Optimal Design for High–Throughput Screening via False Discovery Rate Control

By Tao Feng, Pallavi Basu, Wenguang Sun, Hsun Teresa Ku, Wendy J. Mack
Statistics in Medicine [Ranked A in Mathematical Sciences] | March 2019

DOI

onlinelibrary.wiley.com/doi/abs/10.1002/sim.8144

Access full text

Citation

Tao Feng., Basu, Pallavi., Wenguang Sun., Hsun Teresa Ku., Wendy J. Mack. Optimal Design for High–Throughput Screening via False Discovery Rate Control Statistics in Medicine [Ranked A in Mathematical Sciences] onlinelibrary.wiley.com/doi/abs/10.1002/sim.8144.

Statistics in Medicine [Ranked A in Mathematical Sciences], 2019

Abstract

High‐throughput screening (HTS) is a large‐scale hierarchical process in which a large number of chemicals are tested in multiple stages. Conventional statistical analyses of HTS studies often suffer from high testing error rates and soaring costs in large‐scale settings. This article develops new methodologies for false discovery rate control and optimal design in HTS studies. We propose a two‐stage procedure that determines the optimal numbers of replicates at different screening stages while simultaneously controlling the false discovery rate in the confirmatory stage subject to a constraint on the total budget. The merits of the proposed methods are illustrated using both simulated and real data. We show that, at the expense of a limited budget, the proposed screening procedure effectively controls the error rate and the design leads to improved detection power.

Pallavi Basu is an Assistant Professor of Operations Management at the Indian School of Business (ISB), where she teaches concepts and approaches in Statistics. Her research interests include the application of statistics in finance, marketing, and other disciplines; high-dimensional statistical inference; large-scale multiple testing; and topics on causal inference.

Professor Basu is a member of the American Statistical Association, the Institute of Mathematical Statistics, and the International Indian Statistical Association. She received her PhD in Business Administration and Statistics from the USC Marshall School of Business and was a postdoctoral fellow at Tel Aviv University. She completed her undergraduate and postgraduate studies in Statistics (specialising in mathematical statistics and probability) from the Indian Statistical Institute (ISI), Kolkata.

Her current research (2023-2026) is partly funded by the Mathematical Research Impact Centric Support (MATRICS) from the Science and Engineering Research Board (SERB), Government of India.

More publications by author

Pallavi Basu