Mercurial > repos > proteore > proteore_go_terms_profiles_comparison

comparison GO_prof_comp.R @ 0:fe80e3b6b5c2 draft default tip

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planemo upload commit b8671ffe2e12dc6612b971a3e6e1dc71496aefd0-dirty
author proteore
date Fri, 24 Jan 2020 10:34:33 -0500
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-1:000000000000 0:fe80e3b6b5c2
1 options(warn=-1) #TURN OFF WARNINGS !!!!!!
2 suppressMessages(library(clusterProfiler,quietly = TRUE))
3 suppressMessages(library(plyr, quietly = TRUE))
4 suppressMessages(library(ggplot2, quietly = TRUE))
5 suppressMessages(library(DOSE, quietly = TRUE))
6
7 #return the number of character from the longest description found (from the 10 first)
8 max_str_length_10_first <- function(vector){
9 vector <- as.vector(vector)
10 nb_description = length(vector)
11 if (nb_description >= 10){nb_description=10}
12 return(max(nchar(vector[1:nb_description])))
13 }
14
15 str2bool <- function(x){
16 if (any(is.element(c("t","true"),tolower(x)))){
17 return (TRUE)
18 }else if (any(is.element(c("f","false"),tolower(x)))){
19 return (FALSE)
20 }else{
21 return(NULL)
22 }
23 }
24
25 get_args <- function(){
26
27 ## Collect arguments
28 args <- commandArgs(TRUE)
29
30 ## Default setting when no arguments passed
31 if(length(args) < 1) {
32 args <- c("--help")
33 }
34
35 ## Help section
36 if("--help" %in% args) {
37 cat("Selection and Annotation HPA
38 Arguments:
39 --inputtype1: type of input (list of id or filename)
40 --inputtype2: type of input (list of id or filename)
41 --input1: input1
42 --input2: input2
43 --column1: the column number which you would like to apply...
44 --column2: the column number which you would like to apply...
45 --header1: true/false if your file contains a header
46 --header2: true/false if your file contains a header
47 --ont: ontology to use
48 --lev: ontology level
49 --org: organism db package
50 --list_name1: name of the first list
51 --list_name2: name of the second list \n")
52
53 q(save="no")
54 }
55
56 parseArgs <- function(x) strsplit(sub("^--", "", x), "=")
57 argsDF <- as.data.frame(do.call("rbind", parseArgs(args)))
58 args <- as.list(as.character(argsDF$V2))
59 names(args) <- argsDF$V1
60
61 return(args)
62 }
63
64 get_ids=function(inputtype, input, ncol, header) {
65
66 if (inputtype == "text") {
67 ids = strsplit(input, "[ \t\n]+")[[1]]
68 } else if (inputtype == "file") {
69 header=str2bool(header)
70 ncol=get_cols(ncol)
71 csv = read.csv(input,header=header, sep="\t", as.is=T)
72 ids=csv[,ncol]
73 }
74
75 ids = unlist(strsplit(as.character(ids),";"))
76 ids = ids[which(!is.na(ids))]
77
78 return(ids)
79 }
80
81 str2bool <- function(x){
82 if (any(is.element(c("t","true"),tolower(x)))){
83 return (TRUE)
84 }else if (any(is.element(c("f","false"),tolower(x)))){
85 return (FALSE)
86 }else{
87 return(NULL)
88 }
89 }
90
91 check_ids <- function(vector,type) {
92 uniprot_pattern = "^([OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2})$"
93 entrez_id = "^([0-9]+|[A-Z]{1,2}_[0-9]+|[A-Z]{1,2}_[A-Z]{1,4}[0-9]+)$"
94 if (type == "entrez")
95 return(grepl(entrez_id,vector))
96 else if (type == "uniprot") {
97 return(grepl(uniprot_pattern,vector))
98 }
99 }
100
101 #res.cmp@compareClusterResult$Description <- sapply(as.vector(res.cmp@compareClusterResult$Description), function(x) {ifelse(nchar(x)>50, substr(x,1,50),x)},USE.NAMES = FALSE)
102 fortify.compareClusterResult <- function(res.cmp, showCategory=30, by="geneRatio", split=NULL, includeAll=TRUE) {
103 clProf.df <- as.data.frame(res.cmp)
104 .split <- split
105 ## get top 5 (default) categories of each gene cluster.
