Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 447771
Title A fully scalable online pre-processing algorithm for short oligonucleotide microarray atlases
Author(s) Lahti, L.M.; Torrente, A.; Elo, L.L.; Brazma, A.; Rung, J.
Source Nucleic Acids Research 41 (2013)10. - ISSN 0305-1048 - 10 p.
DOI https://doi.org/10.1093/nar/gkt229
Department(s) Microbiological Laboratory
VLAG
Publication type Refereed Article in a scientific journal
Publication year 2013
Keyword(s) robust multiarray analysis - probe level data - expression index computation - gene-expression - normalization methods - affymetrix exon - analysis frma - arrays - microbiota - summaries
Abstract Rapid accumulation of large and standardized microarray 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 these data resources. Although short oligonucleotide arrays constitute a major source of genome-wide profiling data, scalable probe-level techniques have been available only for few platforms based on pre-calculated probe effects from restricted reference training sets. To overcome these key limitations, we introduce a fully scalable online-learning algorithm for probe-level analysis and pre-processing of large microarray atlases involving tens of thousands of arrays. In contrast to the alternatives, our algorithm scales up linearly with respect to sample size and is applicable to all short oligonucleotide platforms. The model can use the most comprehensive data collections available to date to pinpoint individual probes affected by noise and biases, providing tools to guide array design and quality control. This is the only available 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 collections
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