Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose-response data

TY - JOUR

T1 - Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose-response data

AU - Mpindi, John-Patrick

AU - Swapnil, Potdar

AU - Dmitrii, Bychkov

AU - Jani, Saarela

AU - Saeed, Khalid

AU - Wennerberg, Krister

AU - Aittokallio, Tero

AU - Östling, Päivi

AU - Kallioniemi, Olli

N1 - © The Author 2015. Published by Oxford University Press.

PY - 2015/6/18

Y1 - 2015/6/18

N2 - Motivation: Most 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. Results: We 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.

AB - Motivation: Most 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. Results: We 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.

KW - Algorithms

KW - Antineoplastic Agents/pharmacology

KW - Data Interpretation, Statistical

KW - Dose-Response Relationship, Drug

KW - Drug Evaluation, Preclinical

KW - High-Throughput Screening Assays/methods

KW - Humans

KW - Male

KW - Normal Distribution

KW - Prostatic Neoplasms/drug therapy

KW - Tumor Cells, Cultured

U2 - 10.1093/bioinformatics/btv455

DO - 10.1093/bioinformatics/btv455

M3 - Journal article

C2 - 26254433

SN - 1367-4811

VL - 31

SP - 3815

EP - 3821

JO - Bioinformatics (Online)

JF - Bioinformatics (Online)

IS - 23

ER -