SCIMITAR provides a variety of tools to analyze trajectory maps of single-cell measurements.
With SCIMITAR you can:
- Obtain coarse-grain, (metastable) state and transition representations of your data. This is useful when you want to get a broad sense of how your data is connected.
- Infer full-fledged Gaussian distribution trajectories from single-cell data --- not only will you get cell orderings and estiamted 'pseudotemporal' mean measurements but also pseudo-time-dependant covariance matrices so you can track how your measurements' correlation change across biological progression.
- Obtain uncertainties for a cell's psuedotemporal positioning (due to uncertainty arising from heteroscedastic noise)
- Obtain genes that significantly change throughout the progression (i.e. 'progression-associated genes')
- Obtain genes that significantly change their correlation structure throughout the progression (i.e. 'progression co-associated genes')
- Infer broad co-regulatory states and psuedotemporal dynamic gene modules from the evolving co-expression matrices.
To install SCIMITAR, follow the steps below:
-
Install the pyroconductor package
-
Do the usual
python setup.py install -
Check out the jupyter notebooks tutorials in the tutorials directory
-
Questions, concerns, or suggestions? Thanks! Open up a ticket or pm @dimenwarper (Pablo Cordero)
If you use SCIMITAR please cite the paper ;)
- Cordero and Stuart, "Tracing co-regulatory network dynamics in noisy, single-cell transcriptome trajectories", Pac. Symp. of Biocomput. (2017)
Also, take a look at the talk slides.