PairGP: Gaussian process modeling of longitudinal data from paired multi-condition studies

Vantini, M.; Mannerström, H.; Rautio, S.; Ahlfors, H.; Stockinger, B.; Lähdesmäki, H.

doi:10.1101/2020.08.11.245621

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bioRxiv · 10.1101/2020.08.11.245621

PairGP: Gaussian process modeling of longitudinal data from paired multi-condition studies

Vantini, M.·Mannerström, H.·Rautio, S.·Ahlfors, H.·Stockinger, B.·Lähdesmäki, H.

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Abstract

We propose PairGP, a non-stationary Gaussian process method to compare gene expression timeseries across several conditions that can account for paired longitudinal study designs and can identify groups of conditions that have different gene expression dynamics. We demonstrate the method on both simulated data and previously unpublished RNA-seq time-series with five conditions. The results show the advantage of modeling the pairing effect to better identify groups of conditions with different dynamics. The implementations is available at https://github.com/michelevantini/PairGP

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Vantini, M., Mannerström, H., Rautio, S., Ahlfors, H., Stockinger, B., Lähdesmäki, H.. 2020-08-12. PairGP: Gaussian process modeling of longitudinal data from paired multi-condition studies. https://doi.org/10.1101/2020.08.11.245621

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