Cut code review time & bugs in half Instantly.
Reviews for AI-powered teams who move fast (but don’t break things) ↗
home / skills / starlitnightly
starlitnightly skills
Find 32 skills from 1 repo created by starlitnightly on GitHub.
Sponsored
datasets-loading
starlitnightly/omicverse
866
This skill provides ready-to-use omicverse built-in datasets and mock data generation to accelerate demos, testing, and signature analyses.
single-preprocessing
starlitnightly/omicverse
866
This skill guides you through omicverse single-cell preprocessing, QC, HVG detection, normalisation, and embeddings for PBMC3k on CPU, CPU–GPU, or GPU.
biocontext-knowledge
starlitnightly/omicverse
866
This skill helps you annotate gene results and explore pathways, literature, and drug associations using BioContext's unified Python API.
single-multiomics
starlitnightly/omicverse
866
This skill provides quick, actionable guidance to integrate and visualize single-cell multi-omics data across MOFA, GLUE, SIMBA, TOSICA, and StaVIA.
bulk-wgcna-analysis
starlitnightly/omicverse
866
This skill guides you through bulk WGCNA analysis with omicverse, from data loading to hub-gene extraction and module visualization.
single-clustering
starlitnightly/omicverse
866
This skill guides you through single-cell clustering and batch correction with omicverse, enabling preprocessing, multiple clustering methods, and batch
bulk-stringdb-ppi
starlitnightly/omicverse
866
This skill helps you query STRING for protein interactions, build PPI networks with pyPPI, and render styled network figures from gene lists.
single-to-spatial-mapping
starlitnightly/omicverse
866
This skill maps single-cell references to spatial transcriptomics profiles, enabling spot-level reconstruction, marker visualization, and downstream reporting.
fm-foundation-models
starlitnightly/omicverse
866
This skill helps you run foundation model workflows for single-cell analysis, from embedding to annotation and integration across 22 models with a unified API.
spatial-tutorials
starlitnightly/omicverse
866
This skill guides you through spatial transcriptomics tutorials using OmicVerse, covering preprocessing, deconvolution, and downstream modelling across
data-viz-plots
starlitnightly/omicverse
866
This skill helps you generate publication-quality matplotlib and seaborn visualizations for bioinformatics data, supporting multi-panel layouts and
plotting-visualization
starlitnightly/omicverse
866
This skill guides you through OmicVerse plotting workflows for bulk, color systems, and single-cell visualizations, enabling reusable palettes and embedding
bulk-combat-correction
starlitnightly/omicverse
866
This skill harmonises bulk RNA-seq data across batches using ComBat, exports corrected matrices, and benchmarks pre/post correction visually.
single-annotation
starlitnightly/omicverse
866
This skill guides you through single-cell annotation workflows from SCSA to GPTAnno and weighted transfer, enabling accurate cell type labeling.
data-export-pdf
starlitnightly/omicverse
866
This skill creates professional PDF reports with text, tables, and embedded images using reportlab, enabling local, provider-agnostic analysis documentation.
single-popv-annotation
starlitnightly/omicverse
866
This skill consolidates up to 10 cell-type classifiers with consensus voting to annotate single-cell data robustly.
tcga-preprocessing
starlitnightly/omicverse
866
This skill guides you through loading TCGA data, initializing metadata, and exporting annotated AnnData while enabling survival analyses.
bulk-deseq2-analysis
starlitnightly/omicverse
866
This skill guides you through PyDESeq2-based bulk RNA-seq differential expression analysis with ID mapping, filtering, visualization, and enrichment in
bulk-deg-analysis
starlitnightly/omicverse
866
This skill guides you through bulk RNA-seq differential expression analysis in omicverse, from gene ID mapping to visualization and pathway enrichment.
single-cellfate-analysis
starlitnightly/omicverse
866
This skill identifies pseudotime-associated genes driving lineage decisions by adaptive ridge regression and Mellon-based density scoring.
single-scenic-grn
starlitnightly/omicverse
866
This skill infers gene regulatory networks from scRNA-seq, prunes regulons, and scores regulon activity for cell-type resolution.
single-cellphone-db
starlitnightly/omicverse
866
This skill quantifies ligand-receptor communication in annotated single-cell data using CellPhoneDB v5 and generates CellChat-style visualizations for
data-stats-analysis
starlitnightly/omicverse
866
This skill performs rigorous statistical analyses locally using scipy and statsmodels, enabling robust t-tests, ANOVA, correlations, and multiple-testing
single-downstream-analysis
starlitnightly/omicverse
866
This skill streamlines single-cell downstream analysis by turning tutorials into executable checklists for AUCell, DEG, SCENIC, and more.
data-io-loading
starlitnightly/omicverse
866
This skill streamlines OmicVerse data loading by replacing scanpy with ov.io readers for h5ad, 10x, Visium, Nanostring, and CSV formats.
bulk-to-single-deconvolution
starlitnightly/omicverse
866
This skill reconstructs single-cell profiles from bulk RNA-seq using Bulk2Single, trains a beta-VAE, and benchmarks against reference scRNA-seq.
bulk-trajblend-interpolation
starlitnightly/omicverse
866
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
866
This skill helps you transform, clean, and reshape data locally using pandas and numpy across any LLM provider.
fastq-analysis
starlitnightly/omicverse
866
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
866
This skill guides you through proper dictionary-based gene set enrichment in OmicVerse, ensuring correct data formats and error-free analysis.
single-trajectory
starlitnightly/omicverse
866
This skill helps you reproduce and extend single-trajectory analyses by integrating PAGA, Palantir, VIA, velocity coupling, and fate scoring notebooks.
data-export-excel
starlitnightly/omicverse
866
This skill exports bioinformatics results and tables to formatted Excel files using openpyxl, running locally for compatibility with all LLM providers.