Sample correlation heatmap

Source: R/plotting.R

Compute and plot the correlation matrix across samples (or group means) using the selected normalisation measure and optional region filters.

plotCorrelationMatrix(
  qseaSet,
  regionsToOverlap = NULL,
  useGroupMeans = FALSE,
  sampleAnnotation = NULL,
  normMethod = "nrpm",
  minEnrichment = 3,
  annotationColors = NA,
  minDensity = 0,
  ...
)

Arguments

qseaSet

qseaSet. Input object containing windows and signal (beta or nrpm).

regionsToOverlap

GRanges, data.frame (coercible to GRanges), or NULL. If provided, restrict computation to windows overlapping these regions; otherwise use all windows. Default: NULL.

useGroupMeans

logical(1). If TRUE, average replicates by the group column and compute correlations on group means; if FALSE, use individual samples. Default: FALSE.

sampleAnnotation

tidyselect specification or character() or NULL. Columns from the sample table to display as annotations on the heatmap (e.g., c("tumour","patient") or bare helpers tumour, patient). Default: NULL.

normMethod

character(1). Measure to correlate. One of "nrpm" or "beta". Default: "nrpm".

minEnrichment

numeric(1). For "beta", values with enrichment < minEnrichment are set to NA before correlation. Default: 3.

annotationColors

list or NA. Optional named colour maps for annotations (passed to pheatmap), e.g. list(tumour = c(Tumour = "firebrick4", Normal = "blue")). Default: NA.

minDensity

numeric(1). Minimum CpG_density required to keep a window. Default: 0.

...

Additional arguments forwarded to pheatmap::pheatmap().

Value

A pheatmap::pheatmap object.

Details

Windows are optionally restricted by regionsToOverlap and filtered by minDensity. Correlations are computed on the chosen normMethod after any group averaging. For "beta", entries below minEnrichment are set to NA to down-weight low-enrichment windows.

Examples

data(exampleTumourNormal, package = "mesa")

# By default uses NRPM
exampleTumourNormal %>% plotCorrelationMatrix()
#> Generating table of nrpm values for 819 regions across 10 samples.
#> No empty columns to remove.
#> No empty rows to remove.


# Using beta values and adding sample annotations
exampleTumourNormal %>%
    plotCorrelationMatrix(normMethod = "beta",
        sampleAnnotation = c(tumour, patient))
#> Generating table of beta values for 819 regions across 10 samples.
#> No empty columns to remove.
#> No empty rows to remove.