"gene5272\" \"gene5375\" \"gene5478\" \"gene5520\" \"gene5664\" \"gene5770\" \n", "[109] \"gene5828\" \"gene5936\" \"gene5975\" \"gene5987\" \"gene6025\" \"gene6029\" \n", "[115] \"gene6096\" \"gene6097\" \"gene6102\" \"gene6104\" \"gene6210\" \"gene6412\" \n", "[121] \"gene6478\" \"gene6534\" \"gene6650\" \"gene6672\" \"gene6678\" \"gene6756\" \n", "[127] \"gene6779\" \"gene6908\" \"gene7116\" \"gene7123\" \"gene7215\" \"gene7220\" \n", "[133] \"gene7250\" \"gene7252\" \"gene7363\" \"gene7374\" \"gene7496\" \"gene7568\" \n", "[139] \"gene7578\" \"gene7648\" \"gene7692\" \"gene7704\" \"gene7713\" \"gene7766\" \n", "[145] \"gene7783\" \"gene7873\" \"gene8102\" \"gene8163\" \"gene8304\" \"gene8559\" \n", "[151] \"gene8758\" \"gene8843\" \"gene8906\" \"gene8924\" \"gene8993\" \"gene8995\" \n", "[157] \"gene9104\" \"gene9192\" \"gene9203\" \"gene9227\" \"gene9326\" \"gene9403\" \n", "[163] \"gene9506\" \"gene9516\" \"gene9652\" \"gene9653\" \"gene9679\" \"gene9725\" \n", "[169] \"gene9741\" \"gene9817\" \"gene9834\" \"gene9848\" \"gene9857\" \"gene9906\" \n", "[175] \"gene10003\" \"gene10057\" \"gene10105\" \"gene10119\" \"gene10190\" \"gene10210\"\n", "[181] \"gene10211\" \"gene10283\" \"gene10326\" \"gene10437\" \"gene10446\" \"gene10529\"\n", "[187] \"gene10587\" \"gene10639\" \"gene10657\" \"gene10667\" \"gene10674\" \"gene10682\"\n", "[193] \"gene10696\" \"gene10706\" \"gene10737\" \"gene10778\" \"gene10812\" \"gene10876\"\n", "[199] \"gene10918\" \"gene10949\" \"gene10956\" \"gene11212\" \"gene11218\" \"gene11295\"\n", "[205] \"gene11357\" \"gene11360\" \"gene11366\" \"gene11384\" \"gene11429\" \"gene11433\"\n", "[211] \"gene11478\" \"gene11527\" \"gene11553\" \"gene11564\" \"gene11631\" \"gene11824\"\n", "[217] \"gene11931\" \"gene12102\" \"gene12211\" \"gene12253\" \"gene12354\" \"gene12370\"\n", "[223] \"gene12377\" \"gene12425\" \"gene12508\" \"gene12547\" \"gene12559\" \"gene12567\"\n", "[229] \"gene12674\" \"gene12675\" \"gene12694\" \"gene12704\" \"gene12723\" \"gene12769\"\n", "[235] \"gene12777\" \"gene12809\" \"gene12819\" \"gene12825\" \"gene12864\" \"gene12902\"\n", "[241] \"gene12927\" \"gene12947\" \"gene13148\" \"gene13170\" \"gene13179\" \"gene13233\"\n", "[247] \"gene13381\" \"gene13461\" \"gene13567\" \"gene13607\" \"gene13649\" \"gene13711\"\n", "[253] \"gene13723\" \"gene13742\" \"gene13780\" \"gene13823\" \"gene13832\" \"gene13872\"\n", "[259] \"gene14038\" \"gene14154\" \"gene14161\" \"gene14178\" \"gene14434\" \"gene14443\"\n", "[265] \"gene14508\" \"gene14539\" \"gene14560\" \"gene14590\" \"gene14604\" \"gene14623\"\n", "[271] \"gene14656\" \"gene14660\" \"gene14782\" \"gene14784\" \"gene14808\" \"gene14889\"\n", "[277] \"gene14924\" \"gene15062\" \"gene15069\" \"gene15083\" \"gene15087\" \"gene15101\"\n", "[283] \"gene15128\" \"gene15173\" \"gene15177\" \"gene15180\" \"gene15232\" \"gene15263\"\n", "[289] \"gene15361\" \"gene15415\" \"gene15454\" \"gene15523\" \"gene15530\" \"gene15592\"\n", "[295] \"gene15596\" \"gene15619\" \"gene15709\" \"gene15714\" \"gene15725\" \"gene15743\"\n", "[301] \"gene15892\" \"gene16056\" \"gene16088\" \"gene16095\" \"gene16122\" \"gene16164