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annotate kseaapp_demo.R @ 27:2e35f925c469 draft

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planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
author eschen42
date Thu, 27 Oct 2022 18:54:25 +0000
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27
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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1 # KSEAapp demo using SQLite data source
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2 library(sqldf) # implies that package RSQLite will be available
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3 library(KSEAapp) # implies that package gplots will be available
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4
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5 # ref: http://adv-r.had.co.nz/Environments.html
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6 # "When creating your own environment, note that you should set its parent
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7 # environment to be the empty environment. This ensures you don't
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8 # accidentally inherit objects from somewhere else."
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9 # Caution: this prevents `with(my_env, expr)` from working when `expr`
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10 # contains anything from the global environment, even operators!
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11 # Hence, `x <- 1; get("x", new_env())` fails by design.
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12 new_env <- function() {
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13 new.env(parent = emptyenv())
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14 }
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15
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16 # Adapted from KSEAapp::KSEA.Heatmap to allow specification of output file
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17 ksea_heatmap <-
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18 function(
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19 # the data frame outputs from the KSEA.Scores() function, in list format
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20 score_list,
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21 # a character vector of all the sample names for heatmap annotation:
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22 # - the names must be in the same order as the data in score_list
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23 # - please avoid long names, as they may get cropped in the final image
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24 sample_labels,
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25 # character string of either "p.value" or "FDR" indicating the data column
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26 # to use for marking statistically significant scores
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27 stats,
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28 # a numeric value between 0 and infinity indicating the min. number of
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29 # substrates a kinase must have to be included in the heatmap
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30 m_cutoff,
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31 # a numeric value between 0 and 1 indicating the p-value/FDR cutoff
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32 # for indicating significant kinases in the heatmap
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33 p_cutoff,
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34 # a binary input of TRUE or FALSE, indicating whether or not to perform
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35 # hierarchical clustering of the sample columns
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36 sample_cluster,
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37 # a binary input of TRUE or FALSE, indicating whether or not to export
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38 # the bar plot as a .tiff image into the working directory
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39 export = FALSE
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40 ) {
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41 filter_m <- function(dataset, m_cutoff) {
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42 filtered <- dataset[(dataset$m >= m_cutoff), ]
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43 return(filtered)
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44 }
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45 score_list_m <- lapply(score_list, function(...)
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46 filter_m(...,
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47 m_cutoff))
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48 for (i in seq_len(length(score_list_m))) {
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49 names <- colnames(score_list_m[[i]])[c(2:7)]
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50 colnames(score_list_m[[i]])[c(2:7)] <-
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51 paste(names, i, sep = ".")
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52 }
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53 master <-
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54 Reduce(function(...)
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55 merge(..., by = "Kinase.Gene", all = F), score_list_m)
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56 #ACE if (is.null(master)) return ("No significant kinase genes found")
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57 row.names(master) <- master$Kinase.Gene
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58 columns <- as.character(colnames(master))
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59 merged_scores <- as.matrix(master[, grep("z.score", columns)])
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60 colnames(merged_scores) <- sample_labels
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61 merged_stats <- as.matrix(master[, grep(stats, columns)])
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62 asterisk <- function(matrix) {
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63 new <- data.frame()
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64 for (i in seq_len(nrow(matrix))) {
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65 for (j in seq_len(ncol(matrix))) {
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66 if (matrix[i, j] < p_cutoff) {
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67 new[i, j] <- "*"
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68 }
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69 else {
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70 new[i, j] <- ""
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71 }
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72 }
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73 }
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74 return(new)
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75 }
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76 merged_asterisk <- as.matrix(asterisk(merged_stats))
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77 create_breaks <- function(merged_scores) {
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78 if (min(merged_scores) < -1.6) {
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79 breaks_neg <- seq(-1.6, 0, length.out = 30)
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80 breaks_neg <-
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81 append(seq(min(merged_scores), -1.6, length.out = 10), breaks_neg)
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82 breaks_neg <- sort(unique(breaks_neg))
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83 }
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84 else {
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85 breaks_neg <- seq(-1.6, 0, length.out = 30)
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86 }
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diff changeset
87 if (max(merged_scores) > 1.6) {
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88 breaks_pos <- seq(0, 1.6, length.out = 30)
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diff changeset
89 breaks_pos <-
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90 append(breaks_pos, seq(1.6, max(merged_scores), length.out = 10))
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91 breaks_pos <- sort(unique(breaks_pos))
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diff changeset
92 }
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93 else {
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94 breaks_pos <- seq(0, 1.6, length.out = 30)
