SPEER (SPecific tissuE variant Effect predictoR) predicts tissue-specific regulatory effects of rare genetic variants using a hierarchial Bayesian model within a transfer learning framework. SPEER's advantages include:
- integration of functional genomic annotations (from DNA sequence alone) with tissue-specific gene expression
- separate predictions in each tissue while flexibly sharing information across tissues
- computationally efficient algorithm that scales well to a large number of variants.
To download the code:
git clone https://github.com/farhand7/SPEER
SPEER is written in Python and requires the following packages:
pandas, sklearn, numpy.
For a complete example of the SPEER pipeline using simulated data, see the ipython notebook
For details on the SPEER algorithm, see
To reproduce ROC curves using simulated data for all three settings described in the paper, see