PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes.
PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes.
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DOI:
10.1038/s41587-023-01887-5
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发表时间:
2023-07
影响因子:
46.9
通讯作者:
Kun Wang;Liangzhen Hou;Xin Wang;Xiangwei Zhai;Zhaolian Lu;Zhike Zi;Weiwei Zhai;Xionglei He;C. Curtis;Danya Zhou;Zheng Hu
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文献类型:
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作者:
Kun Wang;Liangzhen Hou;Xin Wang;Xiangwei Zhai;Zhaolian Lu;Zhike Zi;Weiwei Zhai;Xionglei He;C. Curtis;Danya Zhou;Zheng Hu
Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data andCaenorhabditis elegansground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR–Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.