Wavelet thresholding with bayesian false discovery rate control

The false discovery rate (FDR) procedure has become a popular method for handling multiplicity in high-dimensional data. The definition of FDR has a natural Bayesian interpretation; it is the expected proportion of null hypotheses mistakenly rejected given a measure of evidence for their truth. In this article, we propose controlling the positive FDR using a Bayesian approach where the rejection rule is based on the posterior probabilities of the null hypotheses. Correspondence between Bayesian and frequentist measures of evidence in hypothesis testing has been studied in several contexts. Here we extend the comparison to multiple testing with control of the FDR and illustrate the procedure with an application to wavelet thresholding. The problem consists of recovering signal from noisy measurements. This involves extracting wavelet coefficients that result from true signal and can be formulated as a multiple hypotheses-testing problem. We use simulated examples to compare the performance of our approach to the Benjamini and Hochberg (1995, Journal of the Royal Statistical Society, Series B57, 289-300) procedure. We also illustrate the method with nuclear magnetic resonance spectral data from human brain.     

tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalities

Background

To determine which changes in the host cell genome are crucial for cervical carcinogenesis, a longitudinal in vitro model system of HPV-transformed keratinocytes was profiled in a genome-wide manner. Four cell lines affected with either HPV16 or HPV18 were assayed at 8 sequential time points for gene expression (mRNA) and gene copy number (DNA) using high-resolution microarrays. Available methods for temporal differential expression analysis are not designed for integrative genomic studies.

Results

Here, we present a method that allows for the identification of differential gene expression associated with DNA copy number changes over time. The temporal variation in gene expression is described by a generalized linear mixed model employing low-rank thin-plate splines. Model parameters are estimated with an empirical Bayes procedure, which exploits integrated nested Laplace approximation for fast computation. Iteratively, posteriors of hyperparameters and model parameters are estimated. The empirical Bayes procedure shrinks multiple dispersion-related parameters. Shrinkage leads to more stable estimates of the model parameters, better control of false positives and improvement of reproducibility. In addition, to make estimates of the DNA copy number more stable, model parameters are also estimated in a multivariate way using triplets of features, imposing a spatial prior for the copy number effect.

Conclusion

With the proposed method for analysis of time-course multilevel molecular data, more profound insight may be gained through the identification of temporal differential expression induced by DNA copy number abnormalities. In particular, in the analysis of an integrative oncogenomics study with a time-course set-up our method finds genes previously reported to be involved in cervical carcinogenesis. Furthermore, the proposed method yields improvements in sensitivity, specificity and reproducibility compared to existing methods. Finally, the proposed method is able to handle count (RNAseq) data from time course experiments as is shown on a real data set.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-327) contains supplementary material, which is available to authorized users.     

Work efficiency: a new criterion for comprehensive comparison and evaluation of statistical methods in large-scale identification of differentially expressed genes

Receiver operating characteristic (ROC) has been widely used to evaluate statistical methods, but a fatal problem is that ROC cannot evaluate estimation of the false discovery rate (FDR) of a statistical method and hence the area under of curve as a criterion cannot tell us if a statistical method is conservative. To address this issue, we propose an alternative criterion, work efficiency. Work efficiency is defined as the product of the power and degree of conservativeness of a statistical method. We conducted large-scale simulation comparisons among the optimizing discovery procedure (ODP), the Bonferroni (B-) procedure, Local FDR (Localfdr), ranking analysis of the F-statistics (RAF), the Benjamini-Hochberg (BH-) procedure, and significance analysis of microarray data (SAM). The results show that ODP, SAM, and the B-procedure perform with low efficiencies while the BH-procedure, RAF, and Localfdr work with higher efficiency. ODP and SAM have the same ROC curves but their efficiencies are significantly different.     

The Benjamini-Hochberg method with infinitely many contrasts in linear models

Benjamini and Hochberg's method for controlling the false discoveryrate is applied to the problem of testing infinitely many contrastsin linear models. Exact, easily calculated critical values arederived, defining a new multiple comparisons method for testingcontrasts in linear models. The method is adaptive, dependingon the data through the F-statistic, like the Waller–DuncanBayesian multiple comparisons method. Comparisons with Scheffé'smethod are given, and the method is extended to the simultaneousconfidence intervals of Benjamini and Yekutieli.     

Ranking analysis of microarray data: a powerful method for identifying differentially expressed genes

Microarray technology provides a powerful tool for the expression profile of thousands of genes simultaneously, which makes it possible to explore the molecular and metabolic etiology of the development of a complex disease under study. However, classical statistical methods and technologies fail to be applicable to microarray data. Therefore, it is necessary and motivating to develop powerful methods for large-scale statistical analyses. In this paper, we described a novel method, called Ranking Analysis of Microarray Data (RAM). RAM, which is a large-scale two-sample t-test method, is based on comparisons between a set of ranked T statistics and a set of ranked Z values (a set of ranked estimated null scores) yielded by a "randomly splitting" approach instead of a "permutation" approach and a two-simulation strategy for estimating the proportion of genes identified by chance, i.e., the false discovery rate (FDR). The results obtained from the simulated and observed microarray data show that RAM is more efficient in identification of genes differentially expressed and estimation of FDR under undesirable conditions such as a large fudge factor, small sample size, or mixture distribution of noises than Significance Analysis of Microarrays.     

