biostudies-literatureChen YSilicon Valley Community FoundationSilicon Valley Community Foundation (SVCF)Department of Health | National Health and Medical Research Council (NHMRC)Department of Health | National Health and Medical Research Council96https://www.ebi.ac.uk/biostudies/studies/S-EPMC8943201biostudies-literatureUnknown9(1)Breast cancer is a common and highly heterogeneous disease. Understanding cellular diversity in the mammary gland and its surrounding micro-environment across different states can provide insight into cancer development in the human breast. Recently, we published a large-scale single-cell RNA expression atlas of the human breast spanning normal, preneoplastic and tumorigenic states. Single-cell expression profiles of nearly 430,000 cells were obtained from 69 distinct surgical tissue specimens from 55 patients. This article extends the study by providing quality filtering thresholds, downstream processed R data objects, complete cell annotation and R code to reproduce all the analyses. Data quality assessment measures are presented and details are provided for all the bioinformatic analyseScientific dataR code and downstream analysis objects for the scRNA-seq atlas of normal and tumorigenic human breast tissue.PMC894320110588922021-237445110274211761991078730Smyth GKVisvader JEPal BChen YLindeman GJfalseR code and downstream analysis objects for the scRNA-seq atlas of normal and tumorigenic human breast tissue.Breast cancer is a common and highly heterogeneous disease. Understanding cellular diversity in the mammary gland and its surrounding micro-environment across different states can provide insight into cancer development in the human breast. Recently, we published a large-scale single-cell RNA expression atlas of the human breast spanning normal, preneoplastic and tumorigenic states. Single-cell expression profiles of nearly 430,000 cells were obtained from 69 distinct surgical tissue specimens from 55 patients. This article extends the study by providing quality filtering thresholds, downstream processed R data objects, complete cell annotation and R code to reproduce all the analyses. Data quality assessment measures are presented and details are provided for all the bioinformatic analyse2022-01-01T00:00:00Z2022 Mar2026-04-08T18:08:21.193Z2025-04-05T22:18:03.208ZS-EPMC89432013532204210.1038/s41597-022-01236-2