\"\n", "[307] \"gene16197\" \"gene16213\" \"gene16240\" \"gene16256\" \"gene16482\" \"gene16647\"\n", "[313] \"gene16701\" \"gene16705\" \"gene16788\" \"gene16810\" \"gene16830\" \"gene16870\"\n", "[319] \"gene16967\" \"gene16978\" \"gene16987\" \"gene17113\" \"gene17129\" \"gene17146\"\n", "[325] \"gene17224\" \"gene17233\" \"gene17396\" \"gene17432\" \"gene17562\" \"gene17582\"\n", "[331] \"gene17605\" \"gene17625\" \"gene17862\" \"gene17910\" \"gene18009\" \"gene18055\"\n", "[337] \"gene18169\" \"gene18206\" \"gene18207\" \"gene18316\" \"gene18329\" \"gene18341\"\n", "[343] \"gene18375\" \"gene18410\" \"gene18453\" \"gene18487\" \"gene18509\" \"gene18530\"\n", "[349] \"gene18722\" \"gene18811\" \"gene18847\" \"gene18905\" \"gene18957\" \"gene19008\"\n", "[355] \"gene19066\" \"gene19080\" \"gene19086\" \"gene19137\" \"gene19216\" \"gene19251\"\n", "[361] \"gene19287\" \"gene19564\" \"gene19666\" \"gene19701\" \"gene19757\" \"gene19771\"\n", "[367] \"gene19795\" \"gene19930\" \"gene19978\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hits <- expr[idx,]\n", "rownames(hits)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Plot a heatmap of the genes that meet the FDR filter using agglomerative hierarchical clustering with complete linkage for the dendrograms. Arrange the subjects so all controls are on the left and all cases are on the right in the heatmap." ] }, { "cell_type": "code", "execution_count": 221, "metadata": {}, "outputs": [], "source": [ "clusters <- hclust(dist(hits), method = 'complete')" ] }, { "cell_type": "code", "execution_count": 222, "metadata": { "collapsed": true }, "outputs": [], "source": [ "library(pheatmap)" ] }, { "cell_type": "code", "execution_count": 223, "metadata": { "collapsed": true }, "outputs": [], "source": [ "annot <- data.frame(grp=group$Group, row.names=colnames(hits))" ] }, { "cell_type": "code", "execution_count": 224, "metadata": {}, "outputs": [], "source": [ "options(repr.plot.width=8, repr.plot.height=8)" ] }, { "cell_type": "code", "execution_count": 303, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAYAAAB91L6VAAAEDWlDQ1BJQ0MgUHJvZmlsZQAA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", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pheatmap(hits, \n", " clustering_method = 'single',\n", " annotation_col = annot,\n", " annotation_colors = list(grp = c(case = \"blue\", ctrl = \"yellow\")),\n", " show_rownames = FALSE, show_colnames = FALSE,\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Supervised Learning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Perform logistic regression using LOOCV and the top 10 genes to generate class predictions (case or control) for all subjects" ] }, { "cell_type": "code", "execution_count": 226, "metadata": {}, "outputs": [], "source": [ "n <- nrow(df)\n", "pred <- numeric(n) \n", "for (i in 1:n) {\n", " p.values <- rowttests(expr[,-i], fac = group$Group[-i])$p.value \n", " idx <- order(p.values)\n", " hits <- expr[idx[1:10],]\n", " \n", " # transpose so geens are variables then add group informaiton\n", " df <- t(hits)\n", " df <- data.frame(y=as.factor(group$Group), df)\n", " \n", " model <- glm(y ~ ., data=df[-i,], family='binomial')\n", " pred[i] <- predict(model, df[i,], type='response')\n", "}\n", "yhat <- ifelse(pred < 0.5, 1, 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Evaluate the accuracy, sensitivity, specificity, PPV, NPV of the LOOCV logistic regression" ] }, { "cell_type": "code", "execution_count": 227, "metadata": { "collapsed": true }, "outputs": [], "source": [ "suppressPackageStartupMessages(library(caret))" ] }, { "cell_type": "code", "execution_count": 228, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Confusion Matrix and Statistics\n", "\n", " Reference\n", "Prediction 1 2\n", " 1 50 0\n", " 2 0 50\n", " \n", " Accuracy : 1 \n", " 95% CI : (0.9638, 1)\n", " No Information Rate : 0.5 \n", " P-Value [Acc > NIR] : < 2.2e-16 \n", " \n", " Kappa : 1 \n", " Mcnemar's Test P-Value : NA \n", " \n", " Sensitivity : 1.0 \n", " Specificity : 1.0 \n", " Pos Pred Value : 1.0 \n", " Neg Pred Value : 1.0 \n", " Prevalence : 0.5 \n", " Detection Rate : 0.5 \n", " Detection Prevalence : 0.5 \n", " Balanced Accuracy : 1.0 \n", " \n", " 'Positive' Class : 1 \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "confusionMatrix(yhat, as.integer(group$Group))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Plot an ROC curve for the LOOCV predictions" ] }, { "cell_type": "code", "execution_count": 229, "metadata": { "collapsed": true }, "outputs": [], "source": [ "suppressPackageStartupMessages(library(ROCR))" ] }, { "cell_type": "code", "execution_count": 230, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ROC <- function(preddat, mlab = \"probhat\", ylab = \"y\") {\n", " m <- preddat[[mlab]]\n", " y <- preddat[[ylab]]\n", " performance(prediction(m, y), measure = \"tpr\", x.measure = \"fpr\")\n", "}" ] }, { "cell_type": "code", "execution_count": 231, "metadata": { "collapsed": true }, "outputs": [], "source": [ "preddat <- data.frame(probhat=pred, yhat=yhat, y=as.integer(group$Group))\n", "my.roc <- ROC(preddat)" ] }, { "cell_type": "code", "execution_count": 232, "metadata": { "collapsed": true }, "outputs": [], "source": [ "options(repr.plot.width=4, repr.plot.height=4)" ] }, { "cell_type": "code", "execution_count": 233, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAYAAAB91L6VAAAEDWlDQ1BJQ0MgUHJvZmlsZQAA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\nMPZCVQIAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(my.roc, col = \"blue\")\n", "abline(0, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Read the `outcomes.txt` data into a `data.frame` called `outcomes` with two columns `PID` and `outcome`." ] }, { "cell_type": "code", "execution_count": 234, "metadata": {}, "outputs": [], "source": [ "outcomes <- read.table('data/outcomes.txt')" ] }, { "cell_type": "code", "execution_count": 235, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
V1V2
1035 491.65
1091 512.43
1095 513.15
1019 571.56
1081 517.13
1077 535.44
