Meta-Analysis Based on Nonconvex Regularization

The widespread applications of high-throughput sequencing technology have produced a large number of publicly available gene expression datasets. However, due to the gene expression datasets have the characteristics of small sample size, high dimensionality and high noise, the application of biostatistics and machine learning methods to analyze gene expression data is a challenging task, such as the low reproducibility of important biomarkers in different studies. Meta-analysis is an effective approach to deal with these problems, but the current methods have some limitations. In this paper, we propose the meta-analysis based on three nonconvex regularization methods, which are L1/2 regularization (meta-Half), Minimax Concave Penalty regularization (meta-MCP) and Smoothly Clipped Absolute Deviation regularization (meta-SCAD). The three nonconvex regularization methods are effective approaches for variable selection developed in recent years. Through the hierarchical decomposition of coefficients, our methods not only maintain the flexibility of variable selection and improve the efficiency of selecting important biomarkers, but also summarize and synthesize scientific evidence from multiple studies to consider the relationship between different datasets. We give the efficient algorithms and the theoretical property for our methods. Furthermore, we apply our methods to the simulation data and three publicly available lung cancer gene expression datasets, and compare the performance with state-of-the-art methods. Our methods have good performance in simulation studies, and the analysis results on the three publicly available lung cancer gene expression datasets are clinically meaningful. Our methods can also be extended to other areas where datasets are heterogeneous.

A conserved cell growth cycle can account for the environmental stress responses of divergent eukaryotes

Transitions between the two phases of the cell growth cycle can account for the environmental stress response, the growth-rate response, and the cross-protection between slow growth and various types of stress factors. It is suggested that this mechanism is conserved across budding and fission yeast and normal human cells.

The respiratory metabolic cycle in budding yeast (Saccharomyces cerevisiae) consists of two phases that are most simply defined phenomenologically: low oxygen consumption (LOC) and high oxygen consumption (HOC). Each phase is associated with the periodic expression of thousands of genes, producing oscillating patterns of gene expression found in synchronized cultures and in single cells of slowly growing unsynchronized cultures. Systematic variation in the durations of the HOC and LOC phases can account quantitatively for well-studied transcriptional responses to growth rate differences. Here we show that a similar mechanism—transitions from the HOC phase to the LOC phase—can account for much of the common environmental stress response (ESR) and for the cross-protection by a preliminary heat stress (or slow growth rate) to subsequent lethal heat stress. Similar to the budding yeast metabolic cycle, we suggest that a metabolic cycle, coupled in a similar way to the ESR, in the distantly related fission yeast, Schizosaccharomyces pombe, and in humans can explain gene expression and respiratory patterns observed in these eukaryotes. Although metabolic cycling is associated with the G0/G1 phase of the cell division cycle of slowly growing budding yeast, transcriptional cycling was detected in the G2 phase of the division cycle in fission yeast, consistent with the idea that respiratory metabolic cycling occurs during the phases of the cell division cycle associated with mass accumulation in these divergent eukaryotes.

Comprehensive literature review and statistical considerations for microarray meta-analysis

With the rapid advances of various high-throughput technologies, generation of ‘-omics’ data is commonplace in almost every biomedical field. Effective data management and analytical approaches are essential to fully decipher the biological knowledge contained in the tremendous amount of experimental data. Meta-analysis, a set of statistical tools for combining multiple studies of a related hypothesis, has become popular in genomic research. Here, we perform a systematic search from PubMed and manual collection to obtain 620 genomic meta-analysis papers, of which 333 microarray meta-analysis papers are summarized as the basis of this paper and the other 249 GWAS meta-analysis papers are discussed in the next companion paper. The review in the present paper focuses on various biological purposes of microarray meta-analysis, databases and software and related statistical procedures. Statistical considerations of such an analysis are further scrutinized and illustrated by a case study. Finally, several open questions are listed and discussed.

Randomization techniques for assessing the significance of gene periodicity results

Background

Modern high-throughput measurement technologies such as DNA microarrays and next generation sequencers produce extensive datasets. With large datasets the emphasis has been moving from traditional statistical tests to new data mining methods that are capable of detecting complex patterns, such as clusters, regulatory networks, or time series periodicity. Study of periodic gene expression is an interesting research question that also is a good example of challenges involved in the analysis of high-throughput data in general. Unlike for classical statistical tests, the distribution of test statistic for data mining methods cannot be derived analytically.

Results

We describe the randomization based approach to significance testing, and show how it can be applied to detect periodically expressed genes. We present four randomization methods, three of which have previously been used for gene cycle data. We propose a new method for testing significance of periodicity in gene expression short time series data, such as from gene cycle and circadian clock studies. We argue that the underlying assumptions behind existing significance testing approaches are problematic and some of them unrealistic. We analyze the theoretical properties of the existing and proposed methods, showing how our method can be robustly used to detect genes with exceptionally high periodicity. We also demonstrate the large differences in the number of significant results depending on the chosen randomization methods and parameters of the testing framework.

By reanalyzing gene cycle data from various sources, we show how previous estimates on the number of gene cycle controlled genes are not supported by the data. Our randomization approach combined with widely adopted Benjamini-Hochberg multiple testing method yields better predictive power and produces more accurate null distributions than previous methods.

Conclusions

Existing methods for testing significance of periodic gene expression patterns are simplistic and optimistic. Our testing framework allows strict levels of statistical significance with more realistic underlying assumptions, without losing predictive power. As DNA microarrays have now become mainstream and new high-throughput methods are rapidly being adopted, we argue that not only there will be need for data mining methods capable of coping with immense datasets, but there will also be need for solid methods for significance testing.