BMC Bioinformatics [electronic resource]
Algorithms; Computational Biology; Data Interpretation, Statistical; Diabetes Mellitus, Type 2; False Positive Reactions; Genetic Association Studies; Genome, Human; Humans; Islets of Langerhans; Male; Models, Statistical; Prostatic Neoplasms; Reproducibility of Results; Sequence Analysis, RNA
BACKGROUND: q-value is a widely used statistical method for estimating false discovery rate (FDR), which is a conventional significance measure in the analysis of genome-wide expression data. q-value is a random variable and it may underestimate FDR in practice. An underestimated FDR can lead to unexpected false discoveries in the follow-up validation experiments. This issue has not been well addressed in literature, especially in the situation when the permutation procedure is necessary for p-value calculation.
RESULTS: We proposed a statistical method for the conservative adjustment of q-value. In practice, it is usually necessary to calculate p-value by a permutation procedure. This was also considered in our adjustment method. We used simulation data as well as experimental microarray or sequencing data to illustrate the usefulness of our method.
CONCLUSIONS: The conservativeness of our approach has been mathematically confirmed in this study. We have demonstrated the importance of conservative adjustment of q-value, particularly in the situation that the proportion of differentially expressed genes is small or the overall differential expression signal is weak.
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Lai, Y. (2017). A Statistical Method for the Conservative Adjustment of False Discovery Rate (q-value).. BMC Bioinformatics [electronic resource], 18 (Suppl 3). http://dx.doi.org/10.1186/s12859-017-1474-6