- Open Access
Correspondence regarding "Effect of active smoking on the human bronchial epithelium transcriptome"
© Zuyderduyn; licensee BioMed Central Ltd. 2009
- Received: 04 February 2008
- Accepted: 18 February 2009
- Published: 18 February 2009
In the work of Chari et al. entitled "Effect of active smoking on the human bronchial epithelium transcriptome" the authors use SAGE to identify candidate gene expression changes in bronchial brushings from never, former, and current smokers. These gene expression changes are categorized into those that are reversible or irreversible upon smoking cessation. A subset of these identified genes is validated on an independent cohort using RT-PCR. The authors conclude that their results support the notion of gene expression changes in the lungs of smokers which persist even after an individual has quit.
This correspondence raises questions about the validity of the approach used by the authors to analyze their data. The majority of the reported results suffer deficiencies due to the methods used. The most fundamental of these are explained in detail: biases introduced during data processing, lack of correction for multiple testing, and an incorrect use of clustering for gene discovery. A randomly generated "null" dataset is used to show the consequences of these shortcomings.
Most of Chari et al.'s findings are consistent with what would be expected by chance alone. Although there is clear evidence of reversible changes in gene expression, the majority of those identified appear to be false positives. However, contrary to the authors' claims, no irreversible changes were identified. There is a broad consensus that genetic change due to smoking persists once an individual has quit smoking; unfortunately, this study lacks sufficient scientific rigour to support or refute this hypothesis or identify any specific candidate genes. The pitfalls of large-scale analysis, as exemplified here, may not be unique to Chari et al.
- False Discovery Rate
- Current Smoker
- Gene Expression Change
- GSK3B Expression
- Current Smoker Group
I read with interest the recent work, published in BMC Genomics, entitled "Effect of active smoking on the human bronchial epithelium transcriptome" . In this article, the authors present an analysis of gene expression profiles generated using serial analysis of gene expression (SAGE) . The profiles were obtained from samples of the lung epithelium taken from individuals who have never smoked, had quit smoking, or are current smokers. The authors highlight the differences in gene expression between the three groups, with a particular focus on a substantial number of changes that appear to persist after an individual has stopped smoking. These results are intriguing, as they suggest that some etiology is permanently maintained after smoking cessation, that this manifests itself at the level of gene expression, and that such changes may contribute to an increased risk of developing lung cancer. The findings were widely reported in the mainstream media [3, 4]. However, I have serious concerns about the statistical methods the authors employed and, consequently, the validity of the conclusions drawn.
Summary of criticisms.
poor definition of "preferential" expression
introduces unchecked bias from different group sizes
incorrect use of Venn diagram
confounds overall sense of group-specific differences
use of raw tag counts to determine "preferential" expression
introduces unchecked bias from different library sizes
data filtered using criteria that includes variable to be tested
pre-selects for data more likely to be found significant, confounding estimated of false discovery rate (FDR)
significance threshold set to p ≤ 0.05 without adjusting for multiple testing
false discovery rate (FDR) could be very high
"significant" results undergo post hoc fold-change filter
low tag counts more likely to pass the filter, yet these more likely to represent random variation
other possible null hypotheses not tested
not possible to check for consistency with known biology
null hypotheses formed with 2 of the 3 sample types
loss of power
data selected for differential expression is clustered
formation of distinct clusters is meaningless
genes tested for consistency with third sample group restricted to genes pre-selected as different between original two groups
flaws in implementation of first hypothesis test become propagated and amplified in second hypothesis test
no RT-PCR of irreversible genes
no validation of irreversible gene expression hypothesis
evidence for GSK3B as an irreversible gene is weak or supports reversible hypothesis
selection of GSK3B for further experimentation is not indicated
tags per million (TPM) used in statistical testing rather than for reporting purposes only
artificially inflates non-zero counts
some SAGE tags incorrectly mapped
a) follow-up RT-PCR is not validation, b) evidence for involvement of COX2 pathway is weaker than implied
The relationship between the transcriptomes of never, former, and current smokers
Chari et al. show the gross relationship between sample types in terms of the number of tags preferentially expressed in different subsets of the three smoking status groups (never, former, and current smokers). This is often seen in published SAGE studies, and it serves to orient the reader to the data and provide a preliminary sense of group-specific differences. A Venn diagram, like that found in Chari et al., is a popular way of visualizing the results. However, there are two concerns with the authors' particular application of this method.
