A new class of mixture models for differential gene expression in DNA microarray data

Do NIR spectra collected from laboratory-reared mosquitoes differ from those collected from wild mosquitoes?

BACKGROUND

Near infrared spectroscopy (NIRS) is a high throughput technique that measures absorbance of specific wavelengths of light by biological samples and uses this information to classify the age of lab-reared mosquitoes as younger or older than seven days with an average accuracy greater than 80%. For NIRS to estimate ages of wild mosquitoes, a sample of wild mosquitoes with known age in days would be required to train and test the model. Mark-release-recapture is the most reliable method to produce wild-caught mosquitoes of known age in days. However, it is logistically demanding, time inefficient, subject to low recapture rates, and raises ethical issues due to the release of mosquitoes. Using labels from Detinova dissection results in a mathematical model with poor accuracy. Alternatively, a model trained on spectra from laboratory-reared mosquitoes where age in days is known can be applied to estimate the age of wild mosquitoes, but this would be appropriate only if spectra collected from laboratory-reared and wild mosquitoes are similar.

METHODS AND FINDINGS

We performed k-means (k = 2) cluster analysis on a mixture of spectra collected from lab-reared and wild Anopheles arabiensis to determine if there is any significant difference between these two groups. While controlling the numbers of mosquitoes included in the model at each age, we found two clusters with no significant difference in distribution of spectra collected from lab-reared and wild mosquitoes (p = 0.25). We repeated the analysis using hierarchical clustering, and similarly, no significant difference was observed (p = 0.13).

CONCLUSION

We find no difference between spectra collected from laboratory-reared and wild mosquitoes of the same age and species. The results strengthen and support the on-going practice of applying the model trained on spectra collected from laboratory-reared mosquitoes, especially first-generation laboratory-reared mosquitoes.

Quantitative reproducibility analysis for identifying reproducible targets from high-throughput experiments

BACKGROUND

High-throughput assays are widely used in biological research to select potential targets. One single high-throughput experiment can efficiently study a large number of candidates simultaneously, but is subject to substantial variability. Therefore it is scientifically important to performance quantitative reproducibility analysis to identify reproducible targets with consistent and significant signals across replicate experiments. A few methods exist, but all have limitations.

METHODS

In this paper, we propose a new method for identifying reproducible targets. Considering a Bayesian hierarchical model, we show that the test statistics from replicate experiments follow a mixture of multivariate Gaussian distributions, with the one component with zero-mean representing the irreproducible targets.

RESULTS

A target is thus classified as reproducible or irreproducible based on its posterior probability belonging to the reproducible components. We study the performance of our proposed method using simulations and a real data example.

CONCLUSION

The proposed method is shown to have favorable performance in identifying reproducible targets compared to other methods.

Confident difference criterion: a new Bayesian differentially expressed gene selection algorithm with applications

Background

Recently, the Bayesian method becomes more popular for analyzing high dimensional gene expression data as it allows us to borrow information across different genes and provides powerful estimators for evaluating gene expression levels. It is crucial to develop a simple but efficient gene selection algorithm for detecting differentially expressed (DE) genes based on the Bayesian estimators.

Results

In this paper, by extending the two-criterion idea of Chen et al. (Chen M-H, Ibrahim JG, Chi Y-Y. A new class of mixture models for differential gene expression in DNA microarray data. J Stat Plan Inference. 2008;138:387–404), we propose two new gene selection algorithms for general Bayesian models and name these new methods as the confident difference criterion methods. One is based on the standardized differences between two mean expression values among genes; the other adds the differences between two variances to it. The proposed confident difference criterion methods first evaluate the posterior probability of a gene having different gene expressions between competitive samples and then declare a gene to be DE if the posterior probability is large. The theoretical connection between the proposed first method based on the means and the Bayes factor approach proposed by Yu et al. (Yu F, Chen M-H, Kuo L. Detecting differentially expressed genes using alibrated Bayes factors. Statistica Sinica. 2008;18:783–802) is established under the normal-normal-model with equal variances between two samples. The empirical performance of the proposed methods is examined and compared to those of several existing methods via several simulations. The results from these simulation studies show that the proposed confident difference criterion methods outperform the existing methods when comparing gene expressions across different conditions for both microarray studies and sequence-based high-throughput studies. A real dataset is used to further demonstrate the proposed methodology. In the real data application, the confident difference criterion methods successfully identified more clinically important DE genes than the other methods.

Conclusion

The confident difference criterion method proposed in this paper provides a new efficient approach for both microarray studies and sequence-based high-throughput studies to identify differentially expressed genes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0664-3) contains supplementary material, which is available to authorized users.