Quantitative High Throughput Screening Data Quality Control Analysis R Shiny Application
Conference: Symposium on Data Science and Statistics (SDSS) 2023
05/24/2023: 1:55 PM - 2:00 PM CDT
Lightning
Quantitative high throughput screening (qHTS) assays can be used to evaluate the bioactivity of thousands of chemicals in a single experiment. The Tox21 program utilizes qHTS to prioritize testing of chemicals and predict their effects on humans and the environment. Within Tox21, there are numerous datasets with assay results for thousands of chemicals at different concentration levels, where each chemical is represented by multiple response profiles. Cluster Analysis by Subgroups using ANOVA (CASANOVA) is an automated quality control procedure to identify compounds within an assay that have consistent response patterns. To provide general accessibility to the method, an R Shiny web app has been developed to provide a user-friendly interface for running the CASANOVA analysis and for visualizing the resulting concentration-response profiles. This app enables scientists to easily run CASANOVA on their experimental data, reload previously completed CASANOVA analysis results, and display Tox21 CASANOVA results, without any prior knowledge in R. Visualization of clustered concentration-response curves allows scientists to better understand the main sources of variation in qHTS studies by scrutinizing chemicals that produce inconsistent responses among multiple concentration-response profiles. Concentration at half-maximal response (AC50) estimates are also calculated for each cluster to provide a quantitative measure of chemical potency.
R Shiny
CASANOVA
quantitative high throughput screening
concentration-response
Presenting Author
Guanhua Xie
First Author
Guanhua Xie
CoAuthor(s)
Shawn Harris, Social & Scientific Systems, Inc.
Keith Shockley, National Institute of Health
Shyamal Peddada, Biostatistics and Computational Biology Branch, Division of Intramural Research, NIEHS
Target Audience
Mid-Level
Tracks
Data Visualization
Symposium on Data Science and Statistics (SDSS) 2023
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