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Fixed title formatting plus some typo found #4

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Fixed title formatting plus some typo found #4

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3 changes: 2 additions & 1 deletion README.md
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<img src="https://github.com/dimenwarper/scimitar/raw/master/logo.png" width="300">

## Single Cell Inference of MorphIng Trajectories and their Associated Regulation module

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.
* Infer full-fledged Gaussian distribution trajectories from single-cell data --- not only will you get cell orderings and estimated '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')
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