feature_map {ccfindR} | R Documentation

## Plot heatmap of basis matrix

### Description

Generate heatmap of features derived from factorization of count data.

### Usage

    feature_map(object, basis.matrix = NULL, rank, markers = NULL,
      subtract.mean = TRUE, log = TRUE, max.per.cluster = 10,
      feature.names = NULL, perm = NULL, main = "Feature map",
      cscale = NULL, cex.cluster = 1, cex.feature = 0.5, mar = NULL,
      ...)

### Arguments

`object` | Object of class `scNMFSet`.
`basis.matrix` | Basis matrix can be supplied instead of `object`.
`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.
`feature.names` | Names to be used in the plot for features.
`perm` | Permutation of cluster IDs.
`main` | Main title.
`cscale` | Colors for heatmap.
`cex.cluster` | Cluster ID label size.
`cex.feature` | Feature ID label size.
`mar` | Margins for `graphics::par`.
`...` | Other arguments to be passed to `image`, and `plot`.

### Details

This function uses `image()` and is more flexible than `gene_map`.

If `object` contains multiple ranks, only the requested rank's basis matrix W will be displayed. As in `gene_map`, the features displayed in rows are selected by "max" scheme

### Value

`NULL`

### Examples

    set.seed(1)
    x <- simulate_data(nfeatures=10,nsamples=c(20,20,60))
    rownames(x) <- seq_len(10)

    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)
    feature_map(s, rank=3)

[Package ccfindR version 1.10.0 Index]