Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction - Nature Biotechnology

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Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction - Nature Biotechnology
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Mathematical model could bring us closer to effective stem cell therapies umich NatureBiotech

, Additional motif DTW alignment results showing time lags between TF gene expression and corresponding motif accessibility., The accessibility of TF motifs binned across latent time. The latent time scale was split into 20 equal-sized bins, and the average motif accessibility of cells in each bin was computed and plotted. The motif sequence logos are shown next to the TF names., Time-lag analysis of transcription factors and the expression of their validated downstream target genes.

Extended Data Fig. 4 Chromatin dynamics, Model 0, the necessity of chromatin preprocessing, and pre-fitting illustrations., Chromatin dynamics illustration: chromatin opening and closing are modeled as asymptotically approaching fully opened or fully closed starting from any initial value., Chromatin accessibility change as a function latent time inferred by scVelo using only the RNA portion of the 10X multiome mouse brain dataset .

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