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.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
The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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。
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。
This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
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.
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。
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。