Crowell, A. M. | bioRxiv Science

bioRxiv2018

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

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

bioinformatics↗

bioRxiv2018

Learning and Imputation for Mass-spec Bias Reduction (LIMBR)

MotivationDecreasing costs are making it feasible to perform time series proteomics and genomics experiments with more replicates and higher resolution than ever before. With more replicates and time points, proteome and genome-wide patterns of expression are more readily discernible. These larger experiments require more batches exacerbating batch effects and increasing the number of bias trends. In the case of proteomics, where methods frequently result in missing data this increasing scale is also decreasing the number of peptides observed in all samples. The sources of batch effects and missing data are incompletely understood necessitating novel techniques.\n\nResultsHere we show that by exploiting the structure of time series experiments, it is possible to accurately and reproducibly model and remove batch effects. We implement Learning and Imputation for Mass-spec Bias Reduction (LIMBR) software, which builds on previous block based models of batch effects and includes features specific to time series and circadian studies. To aid in the analysis of time series proteomics experiments, which are often plagued with missing data points, we also integrate an imputation system. By building LIMBR for imputation and time series tailored bias modeling into one straightforward software package, we expect that the quality and ease of large-scale proteomics and genomics time series experiments will be significantly increased.\n\nContactalexander.m.crowell@gmail.com, jay.c.dunlap@dartmouth.edu

bioinformatics↗