heatmap_data_BUSseq {BUSseq}R Documentation

Draw the Heatmap of the Log-scale Read Count Data for the Output of the BUSseq_MCMC Function

Description

Plot the heatmap of the log-scale read count data across multiple batches, and then save the resulting images in the user's directory as "png" format.

Usage

heatmap_data_BUSseq(sce_BUSseqfit, 
                    data_type = c("Raw","Imputed","Corrected"), 
                    gene_set = NULL,
                    project_name= paste0("BUSseq_heatmap_",data_type), 
                    image_dir = NULL, color_key_seq = NULL, 
                    image_width = 1440, image_height = 1080)

Arguments

sce_BUSseqfit

An output SingleCellExperiment object obtained from the function BUSseq_MCMC.

data_type

A string to determine which count data matrix is used to draw the heatmap, "Raw" for the raw count data, "Imputed" for the imputed data, and "Corrected" for the corrected data.

gene_set

A vector of gene indices indicating the gene set of interest to display in the heatmap. The default is all genes. We also recommend displaying the intrinsic genes obtained from intrisic_genes_BUSseq(BUSseqfits_obj).

project_name

A string to name the "png" image. By default, the figure is named as "BUSseq_heatmap_Raw_log1p_data.png."

image_dir

A directory to store the gnereated heatmap. The default is to create a folder called "image" in the current directory and save there.

color_key_seq

A numeric vector indicating the splitting points for binning log-scale read counts into colors. The default is to space the color key points equally between the minimum and maximum of the log-scale read count data.

image_width

The width in pixels of the graphical device to plot. The default is 1440 px.

image_height

The height in pixels of the graphical device to plot. The default is 1080 px.

Details

To cope with the zeros in the count data, we take the transformation log(1+x) on all count data, which corresponds to the R function log1p() instead of log().

Value

Visualize the gene expression data matrix, where each row represents a gene, and each column represents a sample.

Author(s)

Fangda Song

References

Song, Fangda, Ga Ming Angus Chan, and Yingying Wei. Flexible experimental designs for valid single-cell RNA-sequencing experiments allowing batch effects correction. Nature communications 11, no. 1 (2020): 1-15.

Examples

  library(SingleCellExperiment)
  # Plot the imputed read count data of the first 100 genes
  heatmap_data_BUSseq(BUSseqfits_example, data_type = "Imputed",
                      gene_set = 1:100)

[Package BUSseq version 1.0.0 Index]