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TCR Dynamic

Overview

This study establishes a multidimensional quantitative evaluation framework to systematically deconstruct the methodological confounders inherent to temporal T cell receptor (TCR) repertoire analysis. We reveal that widely reported clonal turnover and dynamic abundance shifts observed in sequencing data are fundamentally technical artifacts driven by stochastic sampling of low-expansion clones, phenotypic biases, and localized intra-tumor spatial heterogeneity.

Structure & Figure Reproduction

The code is organized by corresponding main figures in the manuscript. Each directory contains standalone scripts to reproduce the data processing and visualization steps.

  • Fig2/: Scripts for phenotypic clustering, compositional shifts of T cell subsets post-intervention, and temporal dynamics of TCR diversity and clonality.

  • Fig3/: Pipelines comparing time-specific and time-across TCRs across untreated samples, multi-timepoint cohorts, and cross-platform (bulk vs. scTCR-seq) modalities.

  • Fig4/: Code for evaluating sample-specific artifacts in biological replicates and validating mathematical distributions using in silico simulated repertoires.

  • Fig5/: Benchmarking scripts for the six evaluated statistical models (NB, BB, P, NP, Binomial, Fisher) utilizing clinical single-cell datasets, PBMC, and tumor replicate cohorts.

  • Fig6/: Scripts mapping dynamic TCR shifts to distinct T cell subsets to quantify phenotypic biases, and algorithms processing Slide-TCR data to evaluate localized intra-tumor spatial heterogeneity.

Software Requirements

The analytical pipelines are implemented in both R and Python.

R Dependencies:

  • Seurat (Single-cell RNA/TCR integration and visualization)

  • ggplot2 (Data visualization)

  • vagen (TCR diversity and Chao2 asymptotic estimation)

  • GPTCelltype (Automated T cell subset annotation leveraging large language models)

  • forestmodel (Forest plot visualization for statistical modeling results and differential comparisons)

  • Other standard bioinformatics utilities.

Python Dependencies:

  • NoisET (NOIse sampling learning & Expansion detection of T-cell receptors using Bayesian inference.)

