sc-best-practices-auto

To structure single-cell AIRR analysis, this Skill provides best-practice workflows covering TCR/BCR processing, annotation, clonotype definition, and diversity metrics. It consolidates scattered notes into a coherent pipeline you can follow to reproduce and adapt for your study.

The best way to integrate scRNA-seq with immune receptor data is through multimodal integration examples. This Skill guides you in combining GEX and TCR/BCR data using scanpy, scirpy, and anndata for end-to-end downstream insights.

Yes, you can use scanpy and scirpy for clonotype definition and diversity analysis. This Skill requires these Python packages and provides workflow references covering abundance, diversity metrics, and network visualizations for AIRR datasets.

Yes, you need anndata to reproduce single-cell AIRR tutorial pipelines. The Skill implementation requires Python packages such as scanpy, scirpy, and anndata, relying on references and tutorials to ensure reproducibility across scRNA-seq datasets.

To automate annotation guidance for immune receptor data linked to transcriptomes, load your AIRR and scRNA-seq data and follow the Skill's references. These apply annotation, clonotype, and multimodal analysis steps described in the consolidated material.