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Assessing consistency across functional screening datasets in cancer cells

Citation

Cai, Ling (2021), Assessing consistency across functional screening datasets in cancer cells, Dryad, Dataset, https://doi.org/10.5061/dryad.95x69p8kq

Abstract

Many high-throughput screening studies have been carried out in cancer cell lines to identify therapeutic agents and targets. Existing consistency assessment studies only examined two datasets at a time, with conclusions based on a subset of carefully selected features rather than considering global consistency of all the data. In this study we assembled nine compound screening datasets and three functional genomics datasets, and derived direct measures of consistency as well as indirect measures of consistency based on association between functional data and copy number-adjusted gene expression data. These results have been integrated into a web application – the Functional Data Consistency Explorer (FDCE), to allow users to make queries and generate interactive visualizations so that functional data consistency can be assessed for individual features of interest.

Methods

The source and processing methods were briefly described in https://lccl.shinyapps.io/FDCE/, dataset section.  The scripts for cleaning and processing the data are uploaded as scripts.zip. The source script and data for making the shiny app "Functional Data Consistency Explorer (FDCE)" is uploaded as app.zip.

Funding

National Cancer Institute, Award: P30CA142543

National Cancer Institute, Award: P50CA70907

National Cancer Institute, Award: R35CA22044901

National Institute of General Medical Sciences, Award: R35GM136375

Cancer Prevention and Research Institute of Texas, Award: RP190107

Cancer Prevention and Research Institute of Texas, Award: RP180805