plotCorrelationHeatmap | R Documentation |
Heatmap
plotCorrelationHeatmap(object, ...)
plotHeatmap(object, ...)
plotQuantileHeatmap(object, ...)
## S4 method for signature 'DESeqAnalysis'
plotHeatmap(object, ...)
## S4 method for signature 'DESeqAnalysis'
plotCorrelationHeatmap(object, ...)
## S4 method for signature 'DESeqAnalysis'
plotQuantileHeatmap(object, ...)
object |
Object. |
... |
Additional arguments. |
Plot.
Here we're scaling simply by calculating the standard score (z-score).
mu: mean.
sigma: standard deviation.
x: raw score (e.g. count matrix).
z: standard score (z-score).
z = (x - mu) / sigma
See also:
pheatmap:::scale_rows()
.
scale()
for additional scaling approaches.
Row- and column-wise hierarchical clustering is performed when clusterRows
and/or clusterCols
are set to TRUE
. Internally, this calls hclust()
,
and defaults to the Ward method.
Automatic hierarchical clustering of rows and/or columns can error for some datasets. When this occurs, you'll likely see this error:
Error in hclust(d, method = method) : NA/NaN/Inf in foreign function call
In this case, either set clusterRows
and/or clusterCols
to FALSE
, or
you can attempt to pass an hclust
object to these arguments. This is
recommended as an alternate approach to be used with pheatmap()
, which is
called internally by our plotting code. Here's how this can be accomplished:
mat <- assay(mat) dist <- dist(mat) hclust <- hclust(dist, method = "ward.D2")
Updated 2021-03-15.
Michael Steinbaugh, Rory Kirchner
pheatmap::pheatmap()
.
RColorBrewer::brewer.pal()
.
stats::cor()
.
stats::hclust()
.
data(deseq)
## DESeqAnalysis ====
plotHeatmap(deseq)
plotCorrelationHeatmap(deseq)
plotQuantileHeatmap(deseq)
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