Cluster Seurat objects with Leiden or Louvain algorithms to identify cell populations.
npx skills add https://github.com/pwwang/immunopipe --skill seuratclustering
Please help me install this Agent Skill. Skill: seuratclustering Source: https://github.com/pwwang/immunopipe/tree/main/skills/seuratclustering Command: npx skills add https://github.com/pwwang/immunopipe --skill seuratclustering
Clustering single-cell RNA-seq data to identify distinct cell populations and guide downstream annotation, enabling researchers to interpret cellular heterogeneity efficiently.
Provide a Seurat object as input (SeuratPreparing) and run SeuratClustering with default parameters.
High-intent search queries and answers about installing and using this skill.
Cluster single-cell RNA-seq data by providing a pre-processed Seurat object to run FindNeighbors and FindClusters, which identifies distinct cell populations for downstream annotation.
Leiden and Louvain are both algorithms supported for scRNA-seq clustering to identify cell populations. Leiden often yields more refined clusters, while multi-resolution exploration helps balance granularity and biological relevance.
Yes, single-cell clustering supports integration-aware reductions, allowing you to cluster multiple datasets reliably after integration by utilizing the integrated reduction space.
Visualize UMAP embeddings after Seurat clustering to intuitively interpret and explore the cluster structure, helping you understand cellular heterogeneity and distinct populations.
Balance cluster granularity in single-cell data through multi-resolution exploration during clustering, allowing you to adjust parameters and find the optimal balance for biological relevance.
Yes, you need to perform QC and normalization on your scRNA-seq data first. Provide a prepared Seurat object as input to reliably identify distinct cell populations through clustering.
CHENyiru3Analyze scRNA-seq data in R with Seurat for QC, clustering, and marker discovery.
TianGzlabAutomate cell type annotation for single-cell omics data using marker genes, CellTypist, SingleR, or scmap.
pwwangLoad, preprocess, and integrate multi-sample scRNA-seq data into a Seurat object.
chenyhvvvvAnnotate cell types in spatial transcriptomics data using clustering, marker analysis, and LLM labeling.
