Analyze single-cell ligand-receptor interactions with CellPhoneDB.
npx skills add https://github.com/Ketomihine/my_skills --skill cellphonedb
Please help me install this Agent Skill. Skill: cellphonedb Source: https://github.com/Ketomihine/my_skills/tree/main/cellphonedb Command: npx skills add https://github.com/Ketomihine/my_skills --skill cellphonedb
This Skill enables researchers to analyze ligand-receptor-mediated cell-cell communication in single-cell datasets using CellPhoneDB, turning raw data into actionable interaction insights.
Follow quick steps: install Python, obtain CellPhoneDB data, place your meta.txt and counts.txt in your working directory, then run METHOD 2 for significance testing and review outputs like pvalues.csv and significant_means.csv.
High-intent search queries and answers about installing and using this skill.
Ligand-receptor interactions in scRNA-seq data are identified using statistical significance testing to detect enriched communication patterns between cell types. This Skill validates your counts.txt and meta.txt files, runs the analysis, and outputs pvalues.csv and significant_means.csv to reveal significant interacting pairs.
CellPhoneDB analysis requires a meta.txt file containing cell metadata and a counts.txt file containing gene expression counts. This Skill validates these formats during the data preparation phase to ensure your dataset is correctly structured before running statistical interaction tests.
Spatial microenvironments and transcription factor activity can be integrated into ligand-receptor analysis using spatial constraints and CellSign. This allows you to contextualize cell-cell communication within specific tissue regions and link interactions directly to downstream TF activity.
Cell-cell interactions can be filtered using differentially expressed genes through DEG-based filtering methods. This narrows down the significant ligand-receptor pairs to only those involving genes that are actively differentially expressed across the cell populations being compared.
Python is required to identify ligand-receptor interactions in single-cell datasets using this workflow. You also need to obtain the CellPhoneDB database, manage database versions or custom inputs, and place your meta.txt and counts.txt files in the working directory before running the analysis.
fuzzy-dynamicsStreamline probabilistic single-cell analysis workflows with scvi-tools.
StarlitnightlyRun CellPhoneDB permutation tests on AnnData and produce visualization-ready interaction objects.
MDheweiCurate literature-backed gene lists into a structured table with references.
TianGzlabScore ligand-receptor interactions between spatially co-localized cell types in AnnData objects.
