id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
10_1101-2021_02_10_430705	Stassen, Shobana V.	VIA: Generalized and scalable trajectory inference in single-cell omics data	2021	24	.pdf	application/pdf	13590	1383	53	1 VIA: Generalized and scalable trajectory inference in single-cell omics data 1 VIA: Generalized and scalable trajectory inference in single-cell omics data 35 strategy to compute pseudotime, and reconstruct cell lineages based on lazy-teleporting random walks Step 1: Single-cell level graph is clustered such that each node 50 user defined start cell) is first computed by the expected hitting time for a lazy-teleporting random walk along an 57 network topology and single-cell level pseudotime/lineage probability properties onto an embedding using GAMs, as The cell fates and their lineage pathways are then computed by a two-stage probabilistic method, 94 graph-traversal allows it to infer cell fates when the underlying data spans combinations of multifurcating 201 detected cell fates annotated (o) lineage pathway and gene-pseudotime trend shown for the CD41 Megakaryocytic 259 Figure 3 VIA infers trajectories in single-cell multi-omic and image datasets (a) Major lineages of human Single cells are represented by graph nodes that are connected based on 	./cache/10_1101-2021_02_10_430705.pdf	./txt/10_1101-2021_02_10_430705.txt