Document Type

Report

Publication Date

2025

Pages

9 pp.

Abstract

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s GeneLab supported multi-omics studies under space stressors. Our progress advances personalized TNBC medicine by revealing cellular insights and charts a path toward a comprehensive, multi modal TNBC cell atlas, promising broad impact in oncology and NASA health research.

Rights

© 2025 The Authors, All Rights Reserved.

Included with the kind written permission of the copyright holders.

Original Publication Citation

Scheible, P., Tang, A. H., He, J., & Sun, J. (2025). Benchmarking batch-effect correction methods towards the construction of a triple-negative breast cancer cell atlas. Virginia Space Grant Consortium. https://vsgc.odu.edu/wp-content/uploads/2025/04/Scheible_Peter_VSGC_Conference_Paper_2025.pdf

ORCID

0009-0001-6287-1552 (Scheible), 0000-0002-5772-2878 (Tang), 0009-0000-8905-7553 (Sun)

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