gene_map: Plot heatmap of metagene matrix

Description Usage Arguments Details Value Examples

View source: R/utils.R

Description

Generate heatmap of metagenes derived from factorization of count data.

Usage

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gene_map(object, rank, markers = NULL, subtract.mean = TRUE,
  log = TRUE, max.per.cluster = 10, Colv = NA, gene.names = NULL,
  main = "Genes", col = NULL, ...)

Arguments

object

Object of class scNMFSet.

rank

Rank value for which the gene map is to be displayed. The object must contain the corresponding slot (one element of basis(object)[[k]] for which ranks(object)[[k]]==rank.

markers

Vector of gene names containing markers to be included in addition to the metagenes. All entries of rowData(object) matching them will be added to the metagene list.

subtract.mean

Process each rows of basis matrix W by standardization using the mean of elements within the row.

log

If TRUE, subtract.mean uses geometric mean and division. Otherwise, use arithmetic mean and subtraction.

max.per.cluster

Maximum number of metagenes per cluster.

Colv

NA suppresses reordering and dendrogram of clusters along the column. See heatmap.

gene.names

Names to be used in the plot for genes.

main

Title of plot.

col

Colors for the cluster panels on the left and top.

...

Other arguments to be passed to heatmap, image, and plot.

Details

Wrapper for heatmap to display metagenes and associated basis matrix element magnitudes. Factorization results inside an object specified by its rank value will be retrieved, and metagene sets identified from clusters.

If object contains multiple ranks, only the requested rank's basis matrix W will be displayed. The genes displayed in rows are selected by "max" scheme [Carmona-Saez, BMC Bioinformatics (2006), https://doi.org/10.1186/1471-2105-7-54]: for each cluster (k in 1:ncol), rows of W are sorted by decreasing order of W[,k]. Marker genes for k are those among the top nmarker for which W[,k] is maximum within each row.

Value

NULL

Examples

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set.seed(1)
x <- simulate_data(nfeatures=10,nsamples=c(20,20,60))
rownames(x) <- seq_len(10)
colnames(x) <- seq_len(100)
s <- scNMFSet(count=x,rowData=seq_len(10), colData=seq_len(100))
s <- vb_factorize(s,ranks=seq(2,5))
plot(s)
gene_map(s, rank=3)

ccfindR documentation built on Nov. 8, 2020, 5:12 p.m.