106 if (is.null(showCategory)) {
107 result <- clProf.df
108 } else {
109 Cluster <- NULL # to satisfy codetools
110 topN <- function(res, showCategory) {
111 ddply(.data = res, .variables = .(Cluster), .fun = function(df, N) {
112 if (length(df$Count) > N) {
113 if (any(colnames(df) == "pvalue")) {
114 idx <- order(df$pvalue, decreasing=FALSE)[1:N]
115 } else {
116 ## for groupGO
117 idx <- order(df$Count, decreasing=T)[1:N]
118 }
119 return(df[idx,])
120 } else {
121 return(df)
122 }
123 },
124 N=showCategory
125 )
126 }
127 if (!is.null(.split) && .split %in% colnames(clProf.df)) {
128 lres <- split(clProf.df, as.character(clProf.df[, .split]))
129 lres <- lapply(lres, topN, showCategory = showCategory)
130 result <- do.call('rbind', lres)
131 } else {
132 result <- topN(clProf.df, showCategory)
133 }
134 }
135 ID <- NULL
136 if (includeAll == TRUE) {
137 result = subset(clProf.df, ID %in% result$ID)
138 }
139 ## remove zero count
140 result$Description <- as.character(result$Description) ## un-factor
141 GOlevel <- result[,c("ID", "Description")] ## GO ID and Term
142 GOlevel <- unique(GOlevel)
143 result <- result[result$Count != 0, ]
144 result$Description <- factor(result$Description,levels=rev(GOlevel[,2]))
145 if (by=="rowPercentage") {
146 Description <- Count <- NULL # to satisfy codetools
147 result <- ddply(result,.(Description),transform,Percentage = Count/sum(Count),Total = sum(Count))
148 ## label GO Description with gene counts.
149 x <- mdply(result[, c("Description", "Total")], paste, sep=" (")
150 y <- sapply(x[,3], paste, ")", sep="")
151 result$Description <- y
152
153 ## restore the original order of GO Description
154 xx <- result[,c(2,3)]
155 xx <- unique(xx)
156 rownames(xx) <- xx[,1]
157 Termlevel <- xx[as.character(GOlevel[,1]),2]
158
159 ##drop the *Total* column
160 result <- result[, colnames(result) != "Total"]
161 result$Description <- factor(result$Description, levels=rev(Termlevel))
162
163 } else if (by == "count") {
164 ## nothing
165 } else if (by == "geneRatio") { ##default
166 gsize <- as.numeric(sub("/\\d+$", "", as.character(result$GeneRatio)))
167 gcsize <- as.numeric(sub("^\\d+/", "", as.character(result$GeneRatio)))
168 result$GeneRatio = gsize/gcsize
169 cluster <- paste(as.character(result$Cluster),"\n", "(", gcsize, ")", sep="")
170 lv <- unique(cluster)[order(as.numeric(unique(result$Cluster)))]
171 result$Cluster <- factor(cluster, levels = lv)
172 } else {
173 ## nothing
174 }
175 return(result)
176 }
177
178 ##function plotting.clusteProfile from clusterProfiler pkg
179 plotting.clusterProfile <- function(clProf.reshape.df,x = ~Cluster,type = "dot", colorBy = "p.adjust",by = "geneRatio",title="",font.size=12) {
180
181 Description <- Percentage <- Count <- Cluster <- GeneRatio <- p.adjust <- pvalue <- NULL # to
182 if (type == "dot") {
183 if (by == "rowPercentage") {
184 p <- ggplot(clProf.reshape.df,
185 aes_(x = x, y = ~Description, size = ~Percentage))
186 } else if (by == "count") {
187 p <- ggplot(clProf.reshape.df,
188 aes_(x = x, y = ~Description, size = ~Count))
189 } else if (by == "geneRatio") { ##DEFAULT
190 p <- ggplot(clProf.reshape.df,
191 aes_(x = x, y = ~Description, size = ~GeneRatio))
192 } else {
193 ## nothing here
194 }
195 if (any(colnames(clProf.reshape.df) == colorBy)) {
196 p <- p +
197 geom_point() +
