q-Diffusion – leveraging the full dimensionality of gene coexpression in single-cell transcriptomics

In the ever-evolving landscape of biological research, scientists continually strive to unravel the complexities of cell biology. Single-cell RNA sequencing (scRNAseq) has emerged as a powerful tool for dissecting the molecular intricacies of individual cells, offering unprecedented insights into their gene expression profiles. Now, researchers at the University of Southern California have developed a groundbreaking framework called q-diffusion that promises to unlock the full potential of scRNAseq data, revolutionizing our understanding of cellular diversity and function.

q-diffusion represents a significant leap forward in scRNAseq analysis, allowing researchers to capture the coexpression structure of entire gene libraries with unprecedented precision. By refining existing analysis methods, q-diffusion opens new avenues for exploring the relationships between genes and their functional implications within cells.

Schematic of the mechanisms behind q-diffusion

Fig. 1

When comparing two cells, the kernel fundamentally values expression differences that occur in many genes concurrently. It can enter and augment several common analyses: a q-Diffusion facilitates nuanced phenotype resolution via community detection, as with the second case study in this paper. b q-Diffusion can regularize gene expression program (GEP) estimators like nonnegative matrix factorization (NMF), to promote statistical enrichment of gene ontologies (first and third case studies). c Recent spatial scRNAseq modalities present a new opportunity for macro-segmentation based on cellular transcriptomics, like in the brain (third case study). We present a local distributional segmentation (LDS) algorithm that relies on q-diffusion applied to maximum mean discrepancy (MMD), an established kernel-based statistic.

To demonstrate the capabilities of q-diffusion, the researchers conducted three compelling case studies that showcase its versatility and effectiveness. In the first study, q-diffusion was applied to analyze data from a randomized clinical trial for metastatic colorectal cancer. By uncovering differential effects on patient outcomes with enhanced statistical significance, q-diffusion offers valuable insights for guiding precision treatment strategies.

In the second case study, q-diffusion was put to the test against existing scRNAseq classification methods using peripheral blood mononuclear cell (PBMC) data. Here, q-diffusion outperformed other methods in accurately discriminating cellular responses to IFN-γ stimulation. This success highlights the potential of q-diffusion to refine our understanding of immune cell dynamics and function.

The third case study focused on spatial scRNAseq analysis of human cortical tissue. Leveraging q-diffusion, the researchers developed a novel approach for segmenting local distribution patterns within the tissue, revealing interpretable structures that shed light on the organization of cortical cells.

Overall, q-diffusion represents a paradigm shift in scRNAseq analysis, empowering researchers to extract richer insights from complex biological data. By harnessing the full dimensionality of scRNAseq data, q-diffusion opens doors to new discoveries in cell biology and holds promise for advancing our understanding of health and disease.

Availability – Please visit https://github.com/marmarelis/QDiffusion.jl for access to the Julia package.

Marmarelis MG, Littman R, Battaglin F, Niedzwiecki D, Venook A, Ambite JL, Galstyan A, Lenz HJ, Ver Steeg G. (2024) q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics. Commun Biol 7(1):400. [article].

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