Statistics Papers

Document Type

Journal Article

Date of this Version

2010

Publication Source

The Annals of Applied Statistics

Volume

4

Issue

4

Start Page

1660

Last Page

1697

DOI

10.1214/10-AOAS363

Abstract

Large-scale statistical analysis of data sets associated with genome sequences plays an important role in modern biology. A key component of such statistical analyses is the computation of p-values and confidence bounds for statistics defined on the genome. Currently such computation is commonly achieved through ad hoc simulation measures. The method of randomization, which is at the heart of these simulation procedures, can significantly affect the resulting statistical conclusions. Most simulation schemes introduce a variety of hidden assumptions regarding the nature of the randomness in the data, resulting in a failure to capture biologically meaningful relationships. To address the need for a method of assessing the significance of observations within large scale genomic studies, where there often exists a complex dependency structure between observations, we propose a unified solution built upon a data subsampling approach. We propose a piecewise stationary model for genome sequences and show that the subsampling approach gives correct answers under this model. We illustrate the method on three simulation studies and two real data examples.

Keywords

genome structure correction (GSC), subsampling, piecewise stationary model, segementation-block bootstrap, feature overlap

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Date Posted: 27 November 2017

This document has been peer reviewed.