pyVIA - Multi-Omic Single-Cell Trajectory Inference

VIA is a single-cell Trajectory Inference method that offers topology construction, pseudotimes, automated terminal state prediction and automated plotting of temporal gene dynamics along lineages. VIA combines lazy-teleporting random walks and Monte-Carlo Markov Chain simulations to overcome common challenges such as 1) accurate terminal state and lineage inference, 2) ability to capture combination of cyclic, disconnected and tree-like structures, 3) scalability in feature and sample space. 4) Generalizability to multi-omic analysis. In addition to transcriptomic data, VIA works on scATAC-seq, flow and imaging cytometry data. Please refer to our paper for more details.

VIA visualizes Mouse Gastrulation using time-series and RNA velocity adjusted graphs

VIA plots hi-res edge graph for Mouse Gastrulation (Pijuan Sala) human hematopoiesis

✳️ Fine-grained vector field without using RNA-velocity

Examples and Visualization

There are several Jupyter Notebooks here and on the github page with step-by-step code for real and simulated datasets. ✳️ The NB for multifurcating data shows a step-by-step usage tutorial.

scATAC-seq Human Hematopoiesis (click to open interactive VIA graph)

Notebooks

Notebook	details	dataset	reference
Multifurcation: Starter Tutorial	4-leaf simulation	4-leaf	DynToy
Disconnected Tutorial	disconnected simulation	4-leaf	DynToy
Human Embryoid	16,825 ESCs	EB scRNA-seq and embedding	Moon et al. (2019)
scATAC-seq Hematopoiesis	Human hematopoiesis	scATAC-seq	Buenrostro et al. (2018)
scRNA-seq Hematopoiesis	Human hematopoiesis (5780 cells)	CD34 scRNA-seq	Setty et al. (2019)