Volume 3 Supplement 2
Investigation of reproducibility of differentially expressed genes in DNA microarrays through statistical simulation
© Fan et al; licensee BioMed Central Ltd. 2009
Published: 10 March 2009
Recent publications have raised concerns about the reliability of microarray technology because of the lack of reproducibility of differentially expressed genes (DEGs) from highly similar studies across laboratories and platforms. The rat toxicogenomics study of the MicroArray Quality Control (MAQC) project empirically revealed that the DEGs selected using a fold change (FC)-based criterion were more reproducible than those derived solely by statistical significance such as P-value from a simple t-tests. In this study, we generate a set of simulated microarray datasets to compare gene selection/ranking rules, including P-value, FC and their combinations, using the percentage of overlapping genes between DEGs from two similar simulated datasets as the measure of reproducibility. The results are supportive of the MAQC's conclusion on that DEG lists are more reproducible across laboratories and platforms when FC-based ranking coupled with a nonstringent P-value cutoff is used for gene selection compared with selection based on P-value based ranking method. We conclude that the MAQC recommendation should be considered when reproducibility is an important study objective.
The utility of DNA microarrays has been demonstrated in clinical applications and risk/safety assessments [1–6]. With the wide variety of array platforms and analysis approaches, however, challenges remain in this field. For example, several publications [7–11] recently raised concerns about the reliability of microarray technology based on the lack of agreement in differentially expressed genes (DEGs) obtained from different laboratories and array platforms for highly similar study designs and experiments. By reanalyzing seven of the largest public DNA microarrays datasets aimed at cancer prognosis, Michiels et al. found that the signature genes of the classifiers were extremely unstable . The MicroArray Quality Control (MAQC) project conducted a large study using reference RNA samples and a toxicogenomics dataset [12, 13] revealed that the DEGs selected using fold change (FC)-based criterion were more stable in terms of reproducibility across labs and platforms than those derived solely from statistical significance measures such as P-value from simple t-tests. The MAQC study caused some to argue that the MAQC conclusion could be so broadly generalized. In response, this study sought to duplicate the finding of MAQC, except through statistical simulation using postulated datasets. Specifically, we generated a set of simulated microarray datasets with varying amount of noise, expression magnitude, and sample size in order to systematically compare the relationships among gene selection/ranking rules (i.e., P-value, FC and their combinations) with respect to reproducibility of DEGs.
Summary of the parameters used in this study.
MAQC main study
MAQC Rat toxicogenomics
5 per group
50 per group
Results and discussions
The study applied 24 simulated conditions (or 24 permutations) corresponding to two sample sizes, each having four values of CV and three different mean FC values, corresponding to Table 1. For each permutation, six gene selection methods were used to determine DEGs by comparing the treated group with the control group. These gene selection methods were (1) FC: genes are rank ordered by FC and DEGs determined by a FC cut-off only; (2–3) FC (P < 0.01) and FC (P < 0.05): genes are rank ordered first by FC and DEGs are determined by a P-value cutoff of either 0.01 and 0.05; (4) P: genes are rank ordered by P-value from the simple t-test and DEGs are selected using a specified P-value cutoff; and (5–6) P (FC > 1.4), and P (FC > 2): genes are rank ordered first by P-value and DEGs are then determined by either a FC = 1.4 or FC = 2 cutoff. Each permutation was repeated twice to mimic the process of conducting the same experiment in two different labs or two different platforms. The resulting DEGs from two simulations were compared to assess reproducibility across labs or platforms based on the percentage of overlapping genes (POG).
Whereas POG are affected by the degree of noise level, expression magnitude and sample size of the datasets, the above results clearly demonstrated that the DEGs become more reproducible, especially when fewer genes are selected, if the FC is included as the ranking criterion for subsequent DEGs identification. It is likely that the discordance of reported microarray results in literature is in large part due to the widespread of using P-based approach to rank genes over the FC-based method. The results of our another related study demonstrated that the relationship of the tradeoff between reproducibility and specificity/sensitivity in the FC (P) approach can be balanced by weighting the FC as a primary consideration in gene ranking: that is an FC criterion explicitly incorporates the measured quantity to ensure reproducibility, whereas a P criterion incorporates control of sensitivity and specificity .
