Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/29610
Title: R code and downstream analysis objects for the scRNA-seq atlas of normal and tumorigenic human breast tissue.
Austin Authors: Chen, Yunshun;Pal, Bhupinder;Lindeman, Geoffrey J;Visvader, Jane E;Smyth, Gordon K
Affiliation: Department of Medicine, The University of Melbourne, Parkville, Victoria, 3010, Australia..
The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia..
Department of Medical Biology, The University of Melbourne, Parkville, Victoria, 3010, Australia..
Olivia Newton-John Cancer Research Institute
School of Cancer Medicine, La Trobe University, Bundoora, Vic, Australia..
The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia..
School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria, 3010, Australia..
Issue Date: 23-Mar-2022
Date: 2022
Publication information: Scientific Data 2022; 9(1): 96
Abstract: 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 analyses that produced results described in the study.
URI: https://ahro.austin.org.au/austinjspui/handle/1/29610
DOI: 10.1038/s41597-022-01236-2
ORCID: 0000-0003-4911-5653
0000-0002-3684-4331
0000-0001-9386-2416
0000-0001-9173-6977
0000-0001-9221-2892
Journal: Scientific data
PubMed URL: 35322042
PubMed URL: https://pubmed.ncbi.nlm.nih.gov/35322042/
Type: Journal Article
Appears in Collections:Journal articles

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