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " V1 & V2\\\\\n", "\\hline\n", "\t 1035 & 491.65\\\\\n", "\t 1091 & 512.43\\\\\n", "\t 1095 & 513.15\\\\\n", "\t 1019 & 571.56\\\\\n", "\t 1081 & 517.13\\\\\n", "\t 1077 & 535.44\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "V1 | V2 | \n", "|---|---|---|---|---|---|\n", "| 1035 | 491.65 | \n", "| 1091 | 512.43 | \n", "| 1095 | 513.15 | \n", "| 1019 | 571.56 | \n", "| 1081 | 517.13 | \n", "| 1077 | 535.44 | \n", "\n", "\n" ], "text/plain": [ " V1 V2 \n", "1 1035 491.65\n", "2 1091 512.43\n", "3 1095 513.15\n", "4 1019 571.56\n", "5 1081 517.13\n", "6 1077 535.44" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(outcomes)" ] }, { "cell_type": "code", "execution_count": 236, "metadata": { "collapsed": true }, "outputs": [], "source": [ "colnames(outcomes) = c('PID', 'outcome')" ] }, { "cell_type": "code", "execution_count": 243, "metadata": { "collapsed": true }, "outputs": [], "source": [ "outcomes$PID = as.factor(outcomes$PID)" ] }, { "cell_type": "code", "execution_count": 244, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
PIDoutcome
1035 491.65
1091 512.43
1095 513.15
1019 571.56
1081 517.13
1077 535.44
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " PID & outcome\\\\\n", "\\hline\n", "\t 1035 & 491.65\\\\\n", "\t 1091 & 512.43\\\\\n", "\t 1095 & 513.15\\\\\n", "\t 1019 & 571.56\\\\\n", "\t 1081 & 517.13\\\\\n", "\t 1077 & 535.44\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "PID | outcome | \n", "|---|---|---|---|---|---|\n", "| 1035 | 491.65 | \n", "| 1091 | 512.43 | \n", "| 1095 | 513.15 | \n", "| 1019 | 571.56 | \n", "| 1081 | 517.13 | \n", "| 1077 | 535.44 | \n", "\n", "\n" ], "text/plain": [ " PID outcome\n", "1 1035 491.65 \n", "2 1091 512.43 \n", "3 1095 513.15 \n", "4 1019 571.56 \n", "5 1081 517.13 \n", "6 1077 535.44 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(outcomes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Merge the `outcomes` and `expr` by joining on the `PID` column." ] }, { "cell_type": "code", "execution_count": 239, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
PIDgene1gene2gene3gene4gene5gene6gene7gene8gene9⋯gene19991gene19992gene19993gene19994gene19995gene19996gene19997gene19998gene19999gene20000
pt1001 1.5619416 0.1274296 0.3818537 0.1754658 -0.119502825-1.40226660 -0.8833635 -0.94309346 -0.4272967 ⋯ -0.62969491 0.2422296 0.8998351 0.6209216 -0.4764382 -0.08677132 -0.8747234 0.5187669 0.2592983 -0.2476950
pt1002 0.7334951 0.8649770 1.8528351 -0.5721328 0.012561868 0.54493040 0.5795712 0.06164405 -0.5987428 ⋯ 0.37135403 0.8149647 0.9313236 -0.1974330 -1.5460305 0.43927981 0.1554110 -0.4076643 -1.3342893 -0.1228203
pt1003 2.2814275 1.1533513 0.5677770 0.7313274 0.004700874-0.03543193 -0.9456161 -1.74386169 1.6602020 ⋯ 0.93993227 -0.6044223 0.1441122 1.1243218 -0.2946378 -1.49409366 -0.1392395 1.0223122 0.7461696 1.3233091
pt1004 -0.2609866 1.3553463 -0.1613340 0.1367666 -2.023435482-0.45307586 -0.4475958 0.96817412 -1.0207642 ⋯ 0.63478797 -1.6004834 0.2385775 0.4706923 -2.1459718 1.00600499 1.1101697 -1.0333510 -0.9573566 0.8587216
pt1005 -1.2796818 -0.6167624 -1.4883161 0.6504108 -1.798611064 0.39441587 -0.3542170 -0.33118565 -0.2219380 ⋯ 0.06840501 0.5348225 -0.3387108 0.2914713 0.9537248 1.61069262 -0.7970636 1.7090697 -0.3343707 -1.0479672
