Abstract

Unlocking the full dimensionality of single-cell RNA sequencing data (scRNAseq) is the next frontier to a richer, fuller understanding of cell biology. We introduce q-diffusion, a framework for capturing the coexpression structure of an entire library of genes, improving on state-of-the-art analysis tools. The method is demonstrated via three case studies. In the first, q-diffusion helps gain statistical significance for differential effects on patient outcomes when analyzing the CALGB/SWOG 80405 randomized phase III clinical trial, suggesting precision guidance for the treatment of metastatic colorectal cancer. Secondly, q-diffusion is benchmarked against existing scRNAseq classification methods using an in vitro PBMC dataset, in which the proposed method discriminates IFN-γ stimulation more accurately. The same case study demonstrates improvements in unsupervised cell clustering with the recent Tabula Sapiens …

Metadata

publication

Communications Biology 7 (1), 400, 2024

year

2024

publication date

2024/4/2

authors

Myrl G Marmarelis, Russell Littman, Francesca Battaglin, Donna Niedzwiecki, Alan Venook, Jose-Luis Ambite, Aram Galstyan, Heinz-Josef Lenz, Greg Ver Steeg

link

https://www.nature.com/articles/s42003-024-06104-w

resource_link

https://www.nature.com/articles/s42003-024-06104-w

journal

Communications Biology

volume

7

issue

1

pages

400

publisher

Nature Publishing Group UK