home / skills / starlitnightly / omicverse

starlitnightly/omicverse

A python library for multi omics included bulk, single cell and spatial RNA-seq analysis.

25 skills

GitHub

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data-viz-plots

starlitnightly/omicverse

833

This skill helps you generate publication-quality matplotlib and seaborn visualizations for bioinformatics data, supporting multi-panel layouts and

bulk-to-single-deconvolution

starlitnightly/omicverse

833

This skill reconstructs single-cell profiles from bulk RNA-seq using Bulk2Single, trains a beta-VAE, and benchmarks against reference scRNA-seq.

single-trajectory

starlitnightly/omicverse

833

This skill helps you reproduce and extend single-trajectory analyses by integrating PAGA, Palantir, VIA, velocity coupling, and fate scoring notebooks.

single-multiomics

starlitnightly/omicverse

833

This skill provides quick, actionable guidance to integrate and visualize single-cell multi-omics data across MOFA, GLUE, SIMBA, TOSICA, and StaVIA.

tcga-preprocessing

starlitnightly/omicverse

833

This skill guides you through loading TCGA data, initializing metadata, and exporting annotated AnnData while enabling survival analyses.

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bulk-deg-analysis

starlitnightly/omicverse

833

This skill guides you through bulk RNA-seq differential expression analysis in omicverse, from gene ID mapping to visualization and pathway enrichment.

bulk-combat-correction

starlitnightly/omicverse

833

This skill harmonises bulk RNA-seq data across batches using ComBat, exports corrected matrices, and benchmarks pre/post correction visually.

data-export-excel

starlitnightly/omicverse

833

This skill exports bioinformatics results and tables to formatted Excel files using openpyxl, running locally for compatibility with all LLM providers.

data-export-pdf

starlitnightly/omicverse

833

This skill creates professional PDF reports with text, tables, and embedded images using reportlab, enabling local, provider-agnostic analysis documentation.

data-stats-analysis

starlitnightly/omicverse

833

This skill performs rigorous statistical analyses locally using scipy and statsmodels, enabling robust t-tests, ANOVA, correlations, and multiple-testing

fastq-analysis

starlitnightly/omicverse

833

This skill guides end-to-end FASTQ-to-count analysis in OmicVerse, automating download, QC, alignment, quantification, and single-cell workflows.

gsea-enrichment

starlitnightly/omicverse

833

This skill guides you through proper dictionary-based gene set enrichment in OmicVerse, ensuring correct data formats and error-free analysis.

single-annotation

starlitnightly/omicverse

833

This skill guides you through single-cell annotation workflows from SCSA to GPTAnno and weighted transfer, enabling accurate cell type labeling.

single-cellphone-db

starlitnightly/omicverse

833

This skill quantifies ligand-receptor communication in annotated single-cell data using CellPhoneDB v5 and generates CellChat-style visualizations for

single-downstream-analysis

starlitnightly/omicverse

833

This skill streamlines single-cell downstream analysis by turning tutorials into executable checklists for AUCell, DEG, SCENIC, and more.

bulk-deseq2-analysis

starlitnightly/omicverse

833

This skill guides you through PyDESeq2-based bulk RNA-seq differential expression analysis with ID mapping, filtering, visualization, and enrichment in

bulk-wgcna-analysis

starlitnightly/omicverse

833

This skill guides you through bulk WGCNA analysis with omicverse, from data loading to hub-gene extraction and module visualization.

bulk-stringdb-ppi

starlitnightly/omicverse

833

This skill helps you query STRING for protein interactions, build PPI networks with pyPPI, and render styled network figures from gene lists.

bulk-trajblend-interpolation

starlitnightly/omicverse

833

This skill bridges gaps in scRNA-seq trajectories by integrating BulkTrajBlend, training beta-VAE and GNN models to interpolate missing cell states.

data-transform

starlitnightly/omicverse

833

This skill helps you transform, clean, and reshape data locally using pandas and numpy across any LLM provider.

plotting-visualization

starlitnightly/omicverse

833

This skill guides you through OmicVerse plotting workflows for bulk, color systems, and single-cell visualizations, enabling reusable palettes and embedding

single-clustering

starlitnightly/omicverse

833

This skill guides you through single-cell clustering and batch correction with omicverse, enabling preprocessing, multiple clustering methods, and batch

single-preprocessing

starlitnightly/omicverse

833

This skill guides you through omicverse single-cell preprocessing, QC, HVG detection, normalisation, and embeddings for PBMC3k on CPU, CPU–GPU, or GPU.

single-to-spatial-mapping

starlitnightly/omicverse

833

This skill maps single-cell references to spatial transcriptomics profiles, enabling spot-level reconstruction, marker visualization, and downstream reporting.

spatial-tutorials

starlitnightly/omicverse

833

This skill guides you through spatial transcriptomics tutorials using OmicVerse, covering preprocessing, deconvolution, and downstream modelling across

starlitnightly/omicverse

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