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95 }
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96 breaks_all <- unique(append(breaks_neg, breaks_pos))
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97 mycol_neg <-
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98 gplots::colorpanel(n = length(breaks_neg),
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99 low = "blue",
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100 high = "white")
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101 mycol_pos <-
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102 gplots::colorpanel(n = length(breaks_pos) - 1,
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103 low = "white",
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104 high = "red")
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105 mycol <- unique(append(mycol_neg, mycol_pos))
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106 color_breaks <- list(breaks_all, mycol)
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107 return(color_breaks)
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108 }
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109 color_breaks <- create_breaks(merged_scores)
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110 plot_height <- nrow(merged_scores) ^ 0.55
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111 plot_width <- ncol(merged_scores) ^ 0.7
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112 draw_heatmap <- function() gplots::heatmap.2(
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113 merged_scores,
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114 Colv = sample_cluster,
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115 scale = "none",
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116 cellnote = merged_asterisk,
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117 notecol = "white",
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118 cexCol = 0.9,
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119 cexRow = 0.9,
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120 srtCol = 45,
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121 notecex = 1.4,
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122 col = color_breaks[[2]],
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123 density.info = "none",
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124 trace = "none",
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125 key = F,
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126 breaks = color_breaks[[1]],
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127 lmat = rbind(c(0, 3), c(2, 1), c(0, 4)),
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128 lhei = c(0.4, 9.5, 0.6),
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129 lwid = c(0.5, 3),
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130 margins = c(2, 6)
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131 )
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132 if (export == "TRUE") {
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133 png(
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134 "KSEA.Merged.Heatmap.png",
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135 width = plot_width * 300,
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136 height = plot_height * 300,
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diff changeset
137 res = 300,
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diff changeset
138 pointsize = 14
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139 )
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diff changeset
140 draw_heatmap()
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diff changeset
141 dev.off()
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142 }
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diff changeset
143 else {
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diff changeset
144 draw_heatmap()
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diff changeset
145 }
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diff changeset
146 }
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147
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148 # Adapted from KSEAapp::KSEA.Barplot to allow:
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149 # - printing main and sub titles
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150 ksea_barplot <- function(
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151 # The Kinase-Substrate dataset uploaded from the file prefaced with
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152 # "PSP&networkin_" available from github.com/casecpb/KSEA/
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153 ksdata = KSEAapp::KSData,
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154 # The experimental data file formatted as described in the
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155 # KSEAapp::KSEA.Complete() documentation
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diff changeset
156 px,
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157 # networkin = TRUE means inclusion of networkin predictions
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158 # networkin = FALSE means not to include and to ignore networkin_cutoff
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diff changeset
159 networkin,
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160 # A numeric value between 1 and infinity setting the minimum networkin score
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161 # (can be left out if networkin = FALSE)
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162 networkin_cutoff,
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163 # A numeric value between 0 and infinity indicating the min. # of substrates
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164 # a kinase must have to be included in the bar plot output
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165 m_cutoff,
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166 # A numeric value greater than zero indicating the p-value cutoff for
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167 # indicating significant kinases in the bar plot
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168 p_cutoff,
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169 # A numeric value between 0 and 1 indicating the minimum magnitude of
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170 # z-value cutoff for adding a kinase to the bar plot
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171 z_threshold = 1.0,
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172 # A binary input of TRUE or FALSE, indicating whether or not to export
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173 # the bar plot as a .tiff image into the working directory
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174 export = FALSE,
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175 # This remaing arguments are passed through to barplot
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176 main = "",
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177 sub = ""
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178 ) {
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179 if (length(grep(";", px$Residue.Both)) == 0) {
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180 new <- px
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181 colnames(new)[c(2, 4)] <- c("SUB_GENE", "SUB_MOD_RSD")
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182 new$log2_fc <- log2(abs(as.numeric(as.character(new$FC))))
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183 new <- new[complete.cases(new$log2_fc), ]
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184 }
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185 else {
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186 double <- px[grep(";", px$Residue.Both), ]
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187 residues <- as.character(double$Residue.Both)
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188 residues <- as.matrix(residues, ncol = 1)
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189 split <- strsplit(residues, split = ";")
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190 x <- sapply(split, length)
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191 single <- data.frame(
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192 Protein = rep(double$Protein, x),
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193 Gene = rep(double$Gene, x),
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194 Peptide = rep(double$Peptide,
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195 x),
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196 Residue.Both = unlist(split),
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197 p = rep(double$p,
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198 x),