First, the criterion for "preferential" expression is unconventional. Specifically, they state "the criteria chosen for preferential expression was a threshold of a raw tag count of ≥ 2 across all samples in a particular set, but not existing in the other sets". This would seem to imply that a tag is preferentially expressed if it appears at the given threshold in all of the libraries of one group, and is below the threshold in all of the libraries of the other two groups. However, an inspection of the authors' supplementary material reveals that "not existing in other sets" can mean that a raw tag count of 0 or 1 appears in any library in both of the competing groups. For example, tag CGGTTTGCAT is considered preferentially expressed in former smokers and has the following counts: current = (1, 16, 12, 5, 17, 14, 16, 8), former = (19, 11, 8, 11, 2, 8, 5, 7, 6, 8, 3, 11), and never = (15, 0, 11, 11). However, the average expression in each group is: current = 100.7 TPM, former = 59.9 TPM, and never = 67.6 TPM. This problem is quite pronounced for a significant fraction of tags preferentially expressed in each of the never, former, and current smoker groups: 57/227 (25%), 33/102 (32%), and 635/2013 (32%), respectively, actually have a higher mean expression in one or both of the competing groups!
For these reasons, the results reported by the authors are of little use in determining a relationship between never, current and former smoker transcriptomes. An alternative would be to use much higher expression thresholds that apply equally to all libraries within a group to reduce the effects of sampling variation and differences in library and group sizes (e.g. ≥50 TPM versus ≤10 TPM). Principal component analysis or hierarchical clustering of the data are also good alternatives to visualize the overall relationship between profiles.
Multiple testing and the determination of differentially expressed tags
In a review of microarray studies used to explore cancer outcomes, Dupuy and Simon identified the failure to adequately control for false positives as a result of multiple testing as the "most common and serious" flaw they observed in procedures for gene selection . The problem can be grasped intuitively when considering a hypothesis test of 10,000 genes (or, in this case, tags). If one considers a p-value ≤ 0.05 as being significant, then we expect that 500 genes will be identified by chance alone. If 1500 genes are subsequently identified from the actual data, we can estimate that 500/1500 = 33% of these will be false positives. That this problem remains an issue in published studies is truly unfortunate, since a range of methods are available to overcome this problem. For instance, a simple Bonferroni correction or the Benjamini-Hochberg method are widely accepted and commonly used [6, 7]. These methods can provide corrected p-values or estimates of the false discovery rate (FDR) for a given threshold p-value. Moreover, powerful resampling approaches (like that used to estimate the FDR in this correspondence) are precise and increasingly common with the rise in available computing power. Regardless of the method used, the objective is to determine a statistical cutoff that results in a reasonable number of false positives. An acceptable adjusted p-value or FDR is somewhat arbitrary, but for the latter metric a value lower than 10–20% is commonly cited .
In Chari et al., the authors' failure to account for multiple testing or to provide some estimate of the FDR drastically diminishes the value of their findings. The authors use a Mann-Whitney U statistic (a non-parametric test) on each tag to calculate the p-value for the null hypothesis that never smokers are the same as current smokers. The authors restrict their testing to tags with a "mean tag count of ≥20 tags per million (TPM) in at least one of never, former or current smoker SAGE libraries". This introduces a bias by pre-selecting tags for testing based on criteria that includes the variable being tested (in this case, smoking status). This bias could be reasonably addressed by filtering using a criterion of a mean expression of 20 TPM across all libraries.
In any event, this filter produces a reduced test set of 8,148 tags to which the statistic is applied. The authors select those tags with p ≤ 0.05 and then apply a fold-change criterion of ≥2. Before more fully exploring the central issue of multiple testing, it should be briefly mentioned that the fold-change criteria is somewhat dubious. It is easier for tags with low counts to meet this threshold than for those with higher counts. For example, assuming libraries consisting of 100,000 tags, a count of 1 versus a count of 3 represents a 3-fold change (p = 0.62, χ2-test), while the more significant difference of a count of 50 versus a count of 75 represents only a 1.5-fold change (p = 0.032, χ2-test). This is a concern when using a non-parametric test like the Mann-Whitney U with SAGE, since the relative rank of expression rather than the actual difference is used to provide statistical inference.
