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     Long Response


    TCR Diversity Metrics β€” Visual Comparison Across Published datasets

    Visual first β€” compact captions and precise citations below each figure. All claims are drawn only from the listed publications; each data point is cited.

    Notes: the HNSCC single-cell spatial study reported a very large total of unique TCRΞ² CDR3 sequences across samples (raw unique sequences aggregated across patients); TCRMatch used curated IEDB-derived sets; TCR2HLA reports cohort scale (individuals) used to train classifiers; TCRanalyzer sample count reflects number of sequenced samples in that methods paper.

         

    These medians come from the systematic assessment of bulk TCR-Seq methodological choices; the authors reported median clonotype counts for TRA and TRB across their RNA-based libraries.

    Context: TCR-L reports median unique TCR sequences per TCGA SKCM patient derived from RNA-seq; Platypus example datasets were small single-cell PBMC cohorts (<10,000 cells total) used for single-cell integration demonstrations; TCRMatch additionally evaluated an independent 10x dataset of 15,769 CDR3Ξ² sequences.

       

    AlphaFold3 achieved median DockQ scores of ~0.636 (class I) and ~0.679 (class II) on the 70-complex TCR-pMHC benchmark β€” useful to compare methodological performance (structure prediction confidence) against repertoire-scale metrics above.

    Key takeaways (visual-first):

    • Single-cell spatial and targeted sequencing studies can produce extremely large unique CDR3 counts when aggregated (millions), while bulk RNA-derived repertoires often show low per-sample recoveries (median ~14.5 sequences/patient in TCGA SKCM) β€” sample type and assay matter greatly for diversity metrics ( cited above).
    • Methodological choices (RNA quality, input amounts, PCR cycles) materially shift measured richness; the bulk TCR-Seq methods paper identifies RNA quality as the dominant driver of observed diversity metrics.
    • Benchmarking structure-prediction quality (DockQ) is complementary to repertoire-scale measures: structure prediction confidence (AlphaFold3 DockQ medians) helps interpret functional inferences from TCR sequences when combined with repertoire-scale datasets.

    If you want: I can (A) compute Shannon / Simpson diversity from your uploaded clonotype tables and produce publication-ready multi-panel Plotly figures, or (B) run a deeper meta-analysis across these papers (e.g., diversity vs sequencing depth) using an AI agent.



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    Updated: March 17, 2026

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    Reading uploaded clonotype tables, computing Shannon/Simpson/Pielou/clonality per sample, and returning a CSV and Plotly-ready JSON for multi-panel visualization using the referenced datasets.



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