GSEPD_Heatmap

Description

Plots the heatmap to the standard display. Uses heatmap.2 from gplots to display selected genes' expression level.

Usage

GSEPD_Heatmap(G,genes,cap_range=3,cellnote="log10")

Arguments

Details

Will use GSEPD$COLORFUNCTION scaled between samples of type GSEPD$Conditions in GSEPD$sampleMeta, including others in the mix. The heatmap's dendrograms (margin trees) are computed by the heatmap.2() function's default method hclust() on the supplied data, resulting in complete linkage heirarchical clustering. Because the magnitude of gene expression varies across a wide range, and we're interested in patterns more than scale, we first normalize each gene(row) by subtracting the mean, dividing by the standard deviation, and capping the min and max to the parameter cap_range=3. The heatmap function is run with no further scaling, ensuring genes with similar differential expression profiles are clustered together. The numbers written in each cell of the heatmap are simply the normalized counts directly from DESeq2::varianceStabilizingTransformation.

Value

No return value: generates a figure.

Examples

  data("IlluminaBodymap")
  data("IlluminaBodymapMeta")
  set.seed(1000) #fixed randomness
  isoform_ids <- Name_to_RefSeq(c("HIF1A","EGFR","MYH7","CD33","BRCA2"))
  rows_of_interest <- unique( c( isoform_ids ,
                                 sample(rownames(IlluminaBodymap),
                                        size=500,replace=FALSE)))
  G <- GSEPD_INIT(Output_Folder="OUT",
                finalCounts=round(IlluminaBodymap[rows_of_interest , ]),
                sampleMeta=IlluminaBodymapMeta,
                COLORS=c("green","black","red"))
  G <- GSEPD_ChangeConditions( G, c("A","B")) #set testing groups first!           
  G <- GSEPD_Process( G ) #have to have processed results to plot them
  
  GSEPD_Heatmap(G, genes=sample(rownames(G$finalCounts),8) )
  

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(rgsepd)
Loading required package: DESeq2
Loading required package: S4Vectors
Loading required package: stats4
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


Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: goseq
Loading required package: BiasedUrn
Loading required package: geneLenDataBase


Loading R/GSEPD 1.4.2
Building human gene name caches
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:1 mapping between keys and columns
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/rgsepd/GSEPD_Heatmap.Rd_%03d_medium.png", width=480, height=480)
> ### Name: GSEPD_Heatmap
> ### Title: GSEPD_Heatmap
> ### Aliases: GSEPD_Heatmap
> ### Keywords: heatmap plot
> 
> ### ** Examples
> 
> 
>   data("IlluminaBodymap")
>   data("IlluminaBodymapMeta")
>   set.seed(1000) #fixed randomness
>   isoform_ids <- Name_to_RefSeq(c("HIF1A","EGFR","MYH7","CD33","BRCA2"))
>   rows_of_interest <- unique( c( isoform_ids ,
+                                  sample(rownames(IlluminaBodymap),
+                                         size=500,replace=FALSE)))
>   G <- GSEPD_INIT(Output_Folder="OUT",
+                 finalCounts=round(IlluminaBodymap[rows_of_interest , ]),
+                 sampleMeta=IlluminaBodymapMeta,
+                 COLORS=c("green","black","red"))
Keeping rows with counts (458 of 505)
>   G <- GSEPD_ChangeConditions( G, c("A","B")) #set testing groups first!           
>   G <- GSEPD_Process( G ) #have to have processed results to plot them
converting counts to integer mode
Would generate OUT/DESEQ.counts.Ax4.Bx8.csv, but one is already present.
Loading hg19 length data...
Fetching GO annotations...
Loading required package: AnnotationDbi
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns
Loading hg19 length data...
Fetching GO annotations...
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns
Loading hg19 length data...
Fetching GO annotations...
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns
Generating OUT/GOSEQ.RES.Ax4.Bx8.GO.csv, overwriting previous existing version.
Written GO categories, now reverse mapping
Generating OUT/GSEPD.RES.Ax4.Bx8.GO2.csv, overwriting previous existing version.
Generating OUT/GSEPD.RES.Ax4.Bx8.MERGE.csv, overwriting previous existing version.
Generating OUT/GSEPD.PCA_AG.Ax4.Bx8.pdf, overwriting previous existing version.
Generating OUT/GSEPD.PCA_DEG.Ax4.Bx8.pdf, overwriting previous existing version.
Generating OUT/SCA.GSEPD.Ax4.Bx8.pdf, overwriting previous existing version.
Calculating Projections and Segregation Significance
Generating OUT/GSEPD.HMA.Ax4.Bx8.pdf, overwriting previous existing version.
Generating OUT/GSEPD.HMA.Ax4.Bx8.csv, overwriting previous existing version.
All Done!
>   
>   GSEPD_Heatmap(G, genes=sample(rownames(G$finalCounts),8) )
>   
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>