zenith_gsa-methods: Perform gene set analysis using zenith

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

Perform gene set analysis using zenith

Description

Perform a competitive gene set analysis accounting for correlation between genes.

Usage

## S4 method for signature 'dreamletResult,GeneSetCollection'
zenith_gsa(
  fit,
  geneSets,
  coefs,
  use.ranks = FALSE,
  n_genes_min = 10,
  inter.gene.cor = 0.01,
  progressbar = TRUE,
  ...
)

## S4 method for signature 'dreamlet_mash_result,GeneSetCollection'
zenith_gsa(
  fit,
  geneSets,
  coefs,
  use.ranks = FALSE,
  n_genes_min = 10,
  inter.gene.cor = 0.01,
  progressbar = TRUE,
  ...
)

Arguments

fit	results from dreamlet()
geneSets	GeneSetCollection
coefs	coefficients to test using topTable(fit, coef=coefs[i])
use.ranks	do a rank-based test TRUE or a parametric test FALSE? default: FALSE
n_genes_min	minimum number of genes in a geneset
inter.gene.cor	if NA, estimate correlation from data. Otherwise, use specified value
progressbar	if TRUE, show progress bar
...	other arguments

Details

This code adapts the widely used camera() analysis \insertCitewu2012camerazenith in the limma package \insertCiteritchie2015limmazenith to the case of linear (mixed) models used by variancePartition::dream().

Value

data.frame of results for each gene set and cell type

data.frame of results for each gene set and cell type

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)

# Differential expression analysis within each assay,
# evaluated on the voom normalized data
res.dl <- dreamlet(res.proc, ~group_id)

# Load Gene Ontology database
# use gene 'SYMBOL', or 'ENSEMBL' id
# use get_MSigDB() to load MSigDB
library(zenith)
go.gs <- get_GeneOntology("CC", to = "SYMBOL")

# Run zenith gene set analysis on result of dreamlet
res_zenith <- zenith_gsa(res.dl, go.gs, "group_idstim", progressbar = FALSE)

# for each cell type select 3 genesets with largest t-statistic
# and 1 geneset with the lowest
# Grey boxes indicate the gene set could not be evaluted because
#    to few genes were represented
plotZenithResults(res_zenith, 3, 1)

GabrielHoffman/dreamlet documentation built on Nov. 8, 2024, 2:45 a.m.