Dataset Information

Detecting strong signals in gene perturbation experiments: An adaptive approach with power guarantee and FDR control.

ABSTRACT: The perturbation of a transcription factor should affect the expression levels of its direct targets. However, not all genes showing changes in expression are direct targets. To increase the chance of detecting direct targets, we propose a modified two-group model where the null group corresponds to genes which are not direct targets, but can have small non-zero effects. We model the behavior of genes from the null set by a Gaussian distribution with unknown variance ? ². To estimate ? ², we focus on a simple estimation approach, the iterated empirical Bayes estimation. We conduct a detailed analysis of the properties of the iterated EB estimate and provide theoretical guarantee of its good performance under mild conditions. We provide simulations comparing the new modeling approach with existing methods, and the new approach shows more stable and better performance under different situations. We also apply it to a real data set from gene knock-down experiments and obtained better results compared with the original two-group model testing for non-zero effects.

SUBMITTER: Guan L

PROVIDER: S-EPMC7731979 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Detecting strong signals in gene perturbation experiments: An adaptive approach with power guarantee and FDR control.

Guan Leying L Chen Xi X Wong Wing Hung WH

Journal of the American Statistical Association 20190814

The perturbation of a transcription factor should affect the expression levels of its direct targets. However, not all genes showing changes in expression are direct targets. To increase the chance of detecting direct targets, we propose a modified two-group model where the null group corresponds to genes which are not direct targets, but can have small non-zero effects. We model the behavior of genes from the null set by a Gaussian distribution with unknown variance <i>τ</i> <sup>2</sup>. To es ...[more]

PMID: 33311819

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Project description:UnlabelledBackgroundBecause of the large volume of data and the intrinsic variation of data intensity observed in microarray experiments, different statistical methods have been used to systematically extract biological information and to quantify the associated uncertainty. The simplest method to identify differentially expressed genes is to evaluate the ratio of average intensities in two different conditions and consider all genes that differ by more than an arbitrary cut-off value to be differentially expressed. This filtering approach is not a statistical test and there is no associated value that can indicate the level of confidence in the designation of genes as differentially expressed or not differentially expressed. At the same time the fold change by itself provide valuable information and it is important to find unambiguous ways of using this information in expression data treatment.ResultsA new method of finding differentially expressed genes, called distributional fold change (DFC) test is introduced. The method is based on an analysis of the intensity distribution of all microarray probe sets mapped to a three dimensional feature space composed of average expression level, average difference of gene expression and total variance. The proposed method allows one to rank each feature based on the signal-to-noise ratio and to ascertain for each feature the confidence level and power for being differentially expressed. The performance of the new method was evaluated using the total and partial area under receiver operating curves and tested on 11 data sets from Gene Omnibus Database with independently verified differentially expressed genes and compared with the t-test and shrinkage t-test. Overall the DFC test performed the best - on average it had higher sensitivity and partial AUC and its elevation was most prominent in the low range of differentially expressed features, typical for formalin-fixed paraffin-embedded sample sets.ConclusionsThe distributional fold change test is an effective method for finding and ranking differentially expressed probesets on microarrays. The application of this test is advantageous to data sets using formalin-fixed paraffin-embedded samples or other systems where degradation effects diminish the applicability of correlation adjusted methods to the whole feature set.

Dataset Information

Detecting strong signals in gene perturbation experiments: An adaptive approach with power guarantee and FDR control.

Publications

Detecting strong signals in gene perturbation experiments: An adaptive approach with power guarantee and FDR control.

Similar Datasets

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