Fully scalable online-preprocessing algorithm for short oligonucleotide microarray atlases

TL;DR

This paper introduces a fully scalable online-learning algorithm for preprocessing short oligonucleotide microarray data, enabling efficient analysis of large collections without extensive memory use, applicable across platforms.

Contribution

The authors present a novel online-learning algorithm that scales linearly with data size, allowing probe-level preprocessing for microarrays on a large scale, unlike previous methods.

Findings

Scales linearly with sample size

Applicable to all short oligonucleotide platforms

Enables processing of tens of thousands of arrays

Abstract

Accumulation of standardized data collections is opening up novel opportunities for holistic characterization of genome function. The limited scalability of current preprocessing techniques has, however, formed a bottleneck for full utilization of contemporary microarray collections. While short oligonucleotide arrays constitute a major source of genome-wide profiling data, scalable probe-level preprocessing algorithms have been available only for few measurement platforms based on pre-calculated model parameters from restricted reference training sets. To overcome these key limitations, we introduce a fully scalable online-learning algorithm that provides tools to process large microarray atlases including tens of thousands of arrays. Unlike the alternatives, the proposed algorithm scales up in linear time with respect to sample size and is readily applicable to all short oligonucleotide platforms. This is the only available preprocessing algorithm that can learn probe-level parameters based on sequential hyperparameter updates at small, consecutive batches of data, thus circumventing the extensive memory requirements of the standard approaches and opening up novel opportunities to take full advantage of contemporary microarray data collections. Moreover, using the most comprehensive data collections to estimate probe-level effects can assist in pinpointing individual probes affected by various biases and provide new tools to guide array design and quality control. The implementation is freely available in R/Bioconductor at http://www.bioconductor.org/packages/devel/bioc/html/RPA.html