R: Basic heatmap plot function for normalized counts.
plotMRheatmap
R Documentation
Basic heatmap plot function for normalized counts.
Description
This function plots a heatmap of the 'n' features with greatest variance
across rows (or other statistic).
Usage
plotMRheatmap(obj, n, norm = TRUE, log = TRUE, fun = sd, ...)
Arguments
obj
A MRexperiment object with count data.
n
The number of features to plot. This chooses the 'n' features of greatest positive statistic.
norm
Whether or not to normalize the counts - if MRexperiment object.
log
Whether or not to log2 transform the counts - if MRexperiment object.
fun
Function to select top 'n' features.
...
Additional plot arguments.
Value
plotted matrix
See Also
cumNormMat
Examples
data(mouseData)
trials = pData(mouseData)$diet
heatmapColColors=brewer.pal(12,"Set3")[as.integer(factor(trials))];
heatmapCols = colorRampPalette(brewer.pal(9, "RdBu"))(50)
#### version using sd
plotMRheatmap(obj=mouseData,n=200,cexRow = 0.4,cexCol = 0.4,trace="none",
col = heatmapCols,ColSideColors = heatmapColColors)
#### version using MAD
plotMRheatmap(obj=mouseData,n=50,fun=mad,cexRow = 0.4,cexCol = 0.4,trace="none",
col = heatmapCols,ColSideColors = heatmapColColors)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(metagenomeSeq)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: limma
Attaching package: 'limma'
The following object is masked from 'package:BiocGenerics':
plotMA
Loading required package: glmnet
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-5
Loading required package: RColorBrewer
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/metagenomeSeq/plotMRheatmap.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotMRheatmap
> ### Title: Basic heatmap plot function for normalized counts.
> ### Aliases: plotMRheatmap
>
> ### ** Examples
>
>
> data(mouseData)
> trials = pData(mouseData)$diet
> heatmapColColors=brewer.pal(12,"Set3")[as.integer(factor(trials))];
> heatmapCols = colorRampPalette(brewer.pal(9, "RdBu"))(50)
> #### version using sd
> plotMRheatmap(obj=mouseData,n=200,cexRow = 0.4,cexCol = 0.4,trace="none",
+ col = heatmapCols,ColSideColors = heatmapColColors)
> #### version using MAD
> plotMRheatmap(obj=mouseData,n=50,fun=mad,cexRow = 0.4,cexCol = 0.4,trace="none",
+ col = heatmapCols,ColSideColors = heatmapColColors)
>
>
>
>
>
>
> dev.off()
null device
1
>