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Plot PCA of gene expression for an assay

Source: R/plotPCA.R
plotPCA.Rd

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

Usage

# S4 method for class '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)
#>   B cells...
#> 0.2 secs
#>   CD14+ Monocytes...
#> 0.31 secs
#>   CD4 T cells...
#> 0.24 secs
#>   CD8 T cells...
#> 0.13 secs
#>   FCGR3A+ Monocytes...
#> 0.27 secs

# 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)
#>   B cells...
#> 0.2 secs
#>   CD14+ Monocytes...
#> 0.27 secs
#>   CD4 T cells...
#> 0.21 secs
#>   CD8 T cells...
#> 0.13 secs
#>   FCGR3A+ Monocytes...
#> 0.25 secs

# Extract regression residuals
residsObj = residuals(fit)

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