Biology subjects

Pietras, C. M.

Publications and source records attributed to Pietras, C. M..

2 recordsLinked to original sources

bioRxiv2020

Improving cell-specific drug connectivity mapping with collaborative filtering

MotivationDrug repositioning allows expedited discovery of new applications for existing compounds, but re-screening vast compound libraries is often prohibitively expensive. "Connectivity mapping" is a process that links drugs to diseases by identifying compounds whose impact on expression in a collection of cells reverses the diseases impact on expression in disease-relevant tissues. The high throughput LINCS project has expanded the universe of compounds and cell types for which data are available, but even with this effort, many potentially clinically useful combinations are missing. To evaluate the possibility of repurposing drugs this way despite missing data, we compared collaborative filtering with either neighborhood-based or SVD imputation methods to two naive approaches via cross-validation. ResultsMethods were evaluated for their ability to predict drug connectivity despite missing data. Predictions improved when cell type was taken into account. Neighborhood-based collaborative filtering was the most successful method, with the best improvements in non-immortalized primary cells. We also explored which classes of compounds are most and least reliant on cell type for accurate imputation, and we identified connections between related compounds even when many were not measured in the relevant cells. We conclude that even for cells in which drug responses have not been fully characterized, it is possible to identify unassayed drugs that reverse in those cells the expression signatures observed in disease. Contactdonna.slonim@tufts.edu

bioinformatics↗

bioRxiv2019

TEMPO: Detecting Pathway-Specific Temporal Dysregulation of Gene Expression in Disease

While many transcriptional profiling experiments measure dynamic processes that change over time, few include enough time points to adequately capture temporal changes in expression. This is especially true for data from human subjects, for which relevant samples may be hard to obtain, and for developmental processes where dynamics are critically important. Although most expression data sets sample at a single time point, it is possible to use accompanying temporal information to create a virtual time series by combining data from different individuals.\n\nWe introduce TEMPO, a pathway-based outlier detection approach for finding pathways showing significant temporal changes in expression patterns from such combined data. We present findings from applications to existing microarray and RNA-seq data sets. TEMPO identifies temporal dysregulation of biologically relevant pathways in patients with autism spectrum disorders, Huntingtons disease, Alzheimers disease, and COPD. Its findings are distinct from those of standard temporal or gene set analysis methodologies.\n\nOverall, our experiments demonstrate that there is enough signal to overcome the noise inherent in such virtual time series, and that a temporal pathway approach can identify new functional, temporal, or developmental processes associated with specific phenotypes.\n\nAvailabilityAn R package implementing this method and full results tables are available at bcb.cs.tufts.edu/tempo/.

bioinformatics↗