pt1006 -1.6913107 0.1579946 -0.2470046 1.6882536 0.441772120 0.55306632 1.2643491 1.61029959 0.9706492 ⋯ -0.83824677 -1.2207351 -0.2862301 1.4972595 -0.8763991 -2.01472158 0.2444913 -0.3154821 -0.8359005 0.8976610
\n" ], "text/markdown": [ "\n", "PID | gene1 | gene2 | gene3 | gene4 | gene5 | gene6 | gene7 | gene8 | gene9 | ⋯ | gene19991 | gene19992 | gene19993 | gene19994 | gene19995 | gene19996 | gene19997 | gene19998 | gene19999 | gene20000 | \n", "|---|---|---|---|---|---|\n", "| pt1001 | 1.5619416 | 0.1274296 | 0.3818537 | 0.1754658 | -0.119502825 | -1.40226660 | -0.8833635 | -0.94309346 | -0.4272967 | ⋯ | -0.62969491 | 0.2422296 | 0.8998351 | 0.6209216 | -0.4764382 | -0.08677132 | -0.8747234 | 0.5187669 | 0.2592983 | -0.2476950 | \n", "| pt1002 | 0.7334951 | 0.8649770 | 1.8528351 | -0.5721328 | 0.012561868 | 0.54493040 | 0.5795712 | 0.06164405 | -0.5987428 | ⋯ | 0.37135403 | 0.8149647 | 0.9313236 | -0.1974330 | -1.5460305 | 0.43927981 | 0.1554110 | -0.4076643 | -1.3342893 | -0.1228203 | \n", "| pt1003 | 2.2814275 | 1.1533513 | 0.5677770 | 0.7313274 | 0.004700874 | -0.03543193 | -0.9456161 | -1.74386169 | 1.6602020 | ⋯ | 0.93993227 | -0.6044223 | 0.1441122 | 1.1243218 | -0.2946378 | -1.49409366 | -0.1392395 | 1.0223122 | 0.7461696 | 1.3233091 | \n", "| pt1004 | -0.2609866 | 1.3553463 | -0.1613340 | 0.1367666 | -2.023435482 | -0.45307586 | -0.4475958 | 0.96817412 | -1.0207642 | ⋯ | 0.63478797 | -1.6004834 | 0.2385775 | 0.4706923 | -2.1459718 | 1.00600499 | 1.1101697 | -1.0333510 | -0.9573566 | 0.8587216 | \n", "| pt1005 | -1.2796818 | -0.6167624 | -1.4883161 | 0.6504108 | -1.798611064 | 0.39441587 | -0.3542170 | -0.33118565 | -0.2219380 | ⋯ | 0.06840501 | 0.5348225 | -0.3387108 | 0.2914713 | 0.9537248 | 1.61069262 | -0.7970636 | 1.7090697 | -0.3343707 | -1.0479672 | \n", "| pt1006 | -1.6913107 | 0.1579946 | -0.2470046 | 1.6882536 | 0.441772120 | 0.55306632 | 1.2643491 | 1.61029959 | 0.9706492 | ⋯ | -0.83824677 | -1.2207351 | -0.2862301 | 1.4972595 | -0.8763991 | -2.01472158 | 0.2444913 | -0.3154821 | -0.8359005 | 0.8976610 | \n", "\n", "\n" ], "text/plain": [ " PID gene1 gene2 gene3 gene4 gene5 gene6 \n", "1 pt1001 1.5619416 0.1274296 0.3818537 0.1754658 -0.119502825 -1.40226660\n", "2 pt1002 0.7334951 0.8649770 1.8528351 -0.5721328 0.012561868 0.54493040\n", "3 pt1003 2.2814275 1.1533513 0.5677770 0.7313274 0.004700874 -0.03543193\n", "4 pt1004 -0.2609866 1.3553463 -0.1613340 0.1367666 -2.023435482 -0.45307586\n", "5 pt1005 -1.2796818 -0.6167624 -1.4883161 0.6504108 -1.798611064 0.39441587\n", "6 pt1006 -1.6913107 0.1579946 -0.2470046 1.6882536 0.441772120 0.55306632\n", " gene7 gene8 gene9 ⋯ gene19991 gene19992 gene19993 \n", "1 -0.8833635 -0.94309346 -0.4272967 ⋯ -0.62969491 0.2422296 0.8998351\n", "2 0.5795712 0.06164405 -0.5987428 ⋯ 0.37135403 0.8149647 0.9313236\n", "3 -0.9456161 -1.74386169 1.6602020 ⋯ 0.93993227 -0.6044223 0.1441122\n", "4 -0.4475958 0.96817412 -1.0207642 ⋯ 0.63478797 -1.6004834 0.2385775\n", "5 -0.3542170 -0.33118565 -0.2219380 ⋯ 0.06840501 0.5348225 -0.3387108\n", "6 1.2643491 1.61029959 0.9706492 ⋯ -0.83824677 -1.2207351 -0.2862301\n", " gene19994 gene19995 gene19996 gene19997 gene19998 