Infers gene regulatory networks from single-cell expression data using pySCENIC or correlation-based fallback.
npx skills add https://github.com/TianGzlab/OmicsClaw --skill sc-grn
Please help me install this Agent Skill. Skill: sc-grn Source: https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/sc-grn Command: npx skills add https://github.com/TianGzlab/OmicsClaw --skill sc-grn
This Skill automates the complex and multi-step process of inferring gene regulatory networks (GRNs) from single-cell RNA sequencing data, which traditionally requires stitching together multiple bioinformatics tools.
Run the sc-grn skill to infer gene regulatory networks from the provided processed single-cell data file.
High-intent search queries and answers about installing and using this skill.
Infer gene regulatory networks from single-cell RNA-seq data by running automated multi-step inference using pySCENIC, which identifies transcription factor regulons and scores their activity per cell.
A correlation-based fallback method infers gene regulatory networks when pySCENIC is unavailable, providing a simpler alternative to identify transcription factor relationships from single-cell expression data.
Simulate transcription factor perturbation in single-cell data using the integrated CellOracle support, which performs GRN simulation and perturbation analysis to predict cell identity responses.
Yes, scanpy is required to process the single-cell expression data needed to identify transcription factor regulons and score their activity per cell using pySCENIC or the correlation-based fallback.
pySCENIC-based gene regulatory network construction requires pyscenic, arboreto, and ctxcore libraries to execute the GRNBoost2, cisTarget, and AUCell pipeline steps for comprehensive GRN analysis.
Yes, you can use a simpler correlation-based approach for single-cell transcriptomics GRN inference instead of pySCENIC, serving as an automated fallback when the full pySCENIC library stack is unavailable.
anthropicsFilter low-quality cells from single-cell RNA-seq data using MAD-based QC.
ya-wayIdentify transcription factor regulons from single-cell RNA-seq data using pySCENIC.
ovachieverInfer gene regulatory networks from expression data using GRNBoost2 and GENIE3.
fuzzy-dynamicsInfer gene regulatory networks from expression data using GRNBoost2 or GENIE3.
