omicverse/omicclaw

bulk-rna-seq-batch-correction-with-combat

Bulk RNA-seq batch correction with pyComBat: remove batch effects from merged cohorts, export corrected matrices, and benchmark visualizations.

bulk-rna-seq-differential-expression-with-omicverse

Bulk RNA-seq DEG pipeline: gene ID mapping, DESeq2 normalization, statistical testing, volcano plots, and pathway enrichment in OmicVerse.

bulk-rna-seq-deseq2-analysis-with-omicverse

PyDESeq2 differential expression: ID mapping, DE testing, fold-change thresholding, and GSEA enrichment visualization in OmicVerse.

bulk-rna-seq-deconvolution-with-bulk2single

Turn bulk RNA-seq cohorts into synthetic single-cell datasets using omicverse's Bulk2Single workflow for cell fraction estimation, beta-VAE generation, and quality control comparisons against reference scRNA-seq.

bulktrajblend-trajectory-interpolation

Extend scRNA-seq developmental trajectories with BulkTrajBlend by generating intermediate cells from bulk RNA-seq, training beta-VAE and GNN models, and interpolating missing states.

bulk-wgcna-analysis-with-omicverse

WGCNA co-expression network: soft-threshold, module detection, eigengenes, hub genes, and trait correlation in OmicVerse.

gsea-enrichment-analysis

Gene set enrichment analysis with correct geneset format handling. Critical guidance for loading pathway databases and running enrichment in OmicVerse.

omicverse-visualization-for-bulk-color-systems-and-single-cell-d

OmicVerse plotting: volcano, venn, boxplot, embedding, density, dotplot, convex hull, stacked bar, and Forbidden City color palettes.

single-cell-annotation-skills-with-omicverse

Cell type annotation: SCSA, MetaTiME, CellVote consensus, CellMatch, GPTAnno, weighted KNN label transfer in OmicVerse.

single-cell-cellphonedb-communication-mapping

CellPhoneDB v5 ligand-receptor analysis, cell-cell communication networks, and CellChat-style visualization in OmicVerse.

single-cell-clustering-and-batch-correction-with-omicverse

Single-cell clustering (Leiden, Louvain, scICE, GMM), batch correction (Harmony, scVI, BBKNN, Combat), topic modeling, and cNMF in OmicVerse.

single-cell-preprocessing-with-omicverse

Single-cell QC, normalization, HVG detection, PCA, neighbor graph, UMAP/tSNE embedding pipelines in OmicVerse (CPU/GPU).

single2spatial-spatial-mapping

Map scRNA-seq atlases onto spatial transcriptomics slides using omicverse's Single2Spatial workflow for deep-forest training, spot-level assessment, and marker visualisation.

spatial-transcriptomics-tutorials-with-omicverse

Spatial transcriptomics: Visium/HD, Stereo-seq, Slide-seq preprocessing (crop, rotate, cellpose), deconvolution (Tangram, cell2location, Starfysh), clustering (GraphST, STAGATE), integration, trajectory, communication.

tcga-bulk-data-preprocessing-with-omicverse

TCGA bulk RNA-seq preprocessing with pyTCGA: GDC sample sheets, expression archives, clinical metadata, Kaplan-Meier survival analysis, and annotated AnnData export.

foundation-model-analysis

Foundation model workflows: scGPT, Geneformer, UCE, CellPLM cell embedding, annotation, integration via ov.fm unified API. 22 models.