gene19999 gene20000 \n", "1 0.6209216 -0.4764382 -0.08677132 -0.8747234 0.5187669 0.2592983 -0.2476950\n", "2 -0.1974330 -1.5460305 0.43927981 0.1554110 -0.4076643 -1.3342893 -0.1228203\n", "3 1.1243218 -0.2946378 -1.49409366 -0.1392395 1.0223122 0.7461696 1.3233091\n", "4 0.4706923 -2.1459718 1.00600499 1.1101697 -1.0333510 -0.9573566 0.8587216\n", "5 0.2914713 0.9537248 1.61069262 -0.7970636 1.7090697 -0.3343707 -1.0479672\n", "6 1.4972595 -0.8763991 -2.01472158 0.2444913 -0.3154821 -0.8359005 0.8976610" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(data)" ] }, { "cell_type": "code", "execution_count": 241, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
PIDoutcome
1035 491.65
1091 512.43
1095 513.15
1019 571.56
1081 517.13
1077 535.44
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " PID & outcome\\\\\n", "\\hline\n", "\t 1035 & 491.65\\\\\n", "\t 1091 & 512.43\\\\\n", "\t 1095 & 513.15\\\\\n", "\t 1019 & 571.56\\\\\n", "\t 1081 & 517.13\\\\\n", "\t 1077 & 535.44\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "PID | outcome | \n", "|---|---|---|---|---|---|\n", "| 1035 | 491.65 | \n", "| 1091 | 512.43 | \n", "| 1095 | 513.15 | \n", "| 1019 | 571.56 | \n", "| 1081 | 517.13 | \n", "| 1077 | 535.44 | \n", "\n", "\n" ], "text/plain": [ " PID outcome\n", "1 1035 491.65 \n", "2 1091 512.43 \n", "3 1095 513.15 \n", "4 1019 571.56 \n", "5 1081 517.13 \n", "6 1077 535.44 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(outcomes)" ] }, { "cell_type": "code", "execution_count": 246, "metadata": {}, "outputs": [], "source": [ "data <- data.frame(t(expr)) %>% rownames_to_column('PID') %>% mutate(PID=as.factor(substr(PID, 3,6)))\n", "df <- inner_join(data, outcomes, by='PID')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Perform LOOCV linear regression using the 5 genes most correlated with outcome to get outcome predictions for each subject" ] }, { "cell_type": "code", "execution_count": 260, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "
gene1gene2gene3gene4gene5gene6gene7gene8gene9gene10⋯gene19992gene19993gene19994gene19995gene19996gene19997gene19998gene19999gene20000outcome
10011.5619416 0.1274296 0.3818537 0.1754658 -0.11950283-1.4022666 -0.8833635 -0.94309346-0.4272967 0.3923721 ⋯ 0.2422296 0.8998351 0.6209216 -0.4764382 -0.08677132-0.8747234 0.5187669 0.2592983 -0.2476950 629.10
10020.7334951 0.8649770 1.8528351 -0.5721328 0.01256187 0.5449304 0.5795712 0.06164405-0.5987428 0.3893356 ⋯ 0.8149647 0.9313236 -0.1974330 -1.5460305 0.43927981 0.1554110 -0.4076643 -1.3342893 -0.1228203 558.71
\n" ], "text/markdown": [ "\n", "| | gene1 | gene2 | gene3 | gene4 | gene5 | gene6 | gene7 | gene8 | gene9 | gene10 | ⋯ | gene19992 | gene19993 | gene19994 | gene19995 | gene19996 | gene19997 | gene19998 | gene19999 | gene20000 | outcome | \n", "|---|---|\n", "| 1001 | 1.5619416 | 0.1274296 | 0.3818537 | 0.1754658 | -0.11950283 | -1.4022666 | -0.8833635 | -0.94309346 | -0.4272967 | 0.3923721 | ⋯ | 0.2422296 | 0.8998351 | 0.6209216 | -0.4764382 | -0.08677132 | -0.8747234 | 0.5187669 | 0.2592983 | -0.2476950 | 629.10 | \n", "| 1002 | 0.7334951 | 0.8649770 | 1.8528351 | -0.5721328 | 