View source: R/plotZenithResults.R
plotZenithResults | R Documentation |
Heatmap of zenith results showing genesets that have the top and bottom t-statistics from each assay.
plotZenithResults(
df,
ntop = 5,
nbottom = 5,
label.angle = 45,
zmax = NULL,
transpose = FALSE,
sortByGeneset = TRUE
)
df |
result |
ntop |
number of gene sets with highest t-statistic to show |
nbottom |
number of gene sets with lowest t-statistic to show |
label.angle |
angle of x-axis label |
zmax |
maxium of the color scales. If not specified, used range of the observed t-statistics |
transpose |
transpose the axes of the plot |
sortByGeneset |
use hierarchical clustering to sort gene sets. Default is TRUE |
Heatmap showing enrichment for gene sets and cell types
# 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 zenith analysis
res.gsa = zenith_gsa(fit, gs, 'gendermale', progressbar=FALSE )
# Show top gene sets
head(res.gsa, 2)
# 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)
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