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March 7th, 2025
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School of Chemistry and Biological Engineering, University of Science and Technology Beijing
bioinformatics
biorxiv

FT-Kernel: An innovative kernel for decoding cellular secrets related time

Xing, C.Open in Google Scholar•Ms, Y.Open in Google Scholar•Wang, Y.Open in Google Scholar•Wang, Y.Open in Google Scholar•Qin, P.Open in Google Scholar•Du, H.Open in Google Scholar•Zeng, Z.Open in Google Scholar

The cell fate participants characterization based on single-cell RNA sequencing (scRNA-seq) data greatly facilitates the mechanism understandings of cellular differentiation. However, inferring these fate factors dynamics along the pseudotime is challenging. Based on cell-state density and pseudotime regression weights, we present an algorithm TimeFactorKernel (FT-Kernel), to predict the key cell fate factors, not only the minimum lineage transition genes, but also the related genesets/pathways, and cellular interaction dynamics along the pseudotime. By extrapolating the pseudotime-related key genes from spectral data as a pseudotime-kernel, FT-Kernel outperformed previous methods. Beyond time-related genes, FT-Kernel offered a comprehensive analysis of the inferred cellular interactions dynamics and lineage pathways dynamics along the pseudotime, which were limited in other methods. Additionally, it facilitated time-related fate factors prediction across various data modalities. Our work developed an important pseudotime-kernel in predicting the fate factors, and provided insights into the cellular hierarchies during development.

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