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Báo cáo y học: "Probe-level estimation improves the detection of differential splicing in Affymetrix exon array studies" docx

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Báo cáo y học: "Probe-level estimation improves the detection of differential splicing in Affymetrix exon array studies" docx

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Open Access Volume et al Laajala 2009 10, Issue 7, Article R77 Method Probe-level estimation improves the detection of differential splicing in Affymetrix exon array studies Essi Laajala*, Tero Aittokallio*†, Riitta Lahesmaa* and Laura L Elo*† Addresses: *Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, FI-20521, Finland †Department of Mathematics, University of Turku, Turku, FI-20014, Finland Correspondence: Laura L Elo Email: laliel@utu.fi Published: 16 July 2009 Genome Biology 2009, 10:R77 (doi:10.1186/gb-2009-10-7-r77) Received: 16 March 2009 Revised: June 2009 Accepted: 16 July 2009 The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2009/10/7/R77 © 2009 Laajala et al.; licensee BioMed Central Ltd This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited p>

A novel statistical procedure Detecting differential splicing is presented that uses probe-level information on Affymetrix exon arrays to detect differential splicing.

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Mục lục

  • Abstract

  • Background

  • Results

    • Performance in synthetic data

    • Reproducibility of detections in the mixture data

    • Reproducibility of detections between laboratories

    • Reproducibility of detections between independent subsamples

    • Detection of confirmed splicing events

    • Discussion

    • Conclusions

    • Materials and methods

      • Detection of differential splicing

        • Intensity model

        • Splicing index

        • Probe-level expression change averaging

        • PECA splicing index

        • FIRMA algorithm

        • Two-way ANOVA approaches

        • Filtering

        • Implementation

        • Datasets and evaluation criteria

          • Synthetic data

          • Mixture data

          • Between-laboratory comparison data

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