Heatmap showing the expression or activity of selected features by self-defined groups

This function is used to draw a heatmap of selected features among self-defined groups from a sparse eset object. By default, the groups are sorted by size, from largest to smallest. Within each group, the cells are sorted alphabetically.

Usage

feature_heatmap(
  input_eset,
  features = NULL,
  group_by = "clusterID",
  scale_method = "none",
  annotation_columns = NULL,
  use_gaps.column = FALSE,
  cluster_rows = FALSE,
  show_rownames = TRUE,
  fontsize.row = 10,
  use_gaps.row = FALSE
)

Arguments

input_eset

The expression set object that filtered, normalized and log-transformed

features

A vector of genes or drivers (row.names of the input eset) to plot

group_by

Character, name of the column for grouping, usually the column of cell types or clusters. Default: "clusterID".

scale_method

Character, method for data scaling: "none" (the default), "column", "row".

annotation_columns

Character, name(s) of the column(s) to add for cell annotation. Default: NULL.

use_gaps.column

Logical, whether to put a gap between cell groups. Default: FALSE.

cluster_rows

Logical, whether to cluster the rows. If TRUE, the rows will be clustered. If FALSE, the rows are displays following the order in 'features'. Default: FALSE.

show_rownames

Logical, whether to show the rownames. Default: TRUE.

fontsize.row

Numeric, font size of the rownames. Defualt: 10.

use_gaps.row

Logical, whether to put a gap between features. Default: FALSE.

Value

Print the heatmap to screen

Examples

data(pbmc14k_expression.eset)
features_of_interest <- c("CD3D","CD27","IL7R","SELL","CCR7","IL32","GZMA","GZMK",
                          "DUSP2","CD8A","GZMH","GZMB","CD79A","CD79B","CD86","CD14")

## 1. the most commonly used command
feature_heatmap(input_eset = pbmc14k_expression.eset,
                features = features_of_interest,
                group_by = "clusterID")


## 2. add one more column ('true_label') for cell annotation
feature_heatmap(input_eset = pbmc14k_expression.eset,
                features = features_of_interest,
                group_by = "clusterID",
                annotation_columns = c("trueLabel"))


## 3. scale the data by row
feature_heatmap(input_eset = pbmc14k_expression.eset,
                features = features_of_interest,
                group_by = "clusterID",
                scale_method = "row")


## 4. cluster the rows
feature_heatmap(input_eset = pbmc14k_expression.eset,
                features = features_of_interest,
                group_by = "clusterID",
                cluster_rows = TRUE)


## 5. add gaps
feature_heatmap(input_eset = pbmc14k_expression.eset,
                features = features_of_interest,
                group_by = "clusterID",
                use_gaps.column = TRUE,
                use_gaps.row = TRUE)