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Reducing the algorithmic variability in transcriptome-based inference

Reducing the algorithmic variability in transcriptome-based inference
Reducing the algorithmic variability in transcriptome-based inference
Motivation: High-throughput measurements of mRNA abundances from microarrays involve several stages of preprocessing. At each stage, a user has access to a large number of algorithms with no universally agreed guidance on which of these to use. We show that binary representations of gene expressions, retaining only information on whether a gene is expressed or not, reduces the variability in results caused by algorithmic choice, while also improving the quality of inference drawn from microarray studies. Results: Binary representation of transcriptome data has the desirable property of reducing the variability introduced at the preprocessing stages due to algorithmic choice. We compare the effect of the choice of algorithms on different problems and suggest that using binary representation of microarray data with Tanimoto kernel for support vector machine reduces the effect of the choice of algorithm and simultaneously improves the performance of classification of phenotypes. Contact: mn@ecs.soton.ac.uk
1367-4803
1185-1191
Tuna, Salih
10b3ffcd-3ed8-4bd5-987a-4d26946d685d
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Tuna, Salih
10b3ffcd-3ed8-4bd5-987a-4d26946d685d
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Tuna, Salih and Niranjan, Mahesan (2010) Reducing the algorithmic variability in transcriptome-based inference Bioinformatics, 26, (9), pp. 1185-1191.

Record type: Article

Abstract

Motivation: High-throughput measurements of mRNA abundances from microarrays involve several stages of preprocessing. At each stage, a user has access to a large number of algorithms with no universally agreed guidance on which of these to use. We show that binary representations of gene expressions, retaining only information on whether a gene is expressed or not, reduces the variability in results caused by algorithmic choice, while also improving the quality of inference drawn from microarray studies. Results: Binary representation of transcriptome data has the desirable property of reducing the variability introduced at the preprocessing stages due to algorithmic choice. We compare the effect of the choice of algorithms on different problems and suggest that using binary representation of microarray data with Tanimoto kernel for support vector machine reduces the effect of the choice of algorithm and simultaneously improves the performance of classification of phenotypes. Contact: mn@ecs.soton.ac.uk

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Published date: May 2010
Organisations: Southampton Wireless Group

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Local EPrints ID: 271141
URI: http://eprints.soton.ac.uk/id/eprint/271141
ISSN: 1367-4803
PURE UUID: 87022313-5d36-454d-ad88-e0c0b907be6e

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Date deposited: 21 May 2010 11:48
Last modified: 18 Jul 2017 06:46

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Contributors

Author: Salih Tuna

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