198 aes_string(color=colorBy) +
199 scale_color_continuous(low="red", high="blue", guide=guide_colorbar(reverse=TRUE))
200 ## scale_color_gradientn(guide=guide_colorbar(reverse=TRUE), colors = enrichplot:::sig_palette)
201 } else {
202 p <- p + geom_point(colour="steelblue")
203 }
204 }
205
206 p <- p + xlab("") + ylab("") + ggtitle(title) +
207 theme_dose(font.size)
208
209 ## theme(axis.text.x = element_text(colour="black", size=font.size, vjust = 1)) +
210 ## theme(axis.text.y = element_text(colour="black",
211 ## size=font.size, hjust = 1)) +
212 ## ggtitle(title)+theme_bw()
213 ## p <- p + theme(axis.text.x = element_text(angle=angle.axis.x,
214 ## hjust=hjust.axis.x,
215 ## vjust=vjust.axis.x))
216
217 return(p)
218 }
219
220 make_dotplot<-function(res.cmp,ontology) {
221
222 dfok<-fortify.compareClusterResult(res.cmp)
223 dfok$Description <- sapply(as.vector(dfok$Description), function(x) {ifelse(nchar(x)>50, substr(x,1,50),x)},USE.NAMES = FALSE)
224 p<-plotting.clusterProfile(dfok, title="")
225
226 #plot(p, type="dot") #
227 output_path= paste("GO_profiles_comp_",ontology,".png",sep="")
228 png(output_path,height = 720, width = 600)
229 pl <- plot(p, type="dot")
230 print(pl)
231 dev.off()
232 }
233
234 get_cols <-function(input_cols) {
235 input_cols <- gsub("c","",gsub("C","",gsub(" ","",input_cols)))
236 if (grepl(":",input_cols)) {
237 first_col=unlist(strsplit(input_cols,":"))[1]
238 last_col=unlist(strsplit(input_cols,":"))[2]
239 cols=first_col:last_col
240 } else {
241 cols = as.integer(unlist(strsplit(input_cols,",")))
242 }
243 return(cols)
244 }
245
246 #to check
247 cmp.GO <- function(l,fun="groupGO",orgdb, ontology, level=3, readable=TRUE) {
248 cmpGO<-compareCluster(geneClusters = l,
249 fun=fun,
250 OrgDb = orgdb,
251 ont=ontology,
252 level=level,
253 readable=TRUE)
254
255 return(cmpGO)
256 }
257
258 check_ids <- function(vector,type) {
259 uniprot_pattern = "^([OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2})$"
260 entrez_id = "^([0-9]+|[A-Z]{1,2}_[0-9]+|[A-Z]{1,2}_[A-Z]{1,4}[0-9]+)$"
261 if (type == "entrez")
262 return(grepl(entrez_id,vector))
263 else if (type == "uniprot") {
264 return(grepl(uniprot_pattern,vector))
265 }
266 }
267
268 main = function() {
269
270 #to get the args of the command line
271 args=get_args()
272
273
274 ids1<-get_ids(args$inputtype1, args$input1, args$column1, args$header1)
275 ids2<-get_ids(args$inputtype2, args$input2, args$column2, args$header2)
276 ont = strsplit(args$ont, ",")[[1]]
277 lev=as.integer(args$lev)
278 org=args$org
279
280 #load annot package
281 suppressMessages(library(args$org, character.only = TRUE, quietly = TRUE))
282
283 # Extract OrgDb
284 if (args$org=="org.Hs.eg.db") {
285 orgdb<-org.Hs.eg.db
286 } else if (args$org=="org.Mm.eg.db") {
287 orgdb<-org.Mm.eg.db
288 } else if (args$org=="org.Rn.eg.db") {
289 orgdb<-org.Rn.eg.db
290 }
291
292 for(ontology in ont) {
293 liste = list("l1"=ids1,"l2"=ids2)
294 names(liste) = c(args$list_name1,args$list_name2)
295 res.cmp<-cmp.GO(l=liste,fun="groupGO",orgdb, ontology, level=lev, readable=TRUE)
296 make_dotplot(res.cmp,ontology)
297 output_path = paste("GO_profiles_comp_",ontology,".tsv",sep="")
298 write.table(res.cmp@compareClusterResult, output_path, sep="\t", row.names=F, quote=F)
299 }
300
301 } #end main
302
303 main()
304