| name | bioconductor-omicsviewer |
| description | 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 |
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)
d <- prepOmicsViewer(
expr = expr, pData = pd, fData = fd,
PCA = TRUE, pca.fillNA = TRUE,
SummarizedExperiment = FALSE
)
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
