Mercurial > repos > proteore > proteore_heatmap_visualization

annotate heatmap_viz.R @ 1:4651551b48e4 draft

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planemo upload commit dd5bd9c90796d9d9a0f62992bb5ca33b0efe6a05-dirty
author proteore
date Wed, 12 Sep 2018 09:37:26 -0400
parents aa0b2692ab8a
children 99207b432ebc
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1 #!/usr/bin/Rscript
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2
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3 suppressMessages(library('plotly'))
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4 suppressMessages(library('heatmaply'))
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5
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6 #packageVersion('plotly')
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7
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8 get_args <- function(){
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9
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10 ## Collect arguments
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11 args <- commandArgs(TRUE)
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12
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13 ## Default setting when no arguments passed
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14 if(length(args) < 1) {
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15 args <- c("--help")
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16 }
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17
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18 ## Help section
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19 if("--help" %in% args) {
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20 cat("Pathview R script
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21 Arguments:
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22 --help Print this test
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23 --input path of the input file (must contains a colum of uniprot and/or geneID accession number)
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24 --output Output file
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25 --type type of output file, could be html, pdf, jpg or png
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26 --cols Columns to use for heatmap, exemple : '3:8' to use columns from the third to the 8th
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27 --row_names Column which contains row names
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28 --header True or False
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29 --col_text_angle Angle of columns label ; from -90 to 90 degres
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30
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31 Example:
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32 ./heatmap_viz.R --input='dat.nucl.norm.imputed.tsv' --output='heatmap.html' --cols='3:8' --row_names='2' --header=TRUE --col_text_angle=0 \n\n")
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33
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34 q(save="no")
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35 }
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36
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37 parseArgs <- function(x) strsplit(sub("^--", "", x), "=")
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38 argsDF <- as.data.frame(do.call("rbind", parseArgs(args)))
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39 args <- as.list(as.character(argsDF$V2))
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40 names(args) <- argsDF$V1
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41
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42 return(args)
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43 }
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44
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45 read_file <- function(path,header){
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46 file <- try(read.table(path,header=header, sep="\t",stringsAsFactors = FALSE, quote="",fill=TRUE),silent=TRUE)
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47 if (inherits(file,"try-error")){
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48 stop("File not found !")
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49 }else{
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50 return(file)
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51 }
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52 }
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53
1
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54 #convert a string to boolean
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55 str2bool <- function(x){
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56 if (any(is.element(c("t","true"),tolower(x)))){
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57 return (TRUE)
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58 }else if (any(is.element(c("f","false"),tolower(x)))){
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59 return (FALSE)
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60 }else{
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61 return(NULL)
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62 }
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63 }
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64
1
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65 #remove remaining quote
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66 #remove lines with at least one empty cell in a matrix between two defined columns
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67 clean_df <- function(mat,first_col,last_col,rownames){
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68 tmp = mat[,first_col:last_col]
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69 tmp <- as.data.frame(apply(tmp,c(1,2),function(x) {ifelse(is.character(x),as.numeric(x),x)}))
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70 bad_lines <- which(apply(tmp, 1, function(x) any(is.na(x))))
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71 mat <- cbind(mat[,as.numeric(rownames)],tmp)
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72 if (length(bad_lines) > 0) {
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73 mat <- mat[- bad_lines,]
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74 print(paste("lines",bad_lines, "has been removed: at least one non numeric content"))
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75 }
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76 return(mat)
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77 }
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78
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79 #get args
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80 args <- get_args()
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81
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82 header=str2bool(args$header)
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83 output <- rapply(strsplit(args$output,"\\."),c) #remove extension
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84 output <- paste(output[1:length(output)-1],collapse=".")
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85 output <- paste(output,args$type,sep=".")
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86 first_col=as.numeric(substr(args$cols,1,1))
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87 last_col=as.numeric(substr(args$cols,3,3))
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88
1
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89 #cleaning data
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90 uto <- read_file(args$input,header = header)
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91 uto <- clean_df(uto,first_col,last_col,args$row_names)
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92 data <- as.data.frame(uto[,-1])
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93 row_names = uto[,1]
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94 if (header) {
1
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95 col_names = names(data)
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96 } else {
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97 col_names = c(first_col:last_col)
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98 }
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99
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100 #building heatmap
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101 heatmaply(data, file=output, margins=c(100,50,NA,0), plot_method="plotly", labRow = row_names, labCol = col_names,
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102 grid_gap = 0,cexCol = 1, column_text_angle = as.numeric(args$col_text_angle), width = 1000, height=1000, colors = c('blue','green','yellow','red'))
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103
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104
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105 ####heatmaply
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106
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107 simulateExprData <- function(n, n0, p, rho0, rho1){
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108 # n: total number of subjects
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109 # n0: number of subjects with exposure 0
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110 # n1: number of subjects with exposure 1
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111 # p: number of genes
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112 # rho0: rho between Z_i and Z_j for subjects with exposure 0
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113 # rho1: rho between Z_i and Z_j for subjects with exposure 1
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114
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115 # Simulate gene expression values according to exposure 0 or 1,
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116 # according to a centered multivariate normal distribution with
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117 # covariance between Z_i and Z_j being rho^|i-j|
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118 n1 <- n - n0
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119 times <- 1:p
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120 H <- abs(outer(times, times, "-"))
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121 V0 <- rho0^H
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122 V1 <- rho1^H
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123
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124 # rows are people, columns are genes
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125 genes0 <- MASS::mvrnorm(n = n0, mu = rep(0,p), Sigma = V0)
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126 genes1 <- MASS::mvrnorm(n = n1, mu = rep(0,p), Sigma = V1)
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127 genes <- rbind(genes0,genes1)
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128 return(genes)
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129 }
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130
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131 #genes <- simulateExprData(n = 50, n0 = 25, p = 100, rho0 = 0.01, rho1 = 0.95)
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132
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133 #heatmaply(genes, k_row = 2, k_col = 2)
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134
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135 #heatmaply(cor(genes), k_row = 2, k_col = 2)
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136
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137
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138
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139
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140
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141
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142