James A Hanley

Publications (2)25.32 Total impact

• Article: Experimental design and statistical methods for improved hit detection in high-throughput screening.

  Nathalie Malo, James A Hanley, Graeme Carlile, Jing Liu, Jerry Pelletier, David Thomas, Robert Nadon
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  ABSTRACT: Identification of active compounds in high-throughput screening (HTS) contexts can be substantially improved by applying classical experimental design and statistical inference principles to all phases of HTS studies. The authors present both experimental and simulated data to illustrate how true-positive rates can be maximized without increasing false-positive rates by the following analytical process. First, the use of robust data preprocessing methods reduces unwanted variation by removing row, column, and plate biases. Second, replicate measurements allow estimation of the magnitude of the remaining random error and the use of formal statistical models to benchmark putative hits relative to what is expected by chance. Receiver Operating Characteristic (ROC) analyses revealed superior power for data preprocessed by a trimmed-mean polish method combined with the RVM t-test, particularly for small- to moderate-sized biological hits.
  Journal of Biomolecular Screening 09/2010; 15(8):990-1000. · 2.05 Impact Factor
• Source

  Available from: gene-quantification.org

  Article: Statistical practice in high-throughput screening data analysis.

  Nathalie Malo, James A Hanley, Sonia Cerquozzi, Jerry Pelletier, Robert Nadon
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  ABSTRACT: High-throughput screening is an early critical step in drug discovery. Its aim is to screen a large number of diverse chemical compounds to identify candidate 'hits' rapidly and accurately. Few statistical tools are currently available, however, to detect quality hits with a high degree of confidence. We examine statistical aspects of data preprocessing and hit identification for primary screens. We focus on concerns related to positional effects of wells within plates, choice of hit threshold and the importance of minimizing false-positive and false-negative rates. We argue that replicate measurements are needed to verify assumptions of current methods and to suggest data analysis strategies when assumptions are not met. The integration of replicates with robust statistical methods in primary screens will facilitate the discovery of reliable hits, ultimately improving the sensitivity and specificity of the screening process.
  Nature Biotechnology 03/2006; 24(2):167-75. · 23.27 Impact Factor