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• W3120362037 endingPage "108589" @default.
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• W3120362037 abstract "Single-cell lineage tracing provides crucial insights into the fates of individual cells. Single-cell RNA sequencing (scRNA-seq) is commonly applied in modern biomedical research, but genetics-based lineage tracing for scRNA-seq data is still unexplored. Variant calling from scRNA-seq data uniquely suffers from expressional drop-outs, including low expression and allelic bias in gene expression, which presents significant obstacles for lineage reconstruction. We introduce SClineager, which infers accurate evolutionary lineages from scRNA-seq data by borrowing information from related cells to overcome expressional drop-outs. We systematically validate SClineager and show that genetics-based lineage tracing is applicable for single-cell-sequencing studies of both tumor and non-tumor tissues using SClineager. Overall, our work provides a powerful tool that can be applied to scRNA-seq data to decipher the lineage histories of cells and that could address a missing opportunity to reveal valuable information from the large amounts of existing scRNA-seq data." @default.
• W3120362037 created "2021-01-18" @default.
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• W3120362037 date "2021-01-01" @default.
• W3120362037 modified "2023-10-16" @default.
• W3120362037 title "Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data" @default.
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• W3120362037 doi "https://doi.org/10.1016/j.celrep.2020.108589" @default.
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• W3120362037 hasPublicationYear "2021" @default.
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