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TCR Dynamic
Overview
This study establishes a multidimensional quantitative evaluation framework to systematically deconstruct the methodological confounders inherent to temporal T cell receptor (TCR) repertoire analysis. We reveal that widely reported clonal turnover and dynamic abundance shifts observed in sequencing data are fundamentally technical artifacts driven by stochastic sampling of low-expansion clones, phenotypic biases, and localized intra-tumor spatial heterogeneity.
Structure & Figure Reproduction
The code is organized by corresponding main figures in the manuscript. Each directory contains standalone scripts to reproduce the data processing and visualization steps.
  • Fig2/: Scripts for phenotypic clustering, compositional shifts of T cell subsets post-intervention, and temporal dynamics of TCR diversity and clonality.
  • Fig3/: Pipelines comparing time-specific and time-across TCRs across untreated samples, multi-timepoint cohorts, and cross-platform (bulk vs. scTCR-seq) modalities.
  • Fig4/: Code for evaluating sample-specific artifacts in biological replicates and validating mathematical distributions using in silico simulated repertoires.
  • Fig5/: Benchmarking scripts for the six evaluated statistical models (NB, BB, P, NP, Binomial, Fisher) utilizing clinical single-cell datasets, PBMC, and tumor replicate cohorts.
  • Fig6/: Scripts mapping dynamic TCR shifts to distinct T cell subsets to quantify phenotypic biases, and algorithms processing Slide-TCR data to evaluate localized intra-tumor spatial heterogeneity.
Software Requirements
The analytical pipelines are implemented in both R and Python.
R Dependencies:
  • Seurat (Single-cell RNA/TCR integration and visualization)
  • ggplot2 (Data visualization)
  • vagen (TCR diversity and Chao2 asymptotic estimation)
  • GPTCelltype (Automated T cell subset annotation leveraging large language models)
  • forestmodel (Forest plot visualization for statistical modeling results and differential comparisons)
  • Other standard bioinformatics utilities.
Python Dependencies:
  • NoisET (NOIse sampling learning & Expansion detection of T-cell receptors using Bayesian inference.)
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TCR Dynamic
Overview
This study establishes a multidimensional quantitative evaluation framework to systematically deconstruct the methodological confounders inherent to temporal T cell receptor (TCR) repertoire analysis. We reveal that widely reported clonal turnover and dynamic abundance shifts observed in sequencing data are fundamentally technical artifacts driven by stochastic sampling of low-expansion clones, phenotypic biases, and localized intra-tumor spatial heterogeneity.
Structure & Figure Reproduction
The code is organized by corresponding main figures in the manuscript. Each directory contains standalone scripts to reproduce the data processing and visualization steps.
  • Fig2/: Scripts for phenotypic clustering, compositional shifts of T cell subsets post-intervention, and temporal dynamics of TCR diversity and clonality.
  • Fig3/: Pipelines comparing time-specific and time-across TCRs across untreated samples, multi-timepoint cohorts, and cross-platform (bulk vs. scTCR-seq) modalities.
  • Fig4/: Code for evaluating sample-specific artifacts in biological replicates and validating mathematical distributions using in silico simulated repertoires.
  • Fig5/: Benchmarking scripts for the six evaluated statistical models (NB, BB, P, NP, Binomial, Fisher) utilizing clinical single-cell datasets, PBMC, and tumor replicate cohorts.
  • Fig6/: Scripts mapping dynamic TCR shifts to distinct T cell subsets to quantify phenotypic biases, and algorithms processing Slide-TCR data to evaluate localized intra-tumor spatial heterogeneity.
Software Requirements
The analytical pipelines are implemented in both R and Python.
R Dependencies:
  • Seurat (Single-cell RNA/TCR integration and visualization)
  • ggplot2 (Data visualization)
  • vagen (TCR diversity and Chao2 asymptotic estimation)
  • GPTCelltype (Automated T cell subset annotation leveraging large language models)
  • forestmodel (Forest plot visualization for statistical modeling results and differential comparisons)
  • Other standard bioinformatics utilities.
Python Dependencies:
  • NoisET (NOIse sampling learning & Expansion detection of T-cell receptors using Bayesian inference.)
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TCR Dynamic
Overview
This study establishes a multidimensional quantitative evaluation framework to systematically deconstruct the methodological confounders inherent to temporal T cell receptor (TCR) repertoire analysis. We reveal that widely reported clonal turnover and dynamic abundance shifts observed in sequencing data are fundamentally technical artifacts driven by stochastic sampling of low-expansion clones, phenotypic biases, and localized intra-tumor spatial heterogeneity.
Structure & Figure Reproduction
The code is organized by corresponding main figures in the manuscript. Each directory contains standalone scripts to reproduce the data processing and visualization steps.
