trendHeatmap: Draw heatmap of gene expression trends

In rhondabacher/Trendy: Breakpoint analysis of time-course expression data

Draw heatmap of gene expression trends

Description

heatmap of the fitted trends

Usage

trendHeatmap(topTrendyData, featureNames = NULL, cexRow = 0.5,
  cexCol = 0.5)

Arguments

topTrendyData	results from topTrendy() function.
featureNames	names of features/genes to plot if the heatmap should be restricted. Deafult is to plot all genes from topTrendy() function.
cexRow	relative text size of row labels, default=.5.
cexCol	relative text size of column labels, default=.5.

Value

The function takes significant genes/features called from the topTrendyData() function. These genes are further grouped into three groups: up, down, or no change in the first segment. Within each group, the genes are sorted by their first break point. The heatmap shows expression trends of these three groups of genes. In the heatmap, red/blue/black represents up/down/nochange. A list of genes in the heatmap order is returned.

Author(s)

Ning Leng and Rhonda Bacher

Examples

 m1 <- matrix(c(c(rnorm(50,5,1),sort(rnorm(50, 15, 5))), rnorm(100, 50,10)), 2, 100, TRUE)
 rownames(m1) <- c("g1","g2")
 colnames(m1) <- paste0("time", seq_len(100))
 myTrends <- results(trendy(m1))
 topGenes <- topTrendy(myTrends)
 #makeHeat <- trendHeatmap(topGenes)

rhondabacher/Trendy documentation built on Oct. 26, 2023, 3:46 a.m.