Cell lineage inference from SNP and scRNA-Seq data

Nucleic Acids Research
Jun DingZiv Bar-Joseph

Abstract

Several recent studies focus on the inference of developmental and response trajectories from single cell RNA-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.

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Citations

Jan 1, 2021·Progress in Retinal and Eye Research·Andrew P VoigtRobert F Mullins
Dec 19, 2020·Frontiers in Cell and Developmental Biology·Yunjin LiGeng Chen
May 1, 2021·Journal of Cardiovascular Development and Disease·Joshua C PetersonMarco C DeRuiter
Jan 29, 2022·Proceedings of the National Academy of Sciences of the United States of America·Li LinJin Li

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Methods Mentioned

BETA
RNA-seq
scRNA-Seq
DNA seq
DNA-seq

Software Mentioned

RED
RADAR
Monocle
scdiff
HISAT2
TBSP
PAVIS
MEL
GATK
PANTHER

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