  • Fig2/: Scripts for phenotypic clustering, compositional shifts of T cell subsets post-intervention, and temporal dynamics of TCR diversity and clonality.
  • Fig3/: Pipelines comparing time-specific and time-across TCRs across untreated samples, multi-timepoint cohorts, and cross-platform (bulk vs. scTCR-seq) modalities.
  • Fig4/: Code for evaluating sample-specific artifacts in biological replicates and validating mathematical distributions using in silico simulated repertoires.
  • Fig5/: Benchmarking scripts for the six evaluated statistical models (NB, BB, P, NP, Binomial, Fisher) utilizing clinical single-cell datasets, PBMC, and tumor replicate cohorts.
  • Fig6/: Scripts mapping dynamic TCR shifts to distinct T cell subsets to quantify phenotypic biases, and algorithms processing Slide-TCR data to evaluate localized intra-tumor spatial heterogeneity.
Software Requirements
The analytical pipelines are implemented in both R and Python.
R Dependencies:
  • Seurat (Single-cell RNA/TCR integration and visualization)
  • ggplot2 (Data visualization)
  • vagen (TCR diversity and Chao2 asymptotic estimation)
  • GPTCelltype (Automated T cell subset annotation leveraging large language models)
  • forestmodel (Forest plot visualization for statistical modeling results and differential comparisons)
  • Other standard bioinformatics utilities.
Python Dependencies:
  • NoisET (NOIse sampling learning & Expansion detection of T-cell receptors using Bayesian inference.)
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TCR Dynamic
Overview
This study establishes a multidimensional quantitative evaluation framework to systematically deconstruct the methodological confounders inherent to temporal T cell receptor (TCR) repertoire analysis. We reveal that widely reported clonal turnover and dynamic abundance shifts observed in sequencing data are fundamentally technical artifacts driven by stochastic sampling of low-expansion clones, phenotypic biases, and localized intra-tumor spatial heterogeneity.
Structure & Figure Reproduction
The code is organized by corresponding main figures in the manuscript. Each directory contains standalone scripts to reproduce the data processing and visualization steps.
  • Fig2/: Scripts for phenotypic clustering, compositional shifts of T cell subsets post-intervention, and temporal dynamics of TCR diversity and clonality.
  • Fig3/: Pipelines comparing time-specific and time-across TCRs across untreated samples, multi-timepoint cohorts, and cross-platform (bulk vs. scTCR-seq) modalities.
  • Fig4/: Code for evaluating sample-specific artifacts in biological replicates and validating mathematical distributions using in silico simulated repertoires.
  • Fig5/: Benchmarking scripts for the six evaluated statistical models (NB, BB, P, NP, Binomial, Fisher) utilizing clinical single-cell datasets, PBMC, and tumor replicate cohorts.
  • Fig6/: Scripts mapping dynamic TCR shifts to distinct T cell subsets to quantify phenotypic biases, and algorithms processing Slide-TCR data to evaluate localized intra-tumor spatial heterogeneity.
Software Requirements
The analytical pipelines are implemented in both R and Python.
R Dependencies:
  • Seurat (Single-cell RNA/TCR integration and visualization)
  • ggplot2 (Data visualization)
  • vagen (TCR diversity and Chao2 asymptotic estimation)
  • GPTCelltype (Automated T cell subset annotation leveraging large language models)
  • forestmodel (Forest plot visualization for statistical modeling results and differential comparisons)
  • Other standard bioinformatics utilities.
Python Dependencies:
  • NoisET (NOIse sampling learning & Expansion detection of T-cell receptors using Bayesian inference.)
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TCR Dynamic
Overview
This study establishes a multidimensional quantitative evaluation framework to systematically deconstruct the methodological confounders inherent to temporal T cell receptor (TCR) repertoire analysis. We reveal that widely reported clonal turnover and dynamic abundance shifts observed in sequencing data are fundamentally technical artifacts driven by stochastic sampling of low-expansion clones, phenotypic biases, and localized intra-tumor spatial heterogeneity.
Structure & Figure Reproduction
The code is organized by corresponding main figures in the manuscript. Each directory contains standalone scripts to reproduce the data processing and visualization steps.
  • Fig2/: Scripts for phenotypic clustering, compositional shifts of T cell subsets post-intervention, and temporal dynamics of TCR diversity and clonality.
  • Fig3/: Pipelines comparing time-specific and time-across TCRs across untreated samples, multi-timepoint cohorts, and cross-platform (bulk vs. scTCR-seq) modalities.
  • Fig4/: Code for evaluating sample-specific artifacts in biological replicates and validating mathematical distributions using in silico simulated repertoires.
  • Fig5/: Benchmarking scripts for the six evaluated statistical models (NB, BB, P, NP, Binomial, Fisher) utilizing clinical single-cell datasets, PBMC, and tumor replicate cohorts.
  • Fig6/: Scripts mapping dynamic TCR shifts to distinct T cell subsets to quantify phenotypic biases, and algorithms processing Slide-TCR data to evaluate localized intra-tumor spatial heterogeneity.
Software Requirements
The analytical pipelines are implemented in both R and Python.
R Dependencies:
  • Seurat (Single-cell RNA/TCR integration and visualization)
  • ggplot2 (Data visualization)
  • vagen (TCR diversity and Chao2 asymptotic estimation)
  • GPTCelltype (Automated T cell subset annotation leveraging large language models)
  • forestmodel (Forest plot visualization for statistical modeling results and differential comparisons)
  • Other standard bioinformatics utilities.
Python Dependencies:
  • NoisET (NOIse sampling learning & Expansion detection of T-cell receptors using Bayesian inference.)
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TCR Dynamic
Overview
This study establishes a multidimensional quantitative evaluation framework to systematically deconstruct the methodological confounders inherent to temporal T cell receptor (TCR) repertoire analysis. We reveal that widely reported clonal turnover and dynamic abundance shifts observed in sequencing data are fundamentally technical artifacts driven by stochastic sampling of low-expansion clones, phenotypic biases, and localized intra-tumor spatial heterogeneity.
Structure & Figure Reproduction
The code is organized by corresponding main figures in the manuscript. Each directory contains standalone scripts to reproduce the data processing and visualization steps.
  • Fig2/: Scripts for phenotypic clustering, compositional shifts of T cell subsets post-intervention, and temporal dynamics of TCR diversity and clonality.
  • Fig3/: Pipelines comparing time-specific and time-across TCRs across untreated samples, multi-timepoint cohorts, and cross-platform (bulk vs. scTCR-seq) modalities.
  • Fig4/: Code for evaluating sample-specific artifacts in biological replicates and validating mathematical distributions using in silico simulated repertoires.
  • Fig5/: Benchmarking scripts for the six evaluated statistical models (NB, BB, P, NP, Binomial, Fisher) utilizing clinical single-cell datasets, PBMC, and tumor replicate cohorts.
  • Fig6/: Scripts mapping dynamic TCR shifts to distinct T cell subsets to quantify phenotypic biases, and algorithms processing Slide-TCR data to evaluate localized intra-tumor spatial heterogeneity.
Software Requirements
The analytical pipelines are implemented in both R and Python.
R Dependencies:
  • Seurat (Single-cell RNA/TCR integration and visualization)
  • ggplot2 (Data visualization)
  • vagen (TCR diversity and Chao2 asymptotic estimation)
  • GPTCelltype (Automated T cell subset annotation leveraging large language models)
  • forestmodel (Forest plot visualization for statistical modeling results and differential comparisons)
  • Other standard bioinformatics utilities.
Python Dependencies:
  • NoisET (NOIse sampling learning & Expansion detection of T-cell receptors using Bayesian inference.)
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GitHub - SuoLab-GZLab/TCR_Dynamic: TCR dynamic of time specific TCRs · GitHub
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TCR Dynamic
Overview
This study establishes a multidimensional quantitative evaluation framework to systematically deconstruct the methodological confounders inherent to temporal T cell receptor (TCR) repertoire analysis. We reveal that widely reported clonal turnover and dynamic abundance shifts observed in sequencing data are fundamentally technical artifacts driven by stochastic sampling of low-expansion clones, phenotypic biases, and localized intra-tumor spatial heterogeneity.
Structure & Figure Reproduction
The code is organized by corresponding main figures in the manuscript. Each directory contains standalone scripts to reproduce the data processing and visualization steps.
  • Fig2/: Scripts for phenotypic clustering, compositional shifts of T cell subsets post-intervention, and temporal dynamics of TCR diversity and clonality.
  • Fig3/: Pipelines comparing time-specific and time-across TCRs across untreated samples, multi-timepoint cohorts, and cross-platform (bulk vs. scTCR-seq) modalities.
  • Fig4/: Code for evaluating sample-specific artifacts in biological replicates and validating mathematical distributions using in silico simulated repertoires.
  • Fig5/: Benchmarking scripts for the six evaluated statistical models (NB, BB, P, NP, Binomial, Fisher) utilizing clinical single-cell datasets, PBMC, and tumor replicate cohorts.
  • Fig6/: Scripts mapping dynamic TCR shifts to distinct T cell subsets to quantify phenotypic biases, and algorithms processing Slide-TCR data to evaluate localized intra-tumor spatial heterogeneity.
Software Requirements
The analytical pipelines are implemented in both R and Python.
R Dependencies:
  • Seurat (Single-cell RNA/TCR integration and visualization)
  • ggplot2 (Data visualization)
  • vagen (TCR diversity and Chao2 asymptotic estimation)
  • GPTCelltype (Automated T cell subset annotation leveraging large language models)
  • forestmodel (Forest plot visualization for statistical modeling results and differential comparisons)
  • Other standard bioinformatics utilities.
Python Dependencies:
  • NoisET (NOIse sampling learning & Expansion detection of T-cell receptors using Bayesian inference.)
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