Unsupervised Classification for Tiling Arrays: ChIP-chip and Transcriptome

TL;DR

This paper introduces an unsupervised classification method for tiling array data that jointly analyzes expression differences and transcribed regions, incorporating biological knowledge and probe dependencies for improved accuracy.

Contribution

It presents a novel unsupervised classification approach that models joint distributions and accounts for probe and biological region dependencies, advancing tiling array analysis.

Findings

Enhanced detection of transcribed regions and expression differences.

Importance of precise modeling and region classification demonstrated.

Application to Arabidopsis data validates the method's effectiveness.

Abstract

Tiling arrays make possible a large scale exploration of the genome thanks to probes which cover the whole genome with very high density until 2 000 000 probes. Biological questions usually addressed are either the expression difference between two conditions or the detection of transcribed regions. In this work we propose to consider simultaneously both questions as an unsupervised classification problem by modeling the joint distribution of the two conditions. In contrast to previous methods, we account for all available information on the probes as well as biological knowledge like annotation and spatial dependence between probes. Since probes are not biologically relevant units we propose a classification rule for non-connected regions covered by several probes. Applications to transcriptomic and ChIP-chip data of Arabidopsis thaliana obtained with a NimbleGen tiling array highlight the importance of a precise modeling and the region classification.