학술논문
q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics.
Document Type
Academic Journal
Author
Marmarelis MG; Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA. myrlm@isi.edu.; Littman R; University of California Los Angeles, Los Angeles, CA, 90095, USA.; Battaglin F; Keck School of Medicine, University of Southern California, 1975 Zonal Ave., Los Angeles, CA, 90033, USA.; Niedzwiecki D; Duke University, Durham, NC, 27708, USA.; Venook A; University of California San Francisco, San Francisco, CA, 94143, USA.; Ambite JL; Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA.; Galstyan A; Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA.; Lenz HJ; Keck School of Medicine, University of Southern California, 1975 Zonal Ave., Los Angeles, CA, 90033, USA.; Ver Steeg G; Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA.; University of California Riverside, Riverside, CA, 92521, USA.
Source
Publisher: Nature Publishing Group UK Country of Publication: England NLM ID: 101719179 Publication Model: Electronic Cited Medium: Internet ISSN: 2399-3642 (Electronic) Linking ISSN: 23993642 NLM ISO Abbreviation: Commun Biol Subsets: MEDLINE
Subject
Language
English
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 human atlas. Finally, a local distributional segmentation approach for spatial scRNAseq, driven by q-diffusion, yields interpretable structures of human cortical tissue.
(© 2024. The Author(s).)
(© 2024. The Author(s).)