qSVA: A wrapper function used to perform qSVA in one step.

In LieberInstitute/qsvaR: Generate Quality Surrogate Variable Analysis for Degradation Correction

A wrapper function used to perform qSVA in one step.

Description

A wrapper function used to perform qSVA in one step.

Usage

qSVA(rse_tx, sig_transcripts = select_transcripts(), mod, assayname)

Arguments

rse_tx	A RangedSummarizedExperiment-class object containing the transcript data desired to be studied.
sig_transcripts	A character() vector of transcripts that should be associated with degradation, expected to be present in rownames(rse_tx).
mod	Model Matrix with necessary variables the you would model for in differential expression.
assayname	character string specifying the name of the assay desired in rse_tx

Value

matrix with k principal components for each sample

Examples

## First we need to define a statistical model. We'll use the example
## rse_tx data. Note that the model you'll use in your own data
## might look different from this model.
mod <- model.matrix(~ mitoRate + Region + rRNA_rate + totalAssignedGene + RIN,
    data = colData(rse_tx)
)

## To ensure that the results are reproducible, you will need to set a
## random seed with the set.seed() function. Internally, we are using
## sva::num.sv() which needs a random seed to ensure reproducibility of the
## results.
set.seed(20230621)
qSVA(rse_tx = rse_tx, mod = mod, assayname = "tpm")

LieberInstitute/qsvaR documentation built on Dec. 14, 2024, 10:30 p.m.