Prediction Interval Ranking Score: Identification of Invariant Expression from Time Series

Crowell, A. M.; Loros, J. J.; Dunlap, J. C.

doi:10.1101/482794

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bioRxiv · 10.1101/482794

Prediction Interval Ranking Score: Identification of Invariant Expression from Time Series

Crowell, A. M.·Loros, J. J.·Dunlap, J. C.

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Abstract

MotivationIdentification of constitutive reference genes is critical for analysis of gene expression. Large numbers of high throughput time series expression data are available, but current methods for identifying invariant expression are not tailored for time series. Identification of reference genes from these data sets can benefit from methods which incorporate the additional information they provide.\n\nResultsHere we show that we can improve identification of invariant expression from time series by modelling the time component of the data. We implement the Prediction Interval Ranking Score (PIRS) software, which screens high throughput time series data and provides a ranked list of reference candidates. We expect that PIRS will improve the quality of gene expression analysis by allowing researchers to identify the best reference genes for their system from publicly available time series.\n\nAvailabilityPIRS can be downloaded and installed with dependencies using pip install pirs and Python code and documentation is available for download at https://github.com/aleccrowell/PIRS.\n\nContactalexander.m.crowell@gmail.com

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Crowell, A. M., Loros, J. J., Dunlap, J. C.. 2018-11-29. Prediction Interval Ranking Score: Identification of Invariant Expression from Time Series. https://doi.org/10.1101/482794

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