Our simulation results show that the choice of gene selection method significantly affects apparent reproducibility of DEGs as measured by POG. Reproducibility as measured by POG between lists substantially increases when FC is the ranking criterion for identifying DEGs, especially for shorter gene lists. This observation holds for different noise levels, expression magnitudes and sample sizes. Our simulation are consistent with MAQC's conclusion that to generate more reproducible DEG lists across labs and platforms, the FC ranking with a nonstringent P-value cutoff, so named the FC (P) approach, should be considered when reproducibility is a consideration in a microarray study.
Xiaohui Fan thanks the support by ORISE research fellowship.
The views presented in this article do not necessarily reflect those of the U.S. Food and Drug Administration
This article has been published as part of BMC Proceedings Volume 3 Supplement 2, 2009: Proceedings of the First International Conference on Toxicogenomics Integrated with Environmental Sciences (TIES-2007). The full contents of the supplement are available online at http://www.biomedcentral.com/1753-6561/3?issue=S2.
- Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, Gascoyne RD, Muller-Hermelink HK, Smeland EB, Staudt LM: The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med. 2002, 346 (25): 1937-1947. 10.1056/NEJMoa012914.View ArticlePubMedGoogle Scholar
- van't Veer LJ, Dai HY, Vijver van de MJ, He YDD, Hart AAM, Mao M, Peterse HL, Kooy van der K, Marton MJ, Witteveen AT, et al: Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002, 415 (6871): 530-536. 10.1038/415530a.View ArticleGoogle Scholar
- Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, Ladd C, Beheshti J, Bueno R, Gillette M, et al: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA. 2001, 98 (24): 13790-13795. 10.1073/pnas.191502998.PubMed CentralView ArticlePubMedGoogle Scholar
- Pomeroy SL, Tamayo P, Gaasenbeek M, Sturla LM, Angelo M, McLaughlin ME, Kim JYH, Goumnerova LC, Black PM, Lau C, et al: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature. 2002, 415 (6870): 436-442. 10.1038/415436a.View ArticlePubMedGoogle Scholar
- Iizuka N, Oka M, Yamada-Okabe H, Nishida M, Maeda Y, Mori N, Takao T, Tamesa T, Tangoku A, Tabuchi H, et al: Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection. Lancet. 2003, 361 (9361): 923-929. 10.1016/S0140-6736(03)12775-4.View ArticlePubMedGoogle Scholar
- Fan C, Oh DS, Wessels L, Weigelt B, Nuyten DSA, Nobel AB, van't Veer LJ, Perou CM: Concordance among gene-expression-based predictors for breast cancer. N Engl J Med. 2006, 355 (6): 560-569. 10.1056/NEJMoa052933.View ArticlePubMedGoogle Scholar
- Marshall E: Getting the noise out of gene arrays. Science. 2004, 306 (5696): 630-631. 10.1126/science.306.5696.630.View ArticlePubMedGoogle Scholar
- Ioannidis JPA: Why most published research findings are false. PLos Med. 2005, 2 (8): e124-10.1371/journal.pmed.0020124.PubMed CentralView ArticlePubMedGoogle Scholar
- Simon R: Development and evaluation of therapeutically relevant predictive classifiers using gene expression profiling. J Natl Cancer Inst. 2006, 98 (17): 1169-1171. 10.1093/jnci/djj364.View ArticlePubMedGoogle Scholar
- Ein-Dor L, Zuk O, Domany E: Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc Natl Acad Sci USA. 2006, 103 (15): 5923-5928. 10.1073/pnas.0601231103.PubMed CentralView ArticlePubMedGoogle Scholar
- Michiels S, Koscielny S, Hill C: Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet. 2005, 365 (9458): 488-492. 10.1016/S0140-6736(05)17866-0.View ArticlePubMedGoogle Scholar
- Guo L, Lobenhofer EK, Wang C, Shippy R, Harris SC, Zhang L, Mei N, Chen T, Herman D, Goodsaid FM, et al: Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat Biotechnol. 2006, 24 (9): 1162-1169. 10.1038/nbt1238.View ArticlePubMedGoogle Scholar
- Shi LM, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC, Collins PJ, de Longueville F, Kawasaki ES, Lee KY, et al: The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol. 2006, 24 (9): 1151-1161. 10.1038/nbt1239.View ArticlePubMedGoogle Scholar
- Shi L, Jones WD, Jensen RV, Harris SC, Perkins RG, Goodsaid FM, Guo L, Croner LJ, Boysen C, Fang H, et al: The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies. BMC Bioinformatics. 2008, 9 (Suppl 9): S10-10.1186/1471-2105-9-S9-S10.PubMed CentralView ArticlePubMedGoogle Scholar
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