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 |
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Disconnected Tutorial |
disconnected simulation |
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Human Embryoid |
16,825 ESCs |
Moon et al. (2019) |
|
scATAC-seq Hematopoiesis |
Human hematopoiesis |
Buenrostro et al. (2018) |
|
scRNA-seq Hematopoiesis |
Human hematopoiesis (5780 cells) |
CD34 scRNA-seq |
Setty et al. (2019) |