View source: R/GSEPD_DEGHeatmap.R
GSEPD_DEGHeatmap | R Documentation |
Generates a gene-by-subject heatmap plot of differentially expressed genes.
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
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