gene_map: Plot heatmap of metagene matrix
In hjunwoo/ccfindR: Cancer Clone Finder

Description Usage Arguments Details Value Examples

Generate heatmap of metagenes derived from factorization of count data.

1
2
3

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

`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`.

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.

NULL

 
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)