omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existin
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library(omicsViewer)
library(Biobase)
packdir <- system.file("extdata", package = "omicsViewer")
expr <- read.delim(file.path(packdir, "expressionMatrix.tsv"), stringsAsFactors = FALSE)
colnames(expr) <- make.names(colnames(expr))
rownames(expr) <- make.names(rownames(expr))
fd <- read.delim(file.path(packdir, "featureGeneral.tsv"), stringsAsFactors = FALSE)
pd <- read.delim(file.path(packdir, "sampleGeneral.tsv"), stringsAsFactors = FALSE)
# Prepare the ExpressionSet object
d <- prepOmicsViewer(
expr = expr, pData = pd, fData = fd,
PCA = TRUE, pca.fillNA = TRUE,
SummarizedExperiment = FALSE
)
# Run correlation analysis and extend metadata
expr_mat <- exprs(d)
drugData <- read.delim(file.path(packdir, "sampleDrug.tsv"))
drugcor <- correlationAnalysis(expr_mat, pheno = drugData[, 1:2])
d <- extendMetaData(d, drugcor, where = "fData")
Input: Expression matrix, feature data, and phenotype data.
Output: An ExpressionSet object prepared and extended for interactive visualization in omicsViewer.
ExpressionSet or SummarizedExperiment objects.fgsea or Fisher's exact test) or STRING network analysis on selected features.correlationAnalysis or hypothesis tests (t-test, Mann-Whitney U test, chi-square, or log-rank test).Rsubread instead.limma, edgeR, or DESeq2 directly.fData) and phenotype data (pData) where row names match the expression matrix's row and column names, respectively.Analysis|Subset|Variable (e.g., ttest|RE_vs_ME|mean.diff).TRUE): Logical indicating whether to perform principal component analysis.TRUE): Logical indicating whether to impute missing values for PCA.FALSE): Logical indicating whether to impute missing values for t-tests.FALSE): Logical indicating whether to return a SummarizedExperiment instead of an ExpressionSet.hclust and as.dendrogram and store them as attributes (rowDendrogram/colDendrogram) of the expression matrix to speed up heatmap rendering.Surv, StringDB, and GS in headers to enable specific downstream tabs (Survival, STRING, ORA/fGSEA) in the analyst panel.extendMetaData to integrate additional statistical results like correlation analyses.rowDendrogram attribute of the expression matrix.Analysis|Subset|Variable format.fData match row names of expr, and row names of pData match column names of expr.还没有评价。欢迎分享你的使用感受。
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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).
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