# Omicsviewer

> omicsViewer

- Skill: `biomate-ai/omicsviewer` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/omicsviewer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/omicsviewer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: biomate-ai (https://skillmd.com/u/biomate-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/biomate-ai/omicsviewer

---


# omicsViewer

## Workflows

### Standard Workflow

```r
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