Apparently low reproducibility of true differential expression discoveries in microarray studies

Zhang, Min; Yao, Chen; Guo, Zheng; Zou, Jinfeng; Zhang, Lin; Xiao, Hui; Wang, Dong; Yang, Da; Gong, Xue; Zhu, Jing; Li, Yanhui; Li, Xia

doi:10.1093/bioinformatics/btn365

Cited by 114 publications

(127 citation statements)

References 49 publications

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“…However, for higher MV percentages the majority of genes will have at least one MV, so by evaluating a wide range of missing percentages we feel that addresses this issue indirectly. Another limitation is that the false discovery rate was not evaluated separately, and recent studies have shown that subsamples of expression matrices can produce gene lists with low FDRs (Zhang et al, 2008). One last point is our use of the term 'biological' impact.…”

Section: Discussionmentioning

confidence: 99%

Biological impact of missing-value imputation on downstream analyses of gene expression profiles

Kang

Brock

et al. 2010

Bioinformatics

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Motivation:Microarray experiments frequently produce multiple missing values (MVs) due to flaws such as dust, scratches, insufficient resolution or hybridization errors on the chips. Unfortunately, many downstream algorithms require a complete data matrix. The motivation of this work is to determine the impact of MV imputation on downstream analysis, and whether ranking of imputation methods by imputation accuracy correlates well with the biological impact of the imputation. Methods: Using eight datasets for differential expression (DE) and classification analysis and eight datasets for gene clustering, we demonstrate the biological impact of missing-value imputation on statistical downstream analyses, including three commonly employed DE methods, four classifiers and three gene-clustering methods. Correlation between the rankings of imputation methods based on three root-mean squared error (RMSE) measures and the rankings based on the downstream analysis methods was used to investigate which RMSE measure was most consistent with the biological impact measures, and which downstream analysis methods were the most sensitive to the choice of imputation procedure. Results: DE was the most sensitive to the choice of imputation procedure, while classification was the least sensitive and clustering was intermediate between the two. The logged RMSE (LRMSE) measure had the highest correlation with the imputation rankings based on the DE results, indicating that the LRMSE is the best representative surrogate among the three RMSE-based measures. Bayesian principal component analysis and least squares adaptive appeared to be the best performing methods in the empirical downstream evaluation.

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Section: Discussionmentioning

confidence: 99%

Biological impact of missing-value imputation on downstream analyses of gene expression profiles

Kang

Brock

et al. 2010

Bioinformatics

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“…On the one hand, when there are many highly correlated features, different ones may be selected under different settings [8]. On the other hand, even there are no redundant features, the existence of multiple non-correlated sets of real markers is also possible [15].…”

Section: Causes Of Instabilitymentioning

confidence: 99%

Stable feature selection for biomarker discovery

2010

Computational Biology and Chemistry

276

186

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Feature selection techniques have been used as the workhorse in biomarker discovery applications for a long time. Surprisingly, the stability of feature selection with respect to sampling variations has long been under-considered. It is only until recently that this issue has received more and more attention. In this article, we review existing stable feature selection methods for biomarker discovery using a generic hierarchal framework. We have two objectives: (1) providing an overview on this new yet fast growing topic for a convenient reference; (2) categorizing existing methods under an expandable framework for future research and development.

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“…The PO (percentage of overlaps) metric is frequently used to evaluate the consistency of different lists (26,36,37). Clearly, to be used as a disease marker for a disease, a differentially expressed miRNA should have a steady regulation pattern across different data sets.…”

Section: The Po Scorementioning

confidence: 99%

“…Importantly, this result also suggests that the differentially expressed miRNA in a particular cancer has a consistent up-or downregulation pattern. On the analysis of different samples for each cancer, the miRNA displaying the most pronounced differential expression varied greatly, which could be attributed to biological variation of the molecular change of miRNA in the samples (26). On the basis of the biological assumption that 2 differentially expressed miRNAs with significantly overlapped targets may have a similar function (27), we proposed a score entitled percentage of overlaps of function-related miRNAs (POF) to evaluate the functional consistency of differentially expressed miRNAs for a cancer.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Consistency of Differential Expression of MicroRNA Detected in Human Cancers

Gong¹,

Wu²,

Wang³

et al. 2011

Molecular Cancer Therapeutics

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Differential expression of microRNA (miRNA) is involved in many human diseases and could potentially be used as a biomarker for disease diagnosis, prognosis, and therapy. However, inconsistency has often been found among differentially expressed miRNAs identified in various studies when using miRNA arrays for a particular disease such as a cancer. Before broadly applying miRNA arrays in a clinical setting, it is critical to evaluate inconsistent discoveries in a rational way. Thus, using data sets from 2 types of cancers, our study shows that the differentially expressed miRNAs detected from multiple experiments for each cancer exhibit stable regulation direction. This result also indicates that miRNA arrays could be used to reliably capture the signals of the regulation direction of differentially expressed miRNAs in cancer. We then assumed that 2 differentially expressed miRNAs with the same regulation direction in a particular cancer play similar functional roles if they regulate the same set of cancer-associated genes. On the basis of this hypothesis, we proposed a score to assess the functional consistency between differentially expressed miRNAs separately extracted from multiple studies for a particular cancer. We showed although lists of differentially expressed miRNAs identified from different studies for each cancer were highly variable, they were rather consistent at the level of function. Thus, the detection of differentially expressed miRNAs in various experiments for a certain disease tends to be functionally reproducible and capture functionally related differential expression of miRNAs in the disease. Mol Cancer Ther; 10(5); 752–60. ©2011 AACR.

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Apparently low reproducibility of true differential expression discoveries in microarray studies

Cited by 114 publications

References 49 publications

Biological impact of missing-value imputation on downstream analyses of gene expression profiles

Biological impact of missing-value imputation on downstream analyses of gene expression profiles

Stable feature selection for biomarker discovery

Evaluating the Consistency of Differential Expression of MicroRNA Detected in Human Cancers

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