Omicsviewer

omicsViewer

Workflows

Standard Workflow

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.

When to Use

• Interactive exploration of high-throughput omics data stored in ExpressionSet or SummarizedExperiment objects.
• Performing on-the-fly enrichment analysis (using fgsea or Fisher's exact test) or STRING network analysis on selected features.
• Testing associations between selected samples and phenotype variables using correlationAnalysis or hypothesis tests (t-test, Mann-Whitney U test, chi-square, or log-rank test).

When NOT to Use

• For high-throughput sequence alignment or raw read counting; use packages like Rsubread instead.
• For complex multi-factor differential expression modeling without interactive visualization; use limma, edgeR, or DESeq2 directly.

Data Requirements

• An expression matrix with unique row names (features) and column names (samples).
• Feature data (fData) and phenotype data (pData) where row names match the expression matrix's row and column names, respectively.
• Column headers in feature and phenotype data formatted as Analysis|Subset|Variable (e.g., ttest|RE_vs_ME|mean.diff).

Key Parameters

• expr: Expression matrix with unique row and column names.
• pData: Phenotype data frame matching columns of the expression matrix.
• fData: Feature data frame matching rows of the expression matrix.
• PCA (TRUE): Logical indicating whether to perform principal component analysis.
• pca.fillNA (TRUE): Logical indicating whether to impute missing values for PCA.
• t.test: Matrix defining t-tests to perform.
• ttest.fillNA (FALSE): Logical indicating whether to impute missing values for t-tests.
• SummarizedExperiment (FALSE): Logical indicating whether to return a SummarizedExperiment instead of an ExpressionSet.

Best Practices

• Pre-calculate dendrograms for rows and columns using hclust and as.dendrogram and store them as attributes (rowDendrogram/colDendrogram) of the expression matrix to speed up heatmap rendering.
• Use reserved keywords like Surv, StringDB, and GS in headers to enable specific downstream tabs (Survival, STRING, ORA/fGSEA) in the analyst panel.
• Extend metadata post-creation using extendMetaData to integrate additional statistical results like correlation analyses.

Common Pitfalls

• Slow heatmap rendering: Calculating dendrograms on the fly for large matrices is slow. Fix: Pre-calculate dendrograms and assign them to the rowDendrogram attribute of the expression matrix.
• Incompatible headers: Front-end fails to recognize analysis columns. Fix: Ensure column names follow the strict Analysis|Subset|Variable format.
• Non-matching identifiers: Feature or sample names do not align between matrices. Fix: Ensure row names of fData match row names of expr, and row names of pData match column names of expr.

Alternatives

• limma: For non-interactive, complex linear modeling of microarray and RNA-seq data.
• DESeq2: For differential expression analysis of RNA-seq count data.
• edgeR: For differential expression analysis of digital gene expression data.

Citations

• Meng Chen (2026). Interactive and explorative visualization of ExpressionSet using omicsViewer.

References

• Homepage: https://bioconductor.org/packages/omicsViewer
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/omicsViewer/inst/doc/quickStart.html