In GabrielHoffman/zenith: Gene set analysis following differential expression using linear (mixed) modeling with dream

library(knitr)
knitr::opts_chunk$set(
  echo = TRUE,
  warning=FALSE,
  message=FALSE,
  error = FALSE,
  tidy = FALSE,
  cache = FALSE)

zenith performs gene set analysis on the result of differential expression using linear (mixed) modeling with dream by considering the correlation between gene expression traits. This package implements the camera method from the limma package proposed by Wu and Smyth (2012). zenith() is a simple extension of camera() to be compatible with linear (mixed) models implemented in dream().

Standard workflow

# Load packages
library(zenith)
library(edgeR)
library(variancePartition)
library(tweeDEseqCountData)
library(kableExtra)

# Load RNA-seq data from LCL's
data(pickrell)
geneCounts = exprs(pickrell.eset)
df_metadata = pData(pickrell.eset)

# Filter genes
# Note this is low coverage data, so just use as code example
dsgn = model.matrix(~ gender, df_metadata)
keep = filterByExpr(geneCounts, dsgn, min.count=5)

# Compute library size normalization
dge = DGEList(counts = geneCounts[keep,])
dge = calcNormFactors(dge)

# Estimate precision weights using voom
vobj = voomWithDreamWeights(dge, ~ gender, df_metadata)

# Apply dream analysis
fit = dream(vobj, ~ gender, df_metadata)
fit = eBayes(fit)

# Load get_MSigDB database, Hallmark genes
# use gene 'SYMBOL', or 'ENSEMBL' id
# use get_GeneOntology() to load Gene Ontology
msdb.gs = get_MSigDB("H", to="ENSEMBL")

# Run zenith analysis, and specific which coefficient to evaluate
res.gsa = zenith_gsa(fit, msdb.gs, 'gendermale', progressbar=FALSE )

# Show top gene sets: head(res.gsa)
kable_styling(kable(head(res.gsa), row.names=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.gsa)

Session Info

sessionInfo()

GabrielHoffman/zenith documentation built on March 10, 2024, 11:43 p.m.