Identify targeted differential expression markers in single-cell data using Seurat FindMarkers.
npx skills add https://github.com/pwwang/immunopipe --skill markersfinder
Please help me install this Agent Skill. Skill: markersfinder Source: https://github.com/pwwang/immunopipe/tree/main/skills/markersfinder Command: npx skills add https://github.com/pwwang/immunopipe --skill markersfinder
Targeted differential expression analysis within single-cell data by wrapping Seurat's FindMarkers for custom group comparisons, enabling researchers to move beyond cluster-vs-all paradigms and explore biologically meaningful contrasts.
Load a Seurat object with the grouping metadata and run MarkersFinder to obtain DE markers and enrichment results.
High-intent search queries and answers about installing and using this skill.
Targeted differential expression analysis on scRNA-seq data is performed by wrapping Seurat FindMarkers to apply custom group comparisons across metadata columns, such as contrasting specific cell clusters or conditions, rather than using default cluster-vs-all paradigms.
You can perform differential expression across multiple cell types and samples simultaneously by applying custom group comparisons across metadata columns within defined single-cell populations, enabling researchers to explore biologically meaningful contrasts.
Automatic pathway and gene-set enrichment analyses are calculated directly on significant markers, with generated visualizations built-in to easily interpret differential expression and enrichment results across the specified groups.
Configurable parameters for Seurat FindMarkers during single-cell differential expression include test.use, logfc.threshold, min.pct, and enrichment databases, allowing precise control over the statistical test and filtering thresholds.
A Seurat object with metadata columns defining the grouping variables is required to explore condition effects and multi-sample designs within defined populations, enabling targeted contrasts rather than broad cluster-vs-all comparisons.
Targeted differential expression is used instead of default cluster-vs-all comparisons to move beyond standard paradigms and explore biologically meaningful contrasts, such as specific cluster c1 vs c3 or condition effects across cell types.
pwwangLoad, preprocess, and integrate multi-sample scRNA-seq data into a Seurat object.
TianGzlabPerforms pathway enrichment analysis on bulk RNA-seq differential expression results using ORA or GSEA methods.
tondevrelAnalyze single-cell gene expression data with AnnData and scRNA-seq workflows.
openscientist-ioIdentify differential gene expression patterns from RNA-seq and transcriptomics data.