0.01256187 | 0.5449304 | 0.5795712 | 0.06164405 | -0.5987428 | 0.3893356 | ⋯ | 0.8149647 | 0.9313236 | -0.1974330 | -1.5460305 | 0.43927981 | 0.1554110 | -0.4076643 | -1.3342893 | -0.1228203 | 558.71 | \n", "\n", "\n" ], "text/plain": [ " gene1 gene2 gene3 gene4 gene5 gene6 gene7 \n", "1001 1.5619416 0.1274296 0.3818537 0.1754658 -0.11950283 -1.4022666 -0.8833635\n", "1002 0.7334951 0.8649770 1.8528351 -0.5721328 0.01256187 0.5449304 0.5795712\n", " gene8 gene9 gene10 ⋯ gene19992 gene19993 gene19994 \n", "1001 -0.94309346 -0.4272967 0.3923721 ⋯ 0.2422296 0.8998351 0.6209216\n", "1002 0.06164405 -0.5987428 0.3893356 ⋯ 0.8149647 0.9313236 -0.1974330\n", " gene19995 gene19996 gene19997 gene19998 gene19999 gene20000 outcome\n", "1001 -0.4764382 -0.08677132 -0.8747234 0.5187669 0.2592983 -0.2476950 629.10 \n", "1002 -1.5460305 0.43927981 0.1554110 -0.4076643 -1.3342893 -0.1228203 558.71 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(df, 2)" ] }, { "cell_type": "code", "execution_count": 259, "metadata": {}, "outputs": [], "source": [ "df <- df %>% column_to_rownames(var='PID')" ] }, { "cell_type": "code", "execution_count": 282, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
outcome
1001629.10
1002558.71
1003583.89
1004482.18
1005403.61
1006512.62
\n" ], "text/latex": [ "\\begin{tabular}{r|l}\n", " & outcome\\\\\n", "\\hline\n", "\t1001 & 629.10\\\\\n", "\t1002 & 558.71\\\\\n", "\t1003 & 583.89\\\\\n", "\t1004 & 482.18\\\\\n", "\t1005 & 403.61\\\\\n", "\t1006 & 512.62\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | outcome | \n", "|---|---|---|---|---|---|\n", "| 1001 | 629.10 | \n", "| 1002 | 558.71 | \n", "| 1003 | 583.89 | \n", "| 1004 | 482.18 | \n", "| 1005 | 403.61 | \n", "| 1006 | 512.62 | \n", "\n", "\n" ], "text/plain": [ " outcome\n", "1001 629.10 \n", "1002 558.71 \n", "1003 583.89 \n", "1004 482.18 \n", "1005 403.61 \n", "1006 512.62 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df %>% select(outcome) %>% head" ] }, { "cell_type": "code", "execution_count": 292, "metadata": {}, "outputs": [], "source": [ "n <- nrow(df)\n", "pred <- numeric(n)\n", "for (i in 1:n) {\n", " y <- df$outcome\n", " x <- df[, -ncol(df)]\n", " \n", " r <- abs(cor(y[-i], x[-i,]))\n", " idx <- order(desc(r))[1:5]\n", " \n", " data <- data.frame(y=y, x=x[, idx])\n", " model <- lm(y ~ ., data=data[-i,])\n", " pred[i] <- predict(model, data[i,])\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Plot a scatter plot with a linear regression curve for predicted (y) versus observed (x) values using `ggplot2`" ] }, { "cell_type": "code", "execution_count": 293, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.1 <- data.frame(observed=df$outcome, predicted=pred)" ] }, { "cell_type": "code", "execution_count": 298, "metadata": {}, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAYAAAB91L6VAAAEDWlDQ1BJQ0MgUHJvZmlsZQAA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\nowAAAABJRU5ErkJggg==", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ggplot(df.1, aes(x=observed, y=predicted)) +\n", "geom_point() +\n", "geom_smooth(method='lm')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 2 }