Mercurial > repos > proteomisc > preprocess_dataset

comparison preprocess_datasets/Affymetrix_Preprocessing.R @ 27:b5eac045873c draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
Uploaded
author proteomisc
date Mon, 04 Dec 2023 12:41:06 +0000
parents
children 183b24bc8574
comparison
equal deleted inserted replaced
26:a085ddc4cb46 27:b5eac045873c
1 options(show.error.messages=F,warn=-1, error=function(){cat(geterrmessage(),file=stderr());q("no",1,F)})
2 sink(stdout(), type = "message")
3 sink(stdout() ,type = "output")
4 suppressWarnings(suppressMessages(library(affy)))
5 suppressWarnings(suppressMessages(library(affyPLM)))
6 suppressWarnings(suppressMessages(library(batch)))
7 suppressWarnings(suppressMessages(library(annotate)))
8 suppressWarnings(suppressMessages(library(limma)))
9 suppressWarnings(suppressMessages(library(markdown)))
10 suppressWarnings(suppressMessages(library(knitr)))
11 source_local <- function(fname){
12 argv <- commandArgs(trailingOnly = FALSE)
13 base_dir <- dirname(substring(argv[grep("--file=", argv)], 8))
14 source(paste(base_dir, fname, sep="/"))
15 }
16 file_path <- function(fname){
17 argv <- commandArgs(trailingOnly = FALSE)
18 base_dir <- dirname(substring(argv[grep("--file=", argv)], 8))
19 pato <- paste(base_dir, fname, sep="/")
20 return(pato)
21 }
22 base_dir <- function(){
23 argv <- commandArgs(trailingOnly = FALSE)
24 base_dir <- dirname(substring(argv[grep("--file=", argv)], 8))
25 return(base_dir)
26 }
27 source_local("Affymetrix_Preprocessing_Functions.R")
28 listArguments = parseCommandArgs(evaluate=FALSE)
29 thefunctions=listArguments[["thefunctions"]]
30 listArguments[["thefunctions"]]=NULL
31 h=listArguments[["h"]]
32 listArguments[["h"]]=NULL
33 w=listArguments[["w"]]
34 listArguments[["w"]]=NULL
35 if (!is.null(listArguments[["image"]])){
36 load(listArguments[["image"]])
37 listArguments[["image"]]=NULL
38 }
39 listArguments[["rawdata"]]=MicroArray_Object$affy_object
40 if(datasetsource=="intern"){
41 designo<-MicroArray_Object$designo
42 }
43
44 if(datasetsource=="extern"){
45 listArguments<-append(listArguments,list(datasetsource=datasetsource,listfullnames=listfullnames))
46 }
47 Prepro_object<-do.call(thefunctions,listArguments)
48 if(datasetsource=="extern"){
49 rownames(Prepro_object$data.bg@phenoData@data)<-listfullnames
50 colnames(exprs(Prepro_object$data.bg))<-listfullnames
51 colnames(exprs(Prepro_object$data.sm))<-listfullnames
52 colnames(exprs(Prepro_object$data.norm))<-listfullnames
53 colnames(exprs(Prepro_object$data.bg))<-listfullnames
54 colnames(exprs(Prepro_object$data.norm))<-listfullnames
55 rownames(Prepro_object$data.norm@phenoData@data)<-listfullnames
56 rownames(Prepro_object$data.norm@protocolData@data)<-listfullnames
57 }
58
59 png(filename ="boxplot_before_NM.png",width = w, height = h, units = "px", pointsize = 14, bg = "white")
60 boxplot((na.omit(as.data.frame(exprs(Prepro_object$data.bg)))), main="Boxplot of intensities before Normalization",col="red",las=2,mar=c(15,2,1,1))
61 invisible(dev.off())
62
63 png(filename ="boxplot_after_NM.png",width = w, height = h, units = "px", pointsize = 14, bg = "white")
64 boxplot((na.omit(as.data.frame(exprs(Prepro_object$data.sm)))), main="Boxplot of intensities After Normalization",col="red",ylab="(intensities)",las=2,mar=c(15,2,1,1))
65 invisible(dev.off())
66
67 png(filename ="MA_plot.png",width = w, height = h)
68 MAplot((Prepro_object$data.norm) ,
69 show.statistics = F, span = 2/3, family.loess = "gaussian",
70 cex = 2, plot.method = as.character("smoothScatter"),
71 azdd.loess = TRUE, lwd = 1, lty = 1, loess.col = "red")
72
73 invisible(dev.off())
74
75 png(filename = "densities_plot_before_NM.png",width = w, height = h)
76 plotDensities(exprs(Prepro_object$data.bg),log=T)
77 invisible(dev.off())
78
79 png(filename = "densities_plot_after_NM.png",width = w, height = h)
80 plotDensities(exprs(Prepro_object$data.norm),log=T)
81 invisible(dev.off())
82
83 AffymetrixRmd=file_path("Affymetrix.Rmd")
84 Style=file_path("look.css")
85 suppressWarnings(suppressMessages(markdownToHTML(AffymetrixRmd,output="PreprocessingPlots.html",quiet = T, stylesheet=Style)))
86 #suppressWarnings(suppressMessages(knit2html(AffymetrixRmd,output="PreprocessingPlots.html",quiet = T)))
87 rm(listArguments)
88 save.image("MicroArray.Preprocessing.RData")
89 sink()
90 sink()