The Poisson Margin Test for Normalisation Free Significance Analysis of NGS Data

  • Adam Kowalczyk
  • Justin Bedo
  • Thomas Conway
  • Bryan Beresford-Smith
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6044)


Motivation: The current methods for the determination of the statistical significance of peaks and regions in NGS data require an explicit normalisation step to compensate for (global or local) imbalances in the sizes of sequenced and mapped libraries. There are no canonical methods for performing such compensations, hence a number of different procedures serving this goal in different ways can be found in the literature. Unfortunately, the normalisation has a significant impact on the final results. Different methods yield very different numbers of detected “significant peaks” even in the simplest scenario of ChIP-Seq experiments which compare the enrichment in a single sample relative to a matching control. This becomes an even more acute issue in the more general case of the comparison of multiple samples, where a number of arbitrary design choices will be required in the data analysis stage, each option resulting in possibly (significantly) different outcomes.

Results: In this paper we investigate a principled statistical procedure which eliminates the need for a normalisation step. We outline its basic properties, in particular the scaling upon depth of sequencing. For the sake of illustration and comparison we report the results of re-analysing a ChIP-Seq experiment for transcription factor binding site detection. In order to quantify the differences between outcomes we use a novel method based on the accuracy of in silico prediction by SVM-models trained on part of the genome and tested on the remainder.

Availability: The supplementary material is available at [1].


Support Vector Machine Peak Calling Peak Range Data Analysis Stage Support Vector Machine Predictor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Adam Kowalczyk
    • 1
    • 2
  • Justin Bedo
    • 1
    • 2
  • Thomas Conway
    • 1
    • 3
  • Bryan Beresford-Smith
    • 1
    • 2
  1. 1.Victoria Research LaboratoryNICTA 
  2. 2.Department of Electrical and Electronic Engineering 
  3. 3.Department of Computer Science and Software EngineeringThe University of MelbourneParkvilleAustralia

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