Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values

Bioinformatics. 2003 Jul 1;19(10):1236-42. doi: 10.1093/bioinformatics/btg148.

Abstract

Motivation: The occurrence of false positives and false negatives in a microarray analysis could be easily estimated if the distribution of p-values were approximated and then expressed as a mixture of null and alternative densities. Essentially any distribution of p-values can be expressed as such a mixture by extracting a uniform density from it.

Results: The occurrence of false positives and false negatives in a microarray analysis could be easily estimated if the distribution of p-values were approximated and then expressed as a mixture of null and alternative densities. Essentially any distribution of p-values can be expressed as such a mixture by extracting a uniform density from it.

Availability: An S-plus function library is available from http://www.stjuderesearch.org/statistics.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.
  • Validation Study

MeSH terms

  • Adaptor Proteins, Signal Transducing*
  • Algorithms*
  • Animals
  • B-Cell CLL-Lymphoma 10 Protein
  • B-Lymphocytes / metabolism
  • False Negative Reactions
  • False Positive Reactions
  • Gene Expression Profiling / methods*
  • Mice
  • Neoplasm Proteins / genetics*
  • Oligonucleotide Array Sequence Analysis / methods*
  • Quality Control
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Sequence Alignment
  • Sequence Analysis / methods*

Substances

  • Adaptor Proteins, Signal Transducing
  • B-Cell CLL-Lymphoma 10 Protein
  • Bcl10 protein, mouse
  • Neoplasm Proteins