The Annals of Applied Statistics

High-throughput data analysis in behavior genetics

Anat Sakov, Ilan Golani, Dina Lipkind, and Yoav Benjamini

Full-text: Open access

Abstract

In recent years, a growing need has arisen in different fields for the development of computational systems for automated analysis of large amounts of data (high-throughput). Dealing with nonstandard noise structure and outliers, that could have been detected and corrected in manual analysis, must now be built into the system with the aid of robust methods. We discuss such problems and present insights and solutions in the context of behavior genetics, where data consists of a time series of locations of a mouse in a circular arena. In order to estimate the location, velocity and acceleration of the mouse, and identify stops, we use a nonstandard mix of robust and resistant methods: LOWESS and repeated running median. In addition, we argue that protection against small deviations from experimental protocols can be handled automatically using statistical methods. In our case, it is of biological interest to measure a rodent’s distance from the arena’s wall, but this measure is corrupted if the arena is not a perfect circle, as required in the protocol. The problem is addressed by estimating robustly the actual boundary of the arena and its center using a nonparametric regression quantile of the behavioral data, with the aid of a fast algorithm developed for that purpose.

Article information

Source
Ann. Appl. Stat. Volume 4, Number 2 (2010), 743-763.

Dates
First available in Project Euclid: 3 August 2010

Permanent link to this document
http://projecteuclid.org/euclid.aoas/1280842138

Digital Object Identifier
doi:10.1214/09-AOAS304

Mathematical Reviews number (MathSciNet)
MR2758419

Citation

Sakov, Anat; Golani, Ilan; Lipkind, Dina; Benjamini, Yoav. High-throughput data analysis in behavior genetics. The Annals of Applied Statistics 4 (2010), no. 2, 743--763. doi:10.1214/09-AOAS304. http://projecteuclid.org/euclid.aoas/1280842138.


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