bulk-rna-seq-deseq2-analysis-with-omicverse
PyDESeq2 differential expression: ID mapping, DE testing, fold-change thresholding, and GSEA enrichment visualization in OmicVerse.
PyDESeq2 differential expression: ID mapping, DE testing, fold-change thresholding, and GSEA enrichment visualization in OmicVerse.
Bulk RNA-seq DEG pipeline: gene ID mapping, DESeq2 normalization, statistical testing, volcano plots, and pathway enrichment in OmicVerse.
Bulk RNA-seq batch correction with pyComBat: remove batch effects from merged cohorts, export corrected matrices, and benchmark visualizations.
AUCell pathway scoring, metacell DEG, scDrug response, SCENIC regulons, cNMF programs, and NOCD community detection in OmicVerse.
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.
Spatial transcriptomics: Visium/HD, Stereo-seq, Slide-seq preprocessing (crop, rotate, cellpose), deconvolution (Tangram, cell2location, Starfysh), clustering (GraphST, STAGATE), integration, trajectory, communication.
Gene set enrichment analysis with correct geneset format handling. Critical guidance for loading pathway databases and running enrichment in OmicVerse.
Multi-omics integration: MOFA factor analysis, GLUE unpaired alignment, SIMBA batch correction, TOSICA label transfer, StaVIA trajectory. Covers scRNA+scATAC paired/unpaired workflows.
Single-cell QC, normalization, HVG detection, PCA, neighbor graph, UMAP/tSNE embedding pipelines in OmicVerse (CPU/GPU).
STRING protein-protein interaction network analysis with pyPPI: query STRING database, build PPI graphs, expand with add_nodes, and visualize styled networks for bulk gene lists.
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.
Trajectory & RNA velocity: PAGA, Palantir, VIA, dynamo, scVelo, latentvelo, graphvelo backends via ov.single.Velo. Pseudotime, stream plots.
Single-cell clustering (Leiden, Louvain, scICE, GMM), batch correction (Harmony, scVI, BBKNN, Combat), topic modeling, and cNMF in OmicVerse.
TCGA bulk RNA-seq preprocessing with pyTCGA: GDC sample sheets, expression archives, clinical metadata, Kaplan-Meier survival analysis, and annotated AnnData export.
Cell type annotation: SCSA, MetaTiME, CellVote consensus, CellMatch, GPTAnno, weighted KNN label transfer in OmicVerse.
Map scRNA-seq atlases onto spatial transcriptomics slides using omicverse's Single2Spatial workflow for deep-forest training, spot-level assessment, and marker visualisation.
WGCNA co-expression network: soft-threshold, module detection, eigengenes, hub genes, and trait correlation in OmicVerse.
CellPhoneDB v5 ligand-receptor analysis, CellChatViz plots, and the newer ccc_heatmap / ccc_network_plot / ccc_stat_plot communication visualizations in OmicVerse.
BioContext knowledge: UniProt, AlphaFold, STRING, Reactome, GO, PanglaoDB, PubMed, OpenTargets queries via ov.biocontext for gene annotation.
Foundation model workflows: scGPT, Geneformer, UCE, CellPLM cell embedding, annotation, integration via ov.fm unified API. 22 models.
Guide through omicverse's alignment module for SRA downloading, FASTQ quality control, STAR alignment, gene quantification, and single-cell kallisto/bustools pipelines covering both bulk and single-cell RNA-seq workflows.
OmicVerse built-in datasets: pbmc3k, pancreas, dentategyrus, zebrafish, immune, spatial, multiome, plus create_mock_dataset() and predefined_signatures GMT gene sets.
Analyze experiment data and generate analysis reports.