Analyzes paired TCR and RNA data to identify cell states via Bayesian inference.
npx skills add https://github.com/pwwang/immunopipe --skill tessa
Please help me install this Agent Skill. Skill: tessa Source: https://github.com/pwwang/immunopipe/tree/main/skills/tessa Command: npx skills add https://github.com/pwwang/immunopipe --skill tessa
TESSA addresses the challenge of mapping the functional landscape of T cells by integrating TCR sequence profiling with transcriptomic profiles to reveal unified cellular states.
Provide a Seurat object with paired TCR and RNA data and run the TESSA process to obtain TESSA clusters.
High-intent search queries and answers about installing and using this skill.
To integrate paired TCR sequences with RNA expression, TESSA constructs a joint representation of T cells. It applies BriseisEncoder embeddings and Bayesian inference to cluster cells by combined TCR and transcriptomic features across samples.
Identifying functional T cell states from multi-dataset single-cell data requires mapping the combined TCR and RNA landscape. TESSA enables this by using MCMC and Bayesian inference to integrate multiple datasets and reveal unified cellular states.
Yes, you need both TRA and TRB chains to perform TCR and RNA data integration. TESSA requires paired TCR sequence data containing both chains, along with upstream data from ScRepCombiningExpression and ScRepLoading, to map functional cellular states.
Yes, you can use a Seurat object to run cross-sample T cell state mapping. You provide a Seurat object containing paired TCR and RNA data, and TESSA processes it to identify functional T cell states across different samples or datasets.
To cluster T cells using TCR and transcriptomic profiles, you need Python 3.8 or higher. The environment must include the tessa Python package and BriseisEncoder to execute the Bayesian inference and embedding processes.
TESSA handles antigen-specific T cell discovery by integrating paired TCR and RNA expression data to construct a joint representation. This approach maps unified cellular states to identify antigen-specific T cells across multiple datasets.
pwwangCluster TCR/BCR clones by CDR3 sequences using GIANA or ClusTCR.
pwwangIdentify marker genes for all cell clusters before T/B cell selection.
antigenomicsAnnotate TCR/BCR sequences using IgBLAST and MMseqs2 reference databases.
galeepAutomates scRNA-seq analysis including QC, normalization, clustering, and visualization with AnnData and scanpy.
