diff_RNA | R Documentation |
Do difference analysis of RNA-seq data
diff_RNA( counts, group, method = "limma", geneLength = NULL, gccontent = NULL, filter = TRUE, edgeRNorm = TRUE, adjust.method = "BH", useTopconfects = TRUE )
counts |
a dataframe or numeric matrix of raw counts data |
group |
sample groups |
method |
one of "DESeq2", "edgeR" , "limma", "dearseq" and "Wilcoxon". |
geneLength |
a vector of gene length. |
gccontent |
a vector of gene GC content. |
filter |
if TRUE, use filterByExpr to filter genes. |
edgeRNorm |
if TRUE, use edgeR to do normalization for dearseq method. |
adjust.method |
character string specifying the method used to adjust p-values for multiple testing. See p.adjust for possible values. |
useTopconfects |
if TRUE, use topconfects to provide a more biologically useful ranked gene list. |
## Not run: library(TCGAbiolinks) query <- GDCquery(project = "TCGA-ACC", data.category = "Transcriptome Profiling", data.type = "Gene Expression Quantification", workflow.type = "STAR - Counts") GDCdownload(query, method = "api", files.per.chunk = 3, directory = Your_Path) dataRNA <- GDCprepare(query = query, directory = Your_Path, save = TRUE, save.filename = "dataRNA.RData") ## get raw count matrix dataPrep <- TCGAanalyze_Preprocessing(object = dataRNA, cor.cut = 0.6, datatype = "STAR - Counts") # Use `diff_RNA` to do difference analysis. # We provide the data of human gene length and GC content in `gene_cov`. group <- sample(c("grp1", "grp2"), ncol(dataPrep), replace = TRUE) library(cqn) # To avoid reporting errors: there is no function "rq" ## get gene length and GC content library(org.Hs.eg.db) genes_bitr <- bitr(rownames(gene_cov), fromType = "ENTREZID", toType = "ENSEMBL", OrgDb = org.Hs.eg.db, drop = TRUE) genes_bitr <- genes_bitr[!duplicated(genes_bitr[,2]), ] gene_cov2 <- gene_cov[genes_bitr$ENTREZID, ] rownames(gene_cov2) <- genes_bitr$ENSEMBL genes <- intersect(rownames(dataPrep), rownames(gene_cov2)) dataPrep <- dataPrep[genes, ] geneLength <- gene_cov2(genes, "length") gccontent <- gene_cov2(genes, "GC") names(geneLength) <- names(gccontent) <- genes ## Difference analysis DEGAll <- diff_RNA(counts = dataPrep, group = group, geneLength = geneLength, gccontent = gccontent) # Use `clusterProfiler` to do enrichment analytics: diffGenes <- DEGAll$logFC names(diffGenes) <- rownames(DEGAll) diffGenes <- sort(diffGenes, decreasing = TRUE) library(clusterProfiler) library(enrichplot) library(org.Hs.eg.db) gsego <- gseGO(gene = diffGenes, OrgDb = org.Hs.eg.db, keyType = "ENSEMBL") dotplot(gsego) ## End(Not run)
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