Anu Karinen, Sirpa Heinävaara, Reetta Nylund and Dariusz Leszczynski*
Corresponding author: Dariusz Leszczynski firstname.lastname@example.org
BMC Genomics 2008, 9:77 doi:10.1186/1471-2164-9-77
(2008-06-25 11:26) STUK
I do agree that independently of the strength of the statistical evidence, it would
be of great interest to know the identity of the affected proteins. However, as we
have stated in the article, we were unable, for technical reasons, to identify the
proteins using Maldi-ToF. We are presently making preparations for a larger study
(50 volunteers) in which we will attempt to confirm the results of the published pilot
study. In the new study, that will be executed using new technological approach, we
should be able to identify the affected proteins (we have learned from our "mistakes"...).
However, it will take 2-3 years of work before we will be able to name-the-names of
Co-author of the commented paper.
Miguel A Andrade-Navarro
(2008-06-24 17:46) Max Delbruck Center for Molecular Medicine
Significant or not, I am curious to know the nature of those eight proteins that were
No competing interests
(2008-04-16 11:29) STUK
I would not be so hasty. Statistical analysis is important no doubt about it. However
it shows only probability, not certainty... We had similar situation in our earlier
study (1). We have identified changes in expression of several proteins but the number
of affected proteins was small. However, one of the proteins (vimentin) that should
be according to statistics a “false positive”, as it “belonged to
the pool of false positives”, was not. When this protein’s expression
was tested with another method it appeared that it was indeed positive finding, and
not only quantitatively but also qualitatively. Therefore, one should be more cautious
before automatically throwing out everything for sake of statistical numbers that
show probability, not certainty. Hypothetically speaking, what if there would be identified
more than the 5% needed spots. Say 40 spots among the 579. Among these 40 spots would
be both real and false positives (2) - but statistically speaking we would have positive
finding. So, when the statistical analysis is done it is also necessary to look at
the proteins themselves and re-confirm whether the change is real or false. This especially
applies to weak stimuli that exert weak effects.
1. R. Nylund and D. Leszczynski, Proteomics analysis of human endothelial cell line
EA.hy926 after exposure to GSM 900 radiation. Proteomics 4, 1359-1365 (2004).
2. Leszczynski D. Letter to the Editor: Mobile phone radiation and gene expression.
Radiation Res. 167, 121 (2007).
Co-author of the commented paper
(2008-04-14 08:35) Jacobs University Bremen
I fully agree with Motulsky' comments on the statistical pitfalls of multiple comparisons.
This problem though is not new, and reviewers of manuscripts should know it.
(2008-03-04 17:56) GraphPad Software
The investigators compared the expression of 579 different proteins. If cell phone
radiation did nothing at all, you'd expect 5% of these comparisons to lead to 'statistically
significant' differences at a significance level of 5% (the authors state that they
did no correction for multiple corrections). Multiply 579 comparisons by 5% -- you'd
expect about 28 'statistically significant' changes just by chance. In fact, as the
authors note in the Discussion, the paper reports fewer 'significant' effects than
It seems to me that the effects (and statistics) presented in the paper are entirely
consistent with the hypothesis that cell phone radiation has no effect at all on protein
expression, and that the small P values are simply due to testing multiple statistical
hypotheses. It certainly is possible that cell phone radiation affects protein expression,
but this paper does not show convincing evidence of any such changes.
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