https://subhadipjana1409.r-universe.devRecent package updates in subhadipjana1409R-universehttps://github.com/subhadipjana1409.pnghttps://subhadipjana1409.r-universe.devSun, 05 Jul 2026 17:46:43 GMTsubhadipjana1409@gmail.com (Subhadip Jana)MultiOmicsBridge provides an end-to-end, reproducible computational framework for integrative analysis of paired host transcriptomics (bulk RNA-seq) and gut microbiome (16S rRNA or shotgun metagenomics) data. The package addresses the lack of a unified Bioconductor workflow for this pairing by implementing five modules: (1) data harmonization and normalization with CLR transformation for microbiome compositional data and TMM/voom for RNA-seq; (2) joint dimensionality reduction via sparse multi-block PLS-DA (DIABLO); (3) multi-omics biomarker discovery through cross-omics correlation networks and sparse feature loadings; (4) integrated diagnostic classification comparing host-only, microbiome-only, and joint Random Forest models with stratified cross-validation; and (5) publication-quality visualization of integration results, biomarker networks, classifier comparisons, and feature flow diagrams. All functions operate natively on SummarizedExperiment and MultiAssayExperiment objects and return a structured MOBResult S4 object. The package is validated on inflammatory bowel disease multi-omics data and designed with complex disease contexts (tuberculosis, HIV, EED) in mind.https://github.com/r-universe/bioc/actions/runs/37916709615Sun, 05 Jul 2026 17:46:43 GMTMultiOmicsBridge0.99.1successhttps://bioc.r-universe.devhttps://github.com/bioc/MultiOmicsBridgeMultiOmicsBridge_workflow.RmdMultiOmicsBridge_workflow.htmlIntegrative Multi-Omics Analysis with MultiOmicsBridge2026-05-11 18:35:572026-07-05 17:09:32subhadipjana1409@gmail.com (Subhadip Jana)scCompoundDE decomposes pseudo-bulk differential expression (DE) signals into two orthogonal components: transcriptional changes (cell-intrinsic expression shifts) and compositional changes (shifts in the relative abundance of cell subtypes). Standard pseudo-bulk DE tools confound these two sources of signal, producing spurious DE calls when subtype proportions differ between conditions. scCompoundDE fits per-subtype limma-voom models, estimates subtype proportion shifts, and uses a z-score-normalized decomposition to assign each gene a TC_ratio score — the fraction of its DE signal attributable to transcription versus composition. Genes are then classified as transcriptional (real biology), compositional (artifact), or mixed (requires caution). All functions operate natively on SingleCellExperiment objects and return a CDEResult S4 object that extends the standard DE output with full decomposition statistics.https://github.com/r-universe/bioc/actions/runs/37617141940Tue, 30 Jun 2026 10:15:57 GMTscCompoundDE0.99.1successhttps://bioc.r-universe.devhttps://github.com/bioc/scCompoundDEscCompoundDE.RmdscCompoundDE.htmlDecomposing pseudo-bulk DE into transcriptional and compositional components with scCompoundDE2026-06-30 10:15:572026-06-30 10:15:57subhadipjana1409@gmail.com (Subhadip Jana)scBatchQC provides a hierarchical empirical Bayes framework for quality control in multi-sample, multi-batch single-cell RNA-seq experiments. Unlike per-sample QC tools, scBatchQC jointly models QC metric distributions (library size, gene count, mitochondrial fraction) and doublet rates across batches, enabling calibrated cell-level QC calls that account for batch structure. The package operates natively on SingleCellExperiment objects and returns augmented colData with per-cell QC flags and batch-adjusted doublet scores.https://github.com/r-universe/bioc/actions/runs/36233842290Mon, 08 Jun 2026 07:03:56 GMTscBatchQC0.99.3successhttps://bioc.r-universe.devhttps://github.com/bioc/scBatchQCscBatchQC_workflow.RmdscBatchQC_workflow.htmlBatch-aware QC for multi-sample scRNA-seq with scBatchQC2026-03-26 09:44:522026-06-08 07:03:56subhadipjana1409@gmail.com (Subhadip Jana)scFastDE provides fast, donor-weighted pseudo-bulk differential expression analysis for multi-donor single-cell RNA-seq experiments. Unlike existing tools that loop over genes serially, scFastDE uses vectorised sparse matrix operations across all genes simultaneously, achieving 10-50x speed gains on large datasets. Donors are weighted by the square root of their cell count, giving principled influence to well-represented donors without discarding donors with few cells. Paired experimental designs (same donors in multiple conditions) are automatically detected; pseudo-bulk is then aggregated per donor-condition pair and a blocking model accounts for inter-donor variation. A sparse pseudo-bulk guard automatically handles cell types where some donors fall below a minimum cell threshold. All functions operate natively on SingleCellExperiment objects.https://github.com/r-universe/bioc/actions/runs/36224309585Thu, 04 Jun 2026 09:20:04 GMTscFastDE0.99.3successhttps://bioc.r-universe.devhttps://github.com/bioc/scFastDEscFastDE_workflow.RmdscFastDE_workflow.htmlFast donor-weighted DE for multi-donor scRNA-seq with scFastDE2026-03-27 12:43:392026-03-27 19:18:43