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199 FC = rep(double$FC, x)
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200 )
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201 new <- px[-grep(";", px$Residue.Both), ]
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202 new <- rbind(new, single)
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203 colnames(new)[c(2, 4)] <- c("SUB_GENE", "SUB_MOD_RSD")
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204 new$log2_fc <- log2(abs(as.numeric(as.character(new$FC))))
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205 new <- new[complete.cases(new$log2_fc), ]
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206 }
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207 if (networkin == TRUE) {
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208 ksdata_filterd <- ksdata[grep("[a-z]", ksdata$Source), ]
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209 ksdata_filterd <- ksdata_filterd[
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210 (ksdata_filterd$networkin_score >= networkin_cutoff),
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211 ]
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212 }
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213 else {
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214 ksdata_filterd <- ksdata[grep("PhosphoSitePlus", ksdata$Source), ]
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215 }
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216 ksdata_dataset <- merge(ksdata_filterd, new)
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217 ksdata_dataset <- ksdata_dataset[order(ksdata_dataset$GENE), ]
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218 ksdata_dataset$uniprot_no_isoform <- sapply(
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219 ksdata_dataset$KIN_ACC_ID,
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220 function(x) unlist(strsplit(as.character(x), split = "-"))[1])
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221 ksdata_dataset_abbrev <- ksdata_dataset[, c(5, 1, 2, 16:19, 14)]
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222 colnames(ksdata_dataset_abbrev) <- c(
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223 "Kinase.Gene",
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224 "Substrate.Gene",
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225 "Substrate.Mod",
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226 "Peptide",
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227 "p",
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228 "FC",
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229 "log2FC",
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230 "Source"
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231 )
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232 ksdata_dataset_abbrev <- ksdata_dataset_abbrev[
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233 order(
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234 ksdata_dataset_abbrev$Kinase.Gene,
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235 ksdata_dataset_abbrev$Substrate.Gene,
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236 ksdata_dataset_abbrev$Substrate.Mod,
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237 ksdata_dataset_abbrev$p
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238 ),
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239 ]
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240 ksdata_dataset_abbrev <- aggregate(
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241 log2FC ~ Kinase.Gene +
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242 Substrate.Gene + Substrate.Mod + Source,
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243 data = ksdata_dataset_abbrev,
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244 FUN = mean
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245 )
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246 ksdata_dataset_abbrev <-
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247 ksdata_dataset_abbrev[order(ksdata_dataset_abbrev$Kinase.Gene), ]
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248 kinase_list <- as.vector(ksdata_dataset_abbrev$Kinase.Gene)
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249 kinase_list <- as.matrix(table(kinase_list))
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250 mean_fc <- aggregate(
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251 log2FC ~ Kinase.Gene,
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252 data = ksdata_dataset_abbrev,
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253 FUN = mean
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254 )
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255 mean_fc <- mean_fc[order(mean_fc[, 1]), ]
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256 mean_fc$m_s <- mean_fc[, 2]
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257 mean_fc$enrichment <- mean_fc$m_s / abs(mean(new$log2_fc, na.rm = T))
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258 mean_fc$m <- kinase_list
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259 mean_fc$z_score <- ((mean_fc$m_s - mean(new$log2_fc, na.rm = T)) *
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260 sqrt(mean_fc$m)) / sd(new$log2_fc, na.rm = T)
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261 mean_fc$p_value <- pnorm(-abs(mean_fc$z_score))
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262 mean_fc$fdr <- p.adjust(mean_fc$p_value, method = "fdr")
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263 mean_fc_filtered <- mean_fc[(mean_fc$m >= m_cutoff), -2]
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264 mean_fc_filtered <-
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265 mean_fc_filtered[abs(mean_fc_filtered$z_score) > z_threshold, -2]
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266 mean_fc_filtered <- mean_fc_filtered[order(mean_fc_filtered$z_score), ]
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267 plot_height <- length(mean_fc_filtered$z_score) ^ 0.55
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268 mean_fc_filtered$color <- "black"
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269 mean_fc_filtered[
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270 (mean_fc_filtered$p_value < p_cutoff) & (mean_fc_filtered$z_score < 0),
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271 ncol(mean_fc_filtered)
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272 ] <- "blue"
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273 mean_fc_filtered[
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274 (mean_fc_filtered$p_value < p_cutoff) & (mean_fc_filtered$z_score > 0),
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275 ncol(mean_fc_filtered)
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276 ] <- "red"
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277 draw_barplot <- function() {
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278 barplot(
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279 as.numeric(mean_fc_filtered$z_score),
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280 col = mean_fc_filtered$color,
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281 border = NA,
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282 xpd = F,
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283 cex.names = 0.6,
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284 cex.axis = 0.8,
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285 xlab = "Kinase z-score",
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286 names.arg = mean_fc_filtered$Kinase.Gene,
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287 horiz = T,
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288 las = 1,
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289 main = main,
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290 sub = sub
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291 )
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292 }
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293 if (export == "TRUE") {
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294 tiff(
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295 "KSEA Bar Plot.tiff",
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296 width = 6 * 300,
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297 height = 300 * plot_height,
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298 res = 300,
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299 pointsize = 13
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300 )
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301 par(mai = c(1, 1, 0.4, 0.4))
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302 draw_barplot()
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diff changeset
303 dev.off()
