RT Journal Article
SR Electronic
T1 Cell lineage inference from SNP and scRNA-Seq data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 401943
DO 10.1101/401943
A1 Jun Ding
A1 Chieh Lin
A1 Ziv Bar-Joseph
YR 2018
UL http://biorxiv.org/content/early/2018/08/30/401943.abstract
AB Several recent studies focus on the inference of developmental and response trajectories from single cell NA-Seq (scRNA-Seq) data. A number of computational methods, often referred to as pseudo-time ordering, have been developed for this task. Recently, CRISPR has also been used to reconstruct lineage trees by inserting random mutations. However, both approaches suffer from drawbacks that limit their use. Here we develop a method to detect significant, cell type specific, sequence mutations from scRNA-Seq data. We show that only a few mutations are enough for reconstructing good branching models. Integrating these mutations with expression data further improves the accuracy of the reconstructed models. As we show, the majority of mutations we identify are likely RNA editing events indicating that such information can be used to distinguish cell types.