Augmenting the bootstrap to analyze high dimensional genomic data

Svitlana Tyekucheva, Francesca Chiaromonte

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

The data produced by high-throughput genomic techniques are often high dimensional and undersampled. In these settings, statistical analyses that require the inversion of covariance matrices, such as those pursuing supervised dimension reduction or the assessment of interdependence structures, are problematic. In this article we show how the idea of adding noise to the bootstrap, pioneered by Efron, and Silverman and Young, in the late seventies and eighties, can be used to overcome undersampling and effectively estimate the inverse covariance matrix for data sets in which the number of observations is small relative to the number of variables. We demonstrate the performance of this approach, which we call augmented bootstrap, on simulated data and on data derived from genomic DNA sequences and microarray experiments.

Original languageEnglish (US)
Pages (from-to)1-18
Number of pages18
JournalTest
Volume17
Issue number1
DOIs
StatePublished - May 1 2008

Fingerprint

Bootstrap
Genomics
High-dimensional
Covariance matrix
DNA Microarray
Inverse matrix
Dimension Reduction
DNA Sequence
High Throughput
Inversion
Estimate
Demonstrate
Experiment
Microarray
Throughput
Dimension reduction
Interdependence

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

Cite this

Tyekucheva, Svitlana ; Chiaromonte, Francesca. / Augmenting the bootstrap to analyze high dimensional genomic data. In: Test. 2008 ; Vol. 17, No. 1. pp. 1-18.
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Augmenting the bootstrap to analyze high dimensional genomic data. / Tyekucheva, Svitlana; Chiaromonte, Francesca.

In: Test, Vol. 17, No. 1, 01.05.2008, p. 1-18.

Research output: Contribution to journalArticle

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