Hierarchical Clustering HeatMap (heatMaplot)

This function performs hierarchical clustering on the transformed expression matrix generated with the DESeq2 package. It uses, by default, a Pearson correlation-based distance measure and complete linkage for cluster join.

heatMaplot(
  exploredds,
  clust,
  DEGlist = NULL,
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL,
  ...
)

Arguments

exploredds	object of class DESeq2::DESeqTransform().
clust	select the data to apply the distance matrix computation. If samples selected, it will be applied the stats::dist() function to the transformed count matrix to get sample-to-sample distances. If ind, it is necessary to provide the list of differentially expressed genes, for the exploredds subset.
DEGlist	List of up or down regulated gene/transcript identifiers meeting the chosen filter settings for all comparisons defined in data frames pval and log2FC.
plotly	logical: when FALSE (default), the ggplot2 plot will be returned. TRUE option returns the plotly version of the plot.
savePlot	logical: when FALSE (default), the plot will not be saved. If TRUE the plot will be saved, and requires the filePlot argument.
filePlot	file name where the plot will be saved. For more information, please consult the ggplot2::ggsave() function.
...	additional parameters for the pheatmap::pheatmap() function.

Value

returns an object of pheatmap or plotly class.

References

Raivo Kolde (2019). pheatmap: Pretty Heatmaps. R package version 1.0.12. https://CRAN.R-project.org/package=pheatmap

Examples

### Load data
targetspath <- system.file("extdata", "targets.txt", 
package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, 
format = "matrix", delim = "-")
countMatrixPath <- system.file("extdata", "countDFeByg.xls", 
package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Samples plot
exploredds <- exploreDDS(countMatrix, targets,
    cmp = cmp[[1]],
    preFilter = NULL, transformationMethod = "rlog"
)
heatMaplot(exploredds, clust = "samples", plotly = TRUE)
## Individuals genes identified in DEG analysis
### DEG analysis with `systemPipeR`
degseqDF <- systemPipeR::run_DESeq2(
    countDF = countMatrix,
    targets = targets, cmp = cmp[[1]], independent = FALSE
)
#> Warning: some variables in design formula are characters, converting to factors
DEG_list <- systemPipeR::filterDEGs(
    degDF = degseqDF,
    filter = c(Fold = 2, FDR = 10)
)
### Plot
heatMaplot(exploredds,
    clust = "ind",
    DEGlist = unique(as.character(unlist(DEG_list[[1]])))
)