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©1996-2010 All Rights Reserved. Online Journal of Bioinformatics.  You may not store these pages in any form except for your own personal use. All other usage or distribution is illegal under international copyright treaties. Permission to use any of these pages in any other way besides the  before mentioned must be gained in writing from the publisher. This article is exclusively copyrighted in its entirety to OJB publications. This article may be copied once but may not be, reproduced or  re-transmitted without the express permission of the editors. Linking: To link to this page or any pages linking to this page you must link directly to this page only here rather than put up your own page.



Online Journal of Bioinformatics

REFEREE FORM



Please complete and remit to Editorial office, Online Journal of Bioinformatics


Author(s):  Haan et al,
ID Number: 42319Q
Title:Beyond
single p-value cutoffs: Methods to improve decision making in GO enrichment analysis of microarray experiments, de Haan et al,



The Editorial board must ensure that the OJB publishes only papers which are scientifically sound. To achieve this objective, the referees are requested to assist the Editors by making an assessment of a paper submitted for publication by:

(a)  Writing a report on the reverse side of this form,
(b} Check the boxes shown below under 1. and  2. ( YES or NO) [N.B.A "NO" assessment must be supported by specific comment in the report.
(c)  Make a recommendation under 3.

The Editor-in-Chief would appreciate hearing from any referee who feels that he/she will be unable to review a manuscript within four weeks.

1. CRITERIA FOR JUDGEMENT (Mark "Yes" or "No").
 

Is the work scientifically sound? Yes

Is the work an original contribution  No see below
Are the conclusions justified on the evidence presented? yes
Is the work free of major errors in fact, logic or technique? yes
Is the paper clearly and concisely written? No see below
Do you consider that the data provided on the care and use of animals (See Instructions to Contributors) is sufficient to establish that the animals used in the experiments were well looked after, that care was taken to avoid distress, and that there was no unethical use of animals? NA


2  PRESENTATION (Mark "Yes" or "No").
 

Does the title clearly indicate the content of the paper?  Yes
Does the abstract convey the essence of the article? No
Are all the tables essential? Yes
Are the figures and drawings of good quality? yes
Are the illustrations necessary for an understanding of the text?  Yes
Is the labelling adequate? Yes


3. RECOMMENDATIONS(Mark one with an X)
 

Not suitable for publication in the OJB
Reassess after major changes
Reassess after suggested changes X
Accept for publication with minor changes
Accept for publication without changes


4. REPORT  

Appended below is my review for de Haan et al. I could not locate the OJB review form though. Let me know if you require scores.
I recommend accept subject to major revision. The paper has some interesting ideas but does not convey any deeper understanding of them.

Review Beyond single p-value cutoffs: Methods to improve decision making in GO enrichment analysis of microarray experiments, de Haan et al, submitted to Online Journal of Bioinformatics

Summary

The paper outlines an assortment of ways of visualising and understanding GO term enrichment in sets of genes. More specifically, the analysis allows both the gene selection criterion and the enrichment significance criterion to vary. This enables the user to gauge how solidly GO terms are associated with their expression data. The authors examine the methods of analysis on a human mesenchymal stem cell microarray data set.


Comments

Overall, the paper is moderately interesting. It proposes and illustrates the possible uses of range of novel but related methods of interest to researchers generating and analysing gene expression data. The paper does not provide a deep theoretical underpinning of the methods, but relies heavily on an informed user “browsing” for possible Gene Ontology-based interpretations of their expression data. In fact, the paper is very informal and relies on a large number of schematic illustrations rather than real examples.

Abstract

The abstract does not adequately reflect the problem/context, approach and results. Instead it is littered with buzzwords, and talks about details of little relevance to understand the above.

Introduction

The paper is not well written. The authors should make a major effort in trying to re-work the material to allow the unprepared reader to digest the idea and to appreciate the potential use. In particular, the introduction is very convoluted and it is hard to untangle what is meant with cut-off for “GO term enrichment”, then cut-off for “number of selected genes” without properly introducing the steps preceding these, e.g. how is the gene selection p-value calculated? The authors should properly define all the terminology used.

The motivation for the ROC-style analysis, to identify predictions that are found by chance alone, is confusing. The point of correcting for multiple tests/false discovery rate/E-value determination is to get a handle of the occurrence of chance predictions—but these are clearly stated to be of no interest to the paper in a few paragraphs later.

Methods

The GO term clustering is based on terms being organised into trees, but the ontologies are not trees, they are directed acyclic graphs.
Overall, the methods presented in section 2 are not clearly described, e.g. the “sliding window” method for gene selection is not unambiguously defined, e.g. what is the “ordering” referring to? This may be obvious to the authors but the reader has to guess (or possibly extrapolate from schematic illustrations and/or examples) to resolve what the methods do. I suggest that the authors try to formalise their methods and use real data to illustrate what they do.

Results and conclusion

It is difficult to draw any conclusions from the results presented. If the main conclusion is that using this assortment of methods for exploring the “GO space” is better than just using the “single p-value” approach, the authors should at least attempt to substantiate this position. Only one example microarray data set is used, but what would the outcomes be from using the standard approach as opposed to the proposed approaches?

 

AUTHORS REPLY dated 8th January 2010

 

Please find attached a submission for the Online Journal of Bioinformatics entitled "Beyond single p-value cutoffs: methods to improve decision making in GO enrichment analysis of microarray experiments" by De Haan et al. It is a revised version of a paper submitted May 18, 2008. We have taken the comments of the referees (your email of Jun 9, 2008) into account in completely revising and rewriting the manuscript, and feel that this version has markedly improved.

 

The paper describes tools to investigate the effects of using different significance cutoffs, both in selecting relevant genes and in deciding when enrichment is significant.

 

We hope that you will find it suitable for publication in your journal.


1996-2010 All Rights Reserved. Online Journal of Bioinformatics.  You may not store these pages in any form except for your own personal use. All other usage or distribution is illegal under international copyright treaties. Permission to use any of these pages in any other way besides the  before mentioned must be gained in writing from the publisher. This article is exclusively copyrighted in its entirety to OJB publications. This article may be copied once but may not be, reproduced or  re-transmitted without the express permission of the editors. Linking: To link to this page or any pages linking to this page you must link directly to this page only here rather than put up your own page.



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