scTrace+: enhance the cell fate inference by integrating the lineage-tracing and multi-faceted transcriptomic similarity information

Abstract

Deciphering the cell state dynamics is crucial for understanding biological processes. Single-cell lineage tracing technologies provide an effective way to track the single-cell lineages by heritable DNA barcodes, but the high missing rates of lineage barcodes and the intra-clonal heterogeneity bring great challenges for dissecting the mechanisms of cell fate decision. Here, we systematically evaluate the feature of single-cell lineage tracing data, and then develop an algorithm scTrace+ to enhance the cell dynamic traces by incorporating multi-faceted transcriptomic similarities into lineage relationships via a Kernelized Probabilistic Matrix Factorization model. We assess its feasibility and performance by conducting ablation and benchmarking experiments on multiple real datasets, and show scTrace+ can accurately predict the fates of cells. Further, scTrace+ effectively identifies some important driver genes implicated in cellular fate decision of diverse biological processes, such as the cell differentiation or the tumor drug responses.