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Heatmap

Usage

plotCorrelationHeatmap(object, ...)

plotHeatmap(object, ...)

plotQuantileHeatmap(object, ...)

# S4 method for DESeqAnalysis
plotHeatmap(object, ...)

# S4 method for DESeqAnalysis
plotCorrelationHeatmap(object, ...)

# S4 method for DESeqAnalysis
plotQuantileHeatmap(object, ...)

Arguments

object

Object.

...

Additional arguments.

Value

Plot.

Note

Updated 2021-03-15.

Scaling

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).

See also:

  • pheatmap:::scale_rows().

  • scale() for additional scaling approaches.

Hierarchical clustering

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:

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")

Author

Michael Steinbaugh, Rory Kirchner

Examples

data(deseq)

## DESeqAnalysis ====
plotHeatmap(deseq)
#>  Using `DESeqTransform` `varianceStabilizingTransformation` counts.
#> → Scaling matrix per row (z-score).
#> → Performing hierarchical clustering with `hclust()` method "ward.D2".
#> ! `hclust()` row calculation failed.

plotCorrelationHeatmap(deseq)
#>  Using `DESeqTransform` `varianceStabilizingTransformation` counts.
#> → Calculating correlation matrix using `pearson` method.

plotQuantileHeatmap(deseq)
#>  Using `DESeqTransform` `varianceStabilizingTransformation` counts.