plotPCA: Plot PCA of gene expression for an assay

In GabrielHoffman/dreamlet: Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs

Plot PCA of gene expression for an assay

Description

Compute PCA of gene expression for an assay, and plot samples coloring by outlier score

Usage

## S4 method for signature 'list'
plotPCA(
  object,
  assays = names(object),
  nPC = 2,
  robust = FALSE,
  ...,
  maxOutlierZ = 20,
  nrow = 2,
  size = 2,
  fdr.cutoff = 0.05
)

Arguments

object	dreamletProcessedData from processAssays() or a list from residuals()
assays	assays / cell types to analyze
nPC	number of PCs to uses for outlier score with outlier()
robust	use robust covariance method, defaults to FALSE
...	arguments passed to MASS::cov.rob()
maxOutlierZ	cap outlier z-scores at this value for plotting to maintain consistent color scale
nrow	number of rows in plot
size	size passed to geom_point()
fdr.cutoff	FDR cutoff to determine outlier

See Also

outlierByAssay()

Examples

library(muscat)
library(SingleCellExperiment)

data(example_sce)

# create pseudobulk for each sample and cell cluster
pb <- aggregateToPseudoBulk(example_sce,
  assay = "counts",
  cluster_id = "cluster_id",
  sample_id = "sample_id",
  verbose = FALSE
)

# voom-style normalization
res.proc <- processAssays(pb, ~group_id)

# PCA to identify outliers
# from normalized expression
plotPCA( res.proc, c("B cells", "CD14+ Monocytes"))

# Run on regression residuals
#-----------------------------

# Regression analysis
fit = dreamlet(res.proc, ~ group_id)

# Extract regression residuals
residsObj = residuals(fit)

# PCA on residuals
plotPCA( residsObj, c("B cells", "CD14+ Monocytes"))

GabrielHoffman/dreamlet documentation built on May 20, 2024, 2:05 p.m.