This function processes the ouput of yaConsensus and acts as a wrapper to the pheatmap function.
# S3 method for yaConsensus
plot(x, G = 2,
annotation = NULL, annotation.colorCode = NULL,
matching_clustering = NULL, consensus_colors = NULL,
reduce_to = -1, main = NULL, ...)
A named list with the following slots:
a data frame. It is the same given in input, with 'consensus' and 'consensus.col' more variables.
a named list of colors associated with each variable in the annotation data-frame.
an object of hclust clust. It's the result of the hclust() applied to the consensus dissimilarity with the complete linkage.
see Note
an object coming from yaConsensus().
an integer value indicating the number of clusters required for the consensus clustering. Default is 2.
a data frame where the variables are annotations (as labels) of samples. The row-names have to match the names of the samples.
a string named list of color names. The names have to be values stored in the annotation data-frame.
a string value matching one of the annotation valiables in the annotation data-frame. The function tries to match at best the color coding of the selected variable in the data-frame.
a list of color provided to annotate the consensus clustering. If provided, the matching_clustering is overrided.
an integer value; default = -1 means all samples are included in the graphical representation; a value greater than 10 forces the graphical output to show 'reduce_to' number of brahch of the consensusu dendrogram. See also detail.
a main title for the plot; default is NULL (no title).
parameters compatible with pheatmap function.
Stefano M. Pagnotta
In the slot 'statistics', the function returns the same statistics of yaConsensus().
If the consensus analysis concerns single-cell, the'reduce_to' parameter has to be set to 500 (suggestion), given that the number of cells is often thousands. In this case, the original annotation is compressed to the number of items displayed. If the consensus color annotation has to match an existing a-priori coloring code, the match is computed concerning the unreduced annotation.
Risso and Pagnotta (2021) - Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles - Bioinformatics, btab091, <DOI: 10.1093/bioinformatics/btab091>
pheatmap::pheatmap(), yaConsensus
# see the examples in yaConsensus help.
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