ArticleBioinformatics (Oxford, England)2015
Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose-response data.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 39 citations in OpenAlex.
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- A statistical simulation model to guide the choices of analytical methods in arrayed CRISPR screen experiments.PloS one · 2024Article
- Hit screening with multivariate robust outlier detection.PloS one · 2024Article
- Clinical forecasting of acute myeloid leukemia using ex vivo drug-sensitivity profiling.Cell reports methods · 2023Article
- The drug-induced phenotypic landscape of colorectal cancer organoids.Nature communications · 2022Article
- Screening for modulators of the cellular composition of gut epithelia via organoid models of intestinal stem cell differentiation.Nature biomedical engineering · 2022Article
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- Identification of hit compounds with anti-schistosomal activity on in vitro generated juvenile worms in cell-free medium.PLoS neglected tropical diseases · 2021Article
- Breeze: an integrated quality control and data analysis application for high-throughput drug screening.Bioinformatics (Oxford, England) · 2020Article
- Rethinking drug design in the artificial intelligence era.Nature reviews. Drug discovery · 2020Review
- Mapping the perturbome network of cellular perturbations.Nature communications · 2019Article
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- High Throughput SiRNA Screening for Chloropicrin and Hydrogen Fluoride-Induced Cornea Epithelial Cell Injury.Journal of visualized experiments : JoVE · 2018Article
- RNA Interference (RNAi) Screening inGenetics · 2018Article
- MightyScreen: An Open-Source Visualization Application for Screening Data Analysis.SLAS discovery : advancing life sciences R & D · 2018Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationMost data analysis tools for high-throughput screening (HTS) seek to uncover interesting hits for further analysis. They typically assume a low hit rate per plate. Hit rates can be dramatically higher in secondary screening, RNAi screening and in drug sensitivity testing using biologically active drugs. In particular, drug sensitivity testing on primary cells is often based on dose-response experiments, which pose a more stringent requirement for data quality and for intra- and inter-plate variation. Here, we compared common plate normalization and noise-reduction methods, including the B-score and the Loess a local polynomial fit method under high hit-rate scenarios of drug sensitivity testing. We generated simulated 384-well plate HTS datasets, each with 71 plates having a range of 20 (5%) to 160 (42%) hits per plate, with controls placed either at the edge of the plates or in a scattered configuration.
resultsWe identified 20% (77/384) as the critical hit-rate after which the normalizations started to perform poorly. Results from real drug testing experiments supported this estimation. In particular, the B-score resulted in incorrect normalization of high hit-rate plates, leading to poor data quality, which could be attributed to its dependency on the median polish algorithm. We conclude that a combination of a scattered layout of controls per plate and normalization using a polynomial least squares fit method, such as Loess helps to reduce column, row and edge effects in HTS experiments with high hit-rates and is optimal for generating accurate dose-response curves. CONTACT: john.mpindi@helsinki.fi. AVAILABILITY AND IMPLEMENTATION: Supplementary information: R code and Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.