zenithPR_gsa: Gene set analysis using pre-computed test statistic

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

Gene set analysis using pre-computed test statistic

Description

Perform gene set analysis on the result of a pre-computed test statistic. Test whether statistics in a gene set are larger/smaller than statistics not in the set.

Usage

zenithPR_gsa(
  statistics,
  ids,
  geneSets,
  use.ranks = FALSE,
  n_genes_min = 10,
  progressbar = TRUE,
  inter.gene.cor = 0.01,
  coef.name = "zenithPR"
)

Arguments

statistics	pre-computed test statistics
ids	name of gene for each entry in statistics
geneSets	GeneSetCollection
use.ranks	do a rank-based test TRUE or a parametric test FALSE? default: FALSE
n_genes_min	minumum number of genes in a geneset
progressbar	if TRUE, show progress bar
inter.gene.cor	correlation of test statistics with in gene set
coef.name	name of column to store test statistic

Details

This is the same as zenith_gsa(), but uses pre-computed test statistics. Note that zenithPR_gsa() may give slightly different results for small samples sizes, if zenithPR_gsa() is fed t-statistics instead of z-statistics.

Value

• NGenes: number of genes in this set

• Correlation: mean correlation between expression of genes in this set

• delta: difference in mean t-statistic for genes in this set compared to genes not in this set

• se: standard error of delta

• p.less: p-value for hypothesis test of H0: delta < 0

• p.greater: p-value for hypothesis test of H0: delta > 0

• PValue: p-value for hypothesis test H0: delta != 0

• Direction: direction of effect based on sign(delta)

• FDR: false discovery rate based on Benjamini-Hochberg method in p.adjust

• coef.name: name for pre-computed test statistics. Default: zenithPR

See Also

zenith_gsa(), limma::cameraPR()

Examples

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

# 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 Hallmark genes from MSigDB
# use gene 'SYMBOL', or 'ENSEMBL' id
# use get_GeneOntology() to load Gene Ontology
gs = get_MSigDB("H", to="ENSEMBL")
   
# Run zenithPR analysis with a test statistic for each gene
tab = topTable(fit, coef='gendermale', number=Inf)
	
res.gsa = zenithPR_gsa(tab$t, rownames(tab), gs)

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