Finding differentially expressed genes in high dimensional data

Rank based test statistic via a distance measure

Sunil Mathur, Ajit Sadana

Research output: Contribution to journalArticle

Abstract

We present a rank-based test statistic for the identification of differentially expressed genes using a distance measure. The proposed test statistic is highly robust against extreme values and does not assume the distribution of parent population. Simulation studies show that the proposed test is more powerful than some of the commonly used methods, such as paired t-test, Wilcoxon signed rank test, and significance analysis of microarray (SAM) under certain non-normal distributions. The asymptotic distribution of the test statistic, and the p-value function are discussed. The application of proposed method is shown using a real-life data set.

Original languageEnglish (US)
Pages (from-to)968-979
Number of pages12
JournalStatistical Methods in Medical Research
Volume24
Issue number6
DOIs
StatePublished - Dec 1 2015

Fingerprint

Distance Measure
High-dimensional Data
Test Statistic
Gene
Wilcoxon Signed Rank Test
Genes
Non-normal Distribution
t-test
Extreme Values
p-Value
Microarray Analysis
Nonparametric Statistics
Microarray
Value Function
Asymptotic distribution
Demography
Simulation Study

Keywords

  • differential
  • efficient
  • genes
  • power
  • rank
  • test statistic

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

Cite this

Finding differentially expressed genes in high dimensional data : Rank based test statistic via a distance measure. / Mathur, Sunil; Sadana, Ajit.

In: Statistical Methods in Medical Research, Vol. 24, No. 6, 01.12.2015, p. 968-979.

Research output: Contribution to journalArticle

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