Find correlation between principal components (PCs) and covariates

Usage

plotPcaCovariates(object, ...)

# S4 method for bcbioRNASeq
plotPcaCovariates(
  object,
  metrics = TRUE,
  normalized = c("tpm", "sf", "fpkm", "vst", "rlog", "tmm", "rle"),
  fdr = 0.1
)

Arguments

object

Object.

metrics

boolean. Include sample summary metrics as covariates. Defaults to include all metrics columns (TRUE), but desired columns can be specified here as a character vector.

normalized

character(1) or logical(1). Normalization method to apply:

FALSE: Raw counts. When using a tximport-compatible caller, these are length scaled by default (see countsFromAbundance argument). When using a featureCounts-compatible caller, these are integer.

tximport caller-specific normalizations:

"tpm": Transcripts per million.

Additional gene-level-specific normalizations:

TRUE / "sf": Size factor (i.e. library size) normalized counts.
See DESeq2::sizeFactors for details.
"fpkm": Fragments per kilobase per million mapped fragments.
Requires fast = FALSE in bcbioRNASeq() call and gene annotations in rowRanges() with defined width().
See DESeq2::fpkm() for details.
"vst": Variance-stabilizing transformation (log2).
Requires fast = FALSE to be set during bcbioRNASeq() call.
See DESeq2::varianceStabilizingTransformation() for more information.
"tmm": Trimmed mean of M-values.
Calculated on the fly.
See edgeR::calcNormFactors() for details.
"rle": Relative log expression transformation.
Calculated on the fly.
See relativeLogExpression() for details.
"rlog": Deprecated. Regularized log transformation (log2).
No longer calculated automatically during bcbioRNASeq() call, but may be defined in legacy objects.
See DESeq2::rlog() for details.
Note that VST is more performant and now recommended by default instead.

Note that logical(1) support only applies to counts(). Other functions in the package require character(1) and use match.arg() internally.

fdr

numeric(1). Cutoff to determine the minimum false discovery rate (FDR) to consider significant correlations between principal components (PCs) and covariates.

...

Additional arguments, passed to DEGreport::degCovariates().

Value

ggplot.

Note

Requires the DEGreport package to be installed.

Updated 2022-10-24.

Author

Lorena Pantano, Michael Steinbaugh, Rory Kirchner

Examples

data(bcb)

## bcbioRNASeq ====
if (requireNamespace("DEGreport", quietly = TRUE)) {
    plotPcaCovariates(bcb)
}
#> 
#> running pca and calculating correlations for:
#> un-scaled  data in pca;
#> pve >= 5%;
#> kendall cor