In:
Bioinformatics, Oxford University Press (OUP), Vol. 30, No. 7 ( 2014-04-01), p. 956-961
Kurzfassung:
Motivation: Gene expression data are currently collected on a wide range of platforms. Differences between platforms make it challenging to combine and compare data collected on different platforms. We propose a new method of cross-platform normalization that uses topic models to summarize the expression patterns in each dataset before normalizing the topics learned from each dataset using per-gene multiplicative weights. Results: This method allows for cross-platform normalization even when samples profiled on different platforms have systematic differences, allows the simultaneous normalization of data from an arbitrary number of platforms and, after suitable training, allows for online normalization of expression data collected individually or in small batches. In addition, our method outperforms existing state-of-the-art platform normalization tools. Availability and implementation: MATLAB code is available at http://morrislab.med.utoronto.ca/plida/. Contact: Amit.Deshwar@utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online.
Materialart:
Online-Ressource
ISSN:
1367-4811
,
1367-4803
DOI:
10.1093/bioinformatics/btt574
Sprache:
Englisch
Verlag:
Oxford University Press (OUP)
Publikationsdatum:
2014
ZDB Id:
1468345-3
SSG:
12
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