run_edgeR: Cluster-specific DE analysis using 'edgeR'

Description Usage Arguments Details Author(s)

View source: R/run_edgeR.R

Description

run_edgeR tests for cluster-specific differential expression by aggregating single-cell measurements and using edgeR for testing.

Usage

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run_edgeR(x, pb, design, contrast = NULL, coef = NULL,
  min_cells = 10, verbose = TRUE)

Arguments

x

a [SingleCellExperiment]{SingleCellExperiment}.

pb

a named list of pseudo-bulk data for each cluster computed with aggregateData.

design

a design matrix with row and column names created with model.matrix.

contrast

a matrix of contrasts created with makeContrasts.

coef

passed to glmQLFTest. Ignored if contrast is not NULL.

min_cells

a numeric. Specifies the minimum number of cells in a given cluster-sample required to consider the sample for differential testing.

verbose

logical. Should information on progress be reported?

method

a character string (see details).

Details

run_edgeR tests for cluster-specific differential expression by aggregating single-cell measurements. Depending on the selected method, differential testing is performed on pseudo-bulk data obtained via...

raw_counts

summing, for every gene, raw counts for each cluster-sample.

normed_counts

summing, for every gene, normalized counts for each cluster-sample.

scaled_cpm

summing, for every gene, scaled CPM for each cluster-sample. Scaled CPM are obtained by multiplying pseudo-bulk raw counts by effective library sizes and dividing by 1M.

Author(s)

Helena Lucia Crowell [email protected]


HelenaLC/ddSingleCell documentation built on Dec. 7, 2018, 7:54 a.m.