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 variancePartition::dream().
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Perform gene set enrichment analysis on differential expression results from a linear mixed model fit with dream and visualize the results.
library(zenith)
library(edgeR)
library(variancePartition)
library(tweeDEseqCountData)
library(kableExtra)
# 1. Filter low-expressed genes
data(pickrell)
geneCounts = exprs(pickrell.eset)
df_metadata = pData(pickrell.eset)
dsgn = model.matrix(~ gender, df_metadata)
keep = filterByExpr(geneCounts, dsgn, min.count=5)
# 2. Compute library size normalization
dge = DGEList(counts = geneCounts[keep,])
dge = calcNormFactors(dge)
# 3. Estimate precision weights
vobj = voomWithDreamWeights(dge, ~ gender, df_metadata)
# 4. Fit linear (mixed) model
fit = dream(vobj, ~ gender, df_metadata)
fit = eBayes(fit)
# 5. Load gene sets and run zenith
msdb.gs = get_MSigDB("H", to="ENSEMBL")
res.gsa = zenith_gsa(fit, msdb.gs, 'gendermale', progressbar=FALSE)
plotZenithResults(res.gsa)
Input: Raw RNA-seq counts and sample metadata. Output: A data frame of gene set enrichment statistics and a heatmap visualization of the top results.
variancePartition::dream().camera method from limma to linear mixed models.get_MSigDB) or Gene Ontology (using get_GeneOntology).limma::camera is sufficient.variancePartition::dream for differential expression; zenith_gsa is specifically designed to take the output of dream().variancePartition::dream() that has been processed with limma::eBayes().GeneSetCollection object containing gene sets (e.g., loaded via get_MSigDB or get_GeneOntology).get_MSigDB or get_GeneOntology.zenith_gsa execution.edgeR::filterByExpr to improve statistical power and model stability.variancePartition::voomWithDreamWeights to account for the mean-variance relationship in RNA-seq count data.plotZenithResults to visualize the top gene set enrichment results across coefficients or cell types.get_MSigDB(..., to=...) matches the ID type used as rownames in the expression matrix and the dream fit.calcNormFactors and apply voomWithDreamWeights before fitting models with dream.limma::camera for fixed-effect linear models.EnrichmentBrowser::getGenesets for loading alternative gene set databases like KEGG or Enrichr.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。
apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
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ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
日本語の概要は準備中です。原文の説明を表示しています。
AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
日本語の概要は準備中です。原文の説明を表示しています。