By Kathryn Murphy / 10 August, 2020
Editorial about the Human Breast Cell Atlas Project and NetworkEditorial
Article by Renée van Amerongen
For more Behind the Scenes of the Human Breast Cell Atlas Project are detailed in an article by Renée van Amerongen. Based on discussion and interviews with Kai Kessenbrock, Whalid Khaled, Devon Lawson, Harikrishna Nakshatri and Nicholas Navin.
Profiling human breast epithelial cells using single cell RNA sequencing identifies cell diversityPaper
Article by Nguyen QH et al.
Paper published in Nature Commununication, published in 2018 by Nguyen et al from Kessenbrock lab. This paper was part of the plot study examinging the single-cell mRNA sequencing (scRNAseq) from 25,790 primary human breast epithelial cells isolated from reduction mammoplasties of seven individuals.
MEDALT: Single-cell copy number lineage tracing enabling gene discoveryPaper
Paper published in Genome Biology, published in 2021 by Wang et al from Chen lab. The method presented in the paper is Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm which looking at single cell copy number profiles and phylogenetics.
Single-cell manifold-preserving feature selection for detecting rare cell populationsPaper
Paper published in Nature Computational Science, published in 2021 by Liang et al from Chen lab. The method presented in the paper is Single-Cell Manifold presERving feature selection (SCMER). The algorithm looks at single cell RNA sequencing profiles to identify non-redundant features that sensitively delineate both common cell lineages and rare cellular states.
Stratified Test Accurately Identifies Differentially Expressed Genes Under Batch Effects in Single-Cell DataPaper
Paper published in IEEE/ACM Transactions on Computational Biology and Bioinformatics, published in 2021 by Liang et al from Chen lab. Paper examined using Van Elteren test, a stratified version of the widely used Wilcoxon rank-sum test to handle discrepancies or batch effects in experiments and or participants and to reduce false discoveries in differentially expressed genes in single cell data.
Sensei: how many samples to tell a change in cell type abundance?Paper
Paper published in BMC Bioinformatics, published in 2022 by Liang et al from Chen lab. Sensei in a new approach used to determine the samples and the number of cells required for single cell experiments in order to detect changes between two groups of samples in single-cell studies. Sensei is an expansion on the t-test and models of cell abundances using a beta-binomial distribution.
Bi-order multimodal integration of single-cell dataPaper
Paper published in Genome Biology, published in 2022 by Dou et al from Chen lab. The method presented bi-order canonical correlation analysis (bi-CCA) in order to improve intergration of data types from two different single cell technolgies.