ct.plot.heatmap {cellTree} | R Documentation

## Gene Expression Heatmap

### Description

Plots a heatmap of gene expression, with cells ordered according to the structure computed by `compute.backbone.tree`.

### Usage

    ct.plot.heatmap(data, b.tree, log.scale = TRUE, sd.filter = 0.7,
      reorder.genes = TRUE)

### Arguments

`data` | A matrix of (non-negative) RNA-seq expression levels where each row is a gene and each column is the cell sequenced.
`b.tree` | igraph object returned by `compute.backbone.tree`.
`log.scale` | Boolean (optional). Whether the data should be log-scaled.
`sd.filter` | Numeric or `FALSE` (optional). Standard-deviation threshold below which genes should be removed from the data (no filtering if set to `FALSE`).
`reorder.genes` | Boolean (optional). Whether the gene rows should be reordered using a dendrogram of their mean value.

### Value

`data` object reordered according to the backbone tree, such as used to plot the heatmap.

### Examples

    # Load pre-computed LDA model for skeletal myoblast RNA-Seq data from HSMMSingleCell package:
    data(HSMM_lda_model)

    # Recover sampling time (in days) for each cell:
    library(HSMMSingleCell)
    data(HSMM_sample_sheet)
    days.factor = HSMM_sample_sheet$Hours
    days = as.numeric(levels(days.factor))[days.factor]

    # Compute near-optimal backbone tree:
    b.tree = compute.backbone.tree(HSMM_lda_model, days)
    # Plot heatmap:
    data(HSMM_expr_matrix)
    ct.plot.heatmap(HSMM_expr_matrix[1:2000,], b.tree, reorder.genes=FALSE)

[Package cellTree version 1.16.0 Index]