Applied Bioinformatics Introduction to R, continued Bing Zhang Department of Biomedical Informatics...
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Transcript of Applied Bioinformatics Introduction to R, continued Bing Zhang Department of Biomedical Informatics...
Applied Bioinformatics
Introduction to R, continued
Bing Zhang
Department of Biomedical Informatics
Vanderbilt University
Matrix subsetting and combining
2
Task R code
Import data from a tabular file data<-read.table("GSE8671_exp.txt",head=TRUE,sep="\t")
Convert data frame to matrix data0<-as.matrix(data)
Get dimensions of the matrix dim(data0)
Select discrete rows by index data0[c(1,3,5,7,9),]
Select continuous rows by index data0[5:10,]
Select discrete columns by index data0[,c(1,3,5,7,9)]
Select continuous columns by index data0[,5:10]
Select both rows and columns by index data0[1:10,1:5]
Select one row by name data0[“1438_at”,]
Select both rows and columns by name data0[c(“1438_at”, “117_at”),c(“GSM215052”, “GSM215079”)]
Calculate variances for all rows gene_variances<-apply(data0,1,var)
Calculate means for all rows gene_means<-apply(data0,1,mean)
Combine columns (same number of rows) combined<-cbind(data0,gene_means,gene_variances)
Select rows by output of a comparison combined[gene_means>60000,]
Save your work The R environment is controlled by hidden files in the startup directory
.Rdata
.Rhistory
Save before quit > q()
Save worksapce image? [y/n/c]:
During a session > save.image()
Save your code to a file (e.g. diff.r), which can be excuted in batch $ R CMD BATCH diff.r &
&: running a program in the background
Screen output to diff.r.Rout
3
Install and load packages
CRAN packages http://cran.r-project.org/web/packages/
>6000 packages
BioConductor packages http://www.bioconductor.org/
~1000 packages for the analysis of high-throughput genomics data
4
Task R code
Install a CRAN package install.packages (“package name”)
Install a BioConductor package souce (“http://www.bioconductor.org/biocLite.R”)biocLite (“package name”)
Load a package/library library (“package name”)
Graphics in R
R has very strong graphic capacities
High quality, high reproducibility, lots of packages
On-screen graphics Works in R Gui (both Windows and Mac)
In Linux, requires X11 (windowing system for bitmap displays) in Linux
Output to a file postscript, pdf, svg
jpeg, png, tiff, …
5
Start a pdf file pdf(“gse4183_clustering.pdf”, width=10, height=15)
Generate a heatmap heatmap.plus(data3, Rowv=as.dendrogram(rhc), Colv=as.dendrogram(hc), colSideColors=ann, cexRow=0.5, cexCol=0.5, col=greenred(256))
Close the file dev.off()