scTrace+: Enhancing 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 single-cell lineages by heritable DNA barcodes, but the high missing rates of lineage barcodes and the intra-clonal heterogeneity bring great challenges to dissecting the mechanisms of cell fate decision. Here, we systematically evaluate the features 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 that scTrace+ can accurately predict the fates of cells. Further, scTrace+ effectively identifies some important driver genes implicated in cellular fate decisions of diverse biological processes, such as cell differentiation or tumor drug responses. A record of this paper's transparent peer review process is included in the supplemental information.