make.diffexp.heatmap: Make a heatmap of the most differentially expressed genes in...

In WarrenLab/single-cell: Helper Functions For Single-Cell Analysis

Make a heatmap of the most differentially expressed genes in a cluster

Description

Given a seurat object, a table of differential expression fold changes and p-values, a cluster number, and a grouping variable name, make a heatmap that shows per-cell expression of the genes with the smallest differential expression p-values, ordered by fold change.

Usage

make.diffexp.heatmap(
  seurat,
  diff.exp,
  cluster,
  grouping.var,
  min.fc = 1,
  max.p = 0.001,
  max.features = 30
)

Arguments

seurat	A seurat object with defined clusters
diff.exp	A table of differential expression p-values and fold changes for all clusters, with columns feature, cluster, fold.change, and p.
cluster	The number of the cluster to plot expression for
grouping.var	The name of the metadata variable in seurat that cells were grouped by in differential expression analysis
min.fc	Minimum absolute value of fold change to include a feature in heatmap
max.p	Maximum differential expression p-value to include a feature in heatmap
max.features	Maximum number of features to include in the heatmap

Value

A ggplot object containing a heatmap with expression of at most max.features genes with the smallest differential expression p-values, ordered by fold change, for all cells in cluster, grouped by grouping.var.

WarrenLab/single-cell documentation built on June 10, 2022, 1:50 a.m.