A quality-controlled microarray method for gene expression profiling

Gene expression profiling on microarrays is widely used to measure the expression of large numbers of genes in a single experiment. Because of the high cost of this method, feasible numbers of replicates are limited, thus impairing the power of statistical analysis. As a step toward reducing technically induced variation, we developed a procedure of sample preparation and analysis that minimizes the number of sample manipulation steps, introduces quality control before array hybridization, and allows recovery of the prepared mRNA for independent validation of results. Sample preparation is based on mRNA separation using oligo(dT) magnetic beads, which are subsequently used for first-strand cDNA synthesis on the beads. cDNA covalently bound to the magnetic beads is used as template for second-strand cDNA synthesis, leaving the intact mRNA in solution for further analysis. The quality of the synthesized cDNA can be assessed by quantitative polymerase chain reaction using 3'- and 5'-specific primer pairs for housekeeping genes such as glyceraldehyde-3-phosphate dehydrogenase. Second-strand cDNA is chemically labeled with fluorescent dyes to avoid dye bias in enzymatic labeling reactions. After hybridization of two differently labeled samples to microarray slides, arrays are scanned and images analyzed automatically with high reproducibility. Quantile-normalized data from five biological replica display a coefficient of variation 45% for 90% of profiled genes, allowing detection of twofold changes with false positive and false negative rates of 10% each. We demonstrate successful application of the procedure for expression profiling in plant leaf tissue. However, the method could be easily adapted for samples from animal including human or from microbial origin.     

Random forests-based differential analysis of gene sets for gene expression data

In DNA microarray studies, gene-set analysis (GSA) has become the focus of gene expression data analysis. GSA utilizes the gene expression profiles of functionally related gene sets in Gene Ontology (GO) categories or priori-defined biological classes to assess the significance of gene sets associated with clinical outcomes or phenotypes. Many statistical approaches have been proposed to determine whether such functionally related gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to the discriminatory power of gene sets and classification of patients.     

Identification of a differential expression signature associated with tumorigenesis and metastasis of laryngeal carcinoma

Objectives

Metastasis is the most significant prognostic factor for laryngeal carcinoma which necessitates the identification of molecular alterations associated with metastasis. The identification of such molecular alterations will not only prove useful in treatment but also provide insight into mechanisms of cancer metastasis. The studies conducted so far have not specifically focused on metastasis or invasion pathways. Therefore we investigated the expression profiles with a pathway focused approach.

Materials and methods

Total RNA was extracted from 36 laryngeal tumors and paired cancer free tissue. Expression levels of 88 genes were determined using a PCR array system following cDNA synthesis. Obtained data was used for the calculation of altered expression levels, facilitating relevant algorithms. Significant alterations were determined according to their p-value obtained by Student's t-test.

Results

Sixteen genes have shown altered expression when compared with adjacent cancer-free tissue. 2 of these 16 genes have shown differential expression in tumors with neck metastasis in respect to non-metastatic tumors.

Conclusion

We found that TGFB1, TIMP1, c-Myc, SPARC, COL4A2 and SOX4 show altered expression in laryngeal tumors. c-Myc and SOX4 expression is decreased as laryngeal tumors switch to metastatic phenotype.     

Identification of differentially expressed genes using multi-resolution wavelet transformation analysis combined with SAM

Although many statistical methods have been proposed for identifying differentially expressed genes, the optimal approach has still not been resolved. Therefore, it is necessary to develop more efficient methods of finding differentially expressed genes while accounting for noise and false discovery rate (FDR). We propose a method based on multi-resolution wavelet transformation analysis combined with SAM for identifying differentially expressed genes by adjusting the Δ and computing the FDR. This method was applied to a microarray expression dataset from adenoma patients and normal subjects. The number of differentially expressed genes gradually reduced with an increasing Δ value, and the FDR was reduced after wavelet transformation. At a given Δ value, the FDR was also reduced before and after wavelet transformation. In conclusion, a greater number and quality of differentially expressed genes were detected using the method when compared to non-transformed data, and the FDRs were notably more controlled and reduced.     

Cytoplasmic polyhedrosis virus-induced differential gene expression in two silkworm strains of different susceptibility

Digital gene expression (DGE) was performed to investigate the gene expression profiles of 4008 and p50 silkworm strains at 48 h after oral infection with BmCPV. 3,668,437 clean tags were identified in the BmCPV-infected p50 silkworms and 3,540,790 clean tags in the control p50. By contrast, 4,498,263 clean tags were identified in the BmCPV-infected 4008 silkworms and 4,164,250 clean tags in the control 4008. A total of 691 differentially expressed genes were detected in the infected 4008 DGE library and 185 were detected in the infected p50 DGE library, respectively