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

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. SignificanceUnderstanding cellular changes over time is key to deciphering lifes processes from development to disease. Current single-cell methods miss crucial information by overlooking cell-state density dynamics and thus neglecting rare, low-density transitional states vital for cell fate decisions. TimeFactorKernel, a novel computational tool, overcomes this limitation. By prioritizing genes active in these low-density transitional states and employing machine learning for precise gene identification, TimeFactorKernel provides a powerful new approach to decode cell fate. Validated and widely adopted, TimeFactorKernel also uniquely models cell-cell interactions, opening new avenues for understanding complex biological systems in health and disease. KeypointsO_LIFT-Kernel is a novel kernel algorithm designed to predict time-related fate factors dynamics from the cell state density and pseudotime regression weights. C_LIO_LIFT-Kernel can be applied to identify lineage transition key genes, cellular interactions, and the multimodal fate kernel inference of genesets/pathways. C_LIO_LICompared to other temporal-related kernels, FT-Kernel minimizes the skewness of gene distribution and more accurately captures the state of fate transitions. C_LIO_LIFT-Kernel is an extensible multimodal framework facilitating to understand the mechanisms of cell differentiation and is available on GitHub. C_LI