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diff changeset
304 }
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diff changeset
305 else {
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diff changeset
306 draw_barplot()
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diff changeset
307 }
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diff changeset
308 }
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309
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310 ksea_app_prep_db <- c(
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diff changeset
311 "mqppep.UT_Phospho_ST_Sites.sqlite",
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diff changeset
312 "mqppep.pST_Sites_NancyDu.sqlite"
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diff changeset
313 )[1]
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314
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315 db <- dbConnect(RSQLite::SQLite(), ksea_app_prep_db)
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316 contrast_metadata_df <-
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317 sqldf("select * from contrast_lvl_lvl_metadata", connection = db)
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318 rslt <- new_env()
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319 rslt$score_list <- list()
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320 rslt$name_list <- list()
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321 #ACE rslt$usable_contrast_list <- list()
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322 next_index <- 0
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323 low_fdr_print <- function(i) {
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324 #ACE i <- rslt$usable_contrast_list[[i]]
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325 if (!is.null(rslt$score_list[[i]])) {
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326 display_metadata <-
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327 contrast_metadata_df[i, c(4, 5, 6), drop = FALSE]
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328 colnames(display_metadata) <-
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329 c(paste0(contrast_metadata_df[i, 2], "_trt"),
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330 paste0(contrast_metadata_df[i, 3], "_trt"),
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diff changeset
331 "fold-change"
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332 )
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diff changeset
333 print(display_metadata, row.names = FALSE)
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diff changeset
334 #ACE print(
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335 #ACE contrast_metadata_df[i, c(2, 4, 3, 5, 6), drop = FALSE],
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diff changeset
336 #ACE row.names = FALSE)
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337 #ACE print(contrast_metadata_df[i, ])
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338 if (sum(rslt$score_list[[i]]$fdr < 0.05) > 0) {
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339 print(
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340 rslt$score_list[[i]][rslt$score_list[[i]]$fdr < 0.05, ],
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diff changeset
341 row.names = FALSE)
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342 } else {
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343 cat("No kinases enriched with FDR < 0.05\n")
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344 }
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345 }
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346 }
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347 err_na_subscr_df_const <-
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348 "missing values are not allowed in subscripted assignments of data frames"
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349
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350 for (i_cntrst in seq_len(nrow(contrast_metadata_df))) {
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351 contrast_label <- sprintf(
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352 "%s -> %s",
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353 contrast_metadata_df[i_cntrst, "b_level"],
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354 contrast_metadata_df[i_cntrst, "a_level"]
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355 )
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356 kseaapp_input <-
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357 sqldf(
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358 x = sprintf("
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359 SELECT `Protein`, `Gene`, `Peptide`, `Residue.Both`, `p`, `FC`
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360 FROM v_kseaapp_input
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361 WHERE contrast = %d
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362 ", i_cntrst
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diff changeset
363 ),
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364 connection = db
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365 )
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366 #ACE K_S_links <-
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367 #ACE KSEA.KS_table(
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368 #ACE KSEAapp::KSData,
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369 #ACE kseaapp_input,
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370 #ACE networkin = TRUE,
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371 #ACE networkin_cutoff = 2
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372 #ACE )
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373 #ACE print(sprintf("contrast = %d", i_cntrst))
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374 tryCatch(
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diff changeset
375 expr = {
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376 main_title <- (
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377 sprintf(
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
378 "Change from treatment %s to treatment %s",
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diff changeset
379 contrast_metadata_df[i_cntrst, "b_level"],
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diff changeset
380 contrast_metadata_df[i_cntrst, "a_level"]
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diff changeset
381 )
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diff changeset
382 )
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383 sub_title <- (
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diff changeset
384 sprintf(
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
385 "Trt %s = {%s}. Trt %s = {%s}.",
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
386 contrast_metadata_df[i_cntrst, "b_level"],
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diff changeset
387 gsub(
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
388 pattern = ";",
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diff changeset
389 replacement = ", ",
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diff changeset
390 x = contrast_metadata_df[i_cntrst, "b_samples"],
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diff changeset
391 fixed = TRUE
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diff changeset
392 ),
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
393 contrast_metadata_df[i_cntrst, "a_level"],
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diff changeset
394 gsub(
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
395 pattern = ";",
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diff changeset
396 replacement = "",
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diff changeset
397 x = contrast_metadata_df[i_cntrst, "a_samples"],
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diff changeset
398 fixed = TRUE
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diff changeset
399 )
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
400 )
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
401 )
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
402 ksea_barplot(
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
403 KSEAapp::KSData,
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
404 kseaapp_input,
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
405 networkin = TRUE,
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
406 networkin_cutoff = 2,
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
407 m_cutoff = 2,
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diff changeset
408 p_cutoff = 0.05,
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diff changeset
409 export = FALSE,
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diff changeset
