q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics.
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.
Duke Scholars
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- Single-Cell Analysis
- Leukocytes, Mononuclear
- Humans
- Gene Expression Profiling
- Cluster Analysis
- 32 Biomedical and clinical sciences
- 31 Biological sciences
Citation
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Single-Cell Analysis
- Leukocytes, Mononuclear
- Humans
- Gene Expression Profiling
- Cluster Analysis
- 32 Biomedical and clinical sciences
- 31 Biological sciences