GSEPD_DEGHeatmap: Differentially Expressed Genes Heatmap
In rgsepd: Gene Set Enrichment / Projection Displays

Description Usage Arguments Details Value Examples

Generates a gene-by-subject heatmap plot of differentially expressed genes.

1	GSEPD_DEGHeatmap(G)

`G`	The GSEPD master object carries sample information and gene expression data. It should have already run Process() to be eligible. Parameters regarding differential expression limits are set within the G$LIMIT list object.

After GSEPD_Process() has created differential expression tables with known filenames, this function can read those tables and make heatmap plots for a subset of genes. We use the N most significant genes, specified by the MAX_Genes_for_Heatmap parameter of the passed GSEPD object.

This function doesn't return anything. If successful, four PDF files are created. HM and HM- are all subjects from sampleMeta and finalCounts, HMS and HMS- are only those in the test groups. The hyphen indicates a smaller unlabeled figure. In each case the data is manipulated as in GSEPD_Heatmap() such that complete linkage clustering is performed on z-score normalized genes using the normalized counts directly from DESeq2::varianceStabilizingTransformation, which are displayed in the labeled figures.

  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=2000,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_DEGHeatmap(G) # all parameters automatic