410 main = main_title,
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diff changeset
411 sub = sub_title
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diff changeset
412 )
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diff changeset
413 },
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
414 error = function(e) {
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diff changeset
415 if (tolower(e$message) == err_na_subscr_df_const) {
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
416 cat(
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
417 sprintf("\nNo scores or plot for contrast '%s' because:\n '%s'\n",
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
418 contrast_label,
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
419 "There are too many missing or invalid values."
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
420 ),
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
421 file = stdout()
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
422 )
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
423 } else {
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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parents:
diff changeset
424 cat(
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
425 sprintf("\nNo scores or plot for contrast '%s' because:\n '%s'\n",
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
426 contrast_label,
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
427 e$message
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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parents:
diff changeset
428 ),
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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diff changeset
429 file = stdout()
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430 )
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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431 }
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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432 }
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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433 )
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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434 tryCatch(
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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435 expr = {
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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436 ksea_scores <-
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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437 KSEA.Scores(
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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438 KSEAapp::KSData,
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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439 kseaapp_input,
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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440 NetworKIN = TRUE,
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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441 NetworKIN.cutoff = 2
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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442 )
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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443
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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444 if (0 < sum(!is.nan(ksea_scores$FDR))) {
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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445 next_index <- 1 + next_index
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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446 rslt$score_list[[next_index]] <- ksea_scores
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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447 #ACE rslt$usable_contrast_list[[next_index]] <- i_cntrst
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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448 rslt$name_list[[next_index]] <- contrast_label
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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449 cat("\n", rslt$name_list[[next_index]], "\n")
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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450 low_fdr_print(next_index)
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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451 }
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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452 },
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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453 error = function(e) str(e)
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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454 )
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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455 }
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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456 if (length(rslt$score_list) > 0) {
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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457 ksea_heatmap(
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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458 # the data frame outputs from the KSEA.Scores() function, in list format
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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459 score_list = rslt$score_list,
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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460 # a character vector of all the sample names for heatmap annotation:
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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461 # - the names must be in the same order as the data in score_list
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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462 # - please avoid long names, as they may get cropped in the final image
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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463 sample_labels = rslt$name_list,
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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464 # character string of either "p.value" or "FDR" indicating the data column
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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465 # to use for marking statistically significant scores
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466 stats = c("p.value", "FDR")[2],
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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467 # a numeric value between 0 and infinity indicating the min. number of
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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468 # substrates a kinase must have to be included in the heatmap
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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469 m_cutoff = 1,
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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470 # a numeric value between 0 and 1 indicating the p-value/FDR cutoff
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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471 # for indicating significant kinases in the heatmap
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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472 p_cutoff = 0.05,
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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473 # a binary input of TRUE or FALSE, indicating whether or not to perform
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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474 # hierarchical clustering of the sample columns
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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475 sample_cluster = TRUE,
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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476 # a binary input of TRUE or FALSE, indicating whether or not to export
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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477 # the bar plot as a .tiff image into the working directory
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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478 export = FALSE
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479 )
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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480 }
2e35f925c469 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit be6750cb1d7ae18e71b60ae6f2c55e5331105c4f
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481 dbDisconnect(db)