To demonstrate the larger problem of multiple testing, I compared the number of tags that meet a given threshold p-value in the actual dataset against the average number obtained from the null dataset in order to estimate the FDR. One must also use the number of results found prior to the use of a post hoc filter like fold-change, and I do so here. It's not clear from Chari et al. whether all 8,148 tags or a smaller number representing a minimum of 20 TPM in only the never or current smoker groups were used. In my estimates of the FDR, I use the more generous assumption of the latter (7,764 tags). Of these, 885 tags have p ≤ 0.05 and 609 of these pass the fold-change filter. When this identical analysis is applied to the null dataset, 7,406 tags are expressed at a mean of 20 TPM in either the never or current smoker groups. Of these, 418 tags have p ≤ 0.05 and 195 of these pass the fold-change filter. Therefore, we can estimate the FDR as 418/885 = 47.2%. This means that, at a minimum, nearly half of the authors' findings are false positives. The true FDR will be higher due to the initial biased filter of group-specific expression of ≥20 TPM and still higher in the reduced set of 609 tags because of the bias introduced by the fold-change filter.
Estimated false discovery rate (FDR) of null hypotheses using different combinations of the three groups
p ≤ 0.05
fold-change ≥ 2
p ≤ 0.05
fold-change ≥ 2
N = C
N = F
F = C
(N, F) = C
N = (F, C)
(N, C) = F
There do appear to be changes that correlate with the individual groups, since the estimated FDR is well below 100%. However, the differences are clearly difficult to distinguish from random variation in the dataset and most certainly do not support the irreversible/reversible dichotomy imposed by the authors. Most likely, there are other explanatory variables that would better explain the structure of the data. If such covariates were even weakly correlated with smoking status, they may well produce the results shown in this correspondence. In fact, several enticing variables are listed in the authors' own sample descriptions: age, pack-years, lung function, and years since quitting.
Supervised clustering of never, former, and current smoker profiles
Chari et al. use a technique they refer to as "supervised clustering". Dupuy and Simon describe this approach as the "most common and serious flaw" when performing class discovery . Specifically, one cannot state that clusters corresponding with a variable (i.e. smoking status) are meaningful if the input genes were selected for a correlation with the same variable. In Chari et al., the authors cluster the 609 tags selected based on their differential expression between never and current smokers. Since the tags were selected based on this very property, it is not surprising that distinct clusters emerge; however, these clusters are not meaningful.
For these reasons, this section of the authors' results is almost meaningless. The little inference that can be made actually indicates a situation at odds with the authors' stated conclusions. Hierarchical clustering and principal component analysis are powerful techniques in class discovery and, as alluded to previously, would have been better applied at the beginning of the authors' paper in an unsupervised manner on an unbiased set of data that does not consider smoking status.
Although the key concerns have been described above, a number of additional issues with the authors' selection of irreversible and reversible genes, as well as the RT-PCR validation efforts, were removed for brevity. These have been included as a supplementary document (see Additional File 1).
Large-scale data analysis is not trivial and flaws are often found in the published literature. It is unfortunate that this perceived complexity and unreliability of large data set analyses have caused many to be wary of gene expression studies when they hold so much potential to provide new insights. Many of the pitfalls of a large-scale analysis can be avoided by keeping a few basic tenets in mind. The data as presented supports the idea that changes to gene expression occur as a result of active smoking, but provides no compelling evidence that these changes persist once an individual has quit. Given that this study was widely publicized, these critical shortcomings become even more important to communicate. This is not to say that such permanent changes do not exist, or that such changes are not identifiable from the dataset the authors use. However, my perspective is that Chari et al. requires major revisions to support their stated conclusions or to consider the work as a basis for decision making in the design of future research.
Jonathan Sheps, Melvin Kwok, Tania Kastelic, Maisie Lo, and Victor Ling for helpful discussion. I especially value their support of the virtues of vigorous and healthy scientific debate.
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