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Related Experiment Video

Updated: Jun 9, 2026

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Generalizing moving averages for tiling arrays using combined p-value statistics.

Katerina J Kechris¹, Brian Biehs, Thomas B Kornberg

¹University of Colorado Denver, CO, USA. katerina.kechris@ucdenver.edu

Statistical Applications in Genetics and Molecular Biology

|September 4, 2010

Summary

This summary is machine-generated.

This study introduces a novel method using combined p-value statistics for analyzing transcription factor binding regions identified by high-density tiling arrays. This approach enhances the prediction of genomic binding sites, improving upon traditional sliding window techniques.

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

High-density tiling arrays are crucial for identifying genome-wide transcription factor binding regions.
Sliding window methods analyzing log ratios or t-statistics are common for tiling array data analysis.

Purpose of the Study:

To present a generalized moving average method using combined p-value statistics for tiling array data.
To improve the identification and prediction of transcription factor binding regions.
To offer a framework for situations where averaging test statistics is inappropriate.

Main Methods:

Development of a sliding window approach evaluating p-values using combined statistics.
Application of the method to Drosophila tiling array data.
Integration of two different tiling array experiments for enhanced analysis.

Main Results:

Demonstration of the combined p-value method's effectiveness on Drosophila data.
Successful prediction of genomic regions enriched for transcription factor binding.
Evaluation of predictions based on proximity to target genes and known binding sites.

Conclusions:

The combined p-value framework offers a robust alternative for analyzing tiling array data.
This method enhances the accuracy of predicting transcription factor binding sites.
The generalized moving average approach provides a flexible tool for genomic data analysis.