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Software . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Software . 2025
License: CC BY
Data sources: Datacite
ZENODO
Software . 2025
License: CC BY
Data sources: Datacite
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Code utilized for analysis of scRNA-seq data in CD27 Agonism Enhances Long-Lived CD4 T Cell Vaccine Responses Critical for Anti-Tumor Immunity

Authors: Hartman, Zachary;

Code utilized for analysis of scRNA-seq data in CD27 Agonism Enhances Long-Lived CD4 T Cell Vaccine Responses Critical for Anti-Tumor Immunity

Abstract

Description of workflow Following processing steps using 10X software multi-pipeline, scRNA-seq data for all samples were collated into a single Seurat object for QC and integration using ‘aCD27_integration_and_QC.R’. This script utilized ‘calculate_sample_experiment_entropy.R’ and ‘make_entropy_plots.R’ to assess cell type and integration performance. Next, for visualization and cell type assignment, relevant marker and immune biology genes were loaded with ‘load_immune_markers.R’. To establish doublet-free well-annotated cells, we filtered clusters for discordant marker gene expression using genes loaded with ‘load_immune_gene_filters.R’. Scripts for filtering each relevant immune type included: ‘sort_and_filter_mitotic.R’, ‘filter_cells_by_gene_expression.R’, and ‘sort_Tcells.R’. Finally, to facilitate consistent visualization, orderings for cell types, classes, and clusters were specificied in ‘load_group_orders.R’. Next analysis of gene expression differences across experimental arms in specific cell types were analyzed in manuscript preparation were analyzed in ‘aCD27_GEX_analysis.R’ . Again, to make consistent ordering for figures, ‘load_group_orders.R’ is sourced. ‘load_immune_markers.R’ provides key immune marker genes for relevant plots. DE expression comparisons across experimental groups was facilitated by function loaded in ‘useful_functions.R’. Finally, clonotype analysis and relevant figure generation was performed using code found in ‘aCD27_clonotype_analysis.R’. For clarity the user facing scripts are listed in bold below and the scripts required to run those with sub-functions and tools are listed beneath the relevant user-facing script. Users will need to alter paths and download relevant software before running in their local environment. List of scripts aCD27_integration_and_QC.R calculate_sample_experiment_entropy.R make_entropy_plots.R load_immune_markers.R load_immune_gene_filters.R sort_and_filter_mitotic.R filter_cells_by_gene_expression.R sort_Tcells.R load_group_orders.R aCD27_GEX_analysis.R load_group_orders.R load_immune_markers.R useful_functions.R" aCD27_clonotype_analysis.R

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Keywords

Breast cancer, scRNA-seq Workflow, HER2 vaccination, CD27 agonism

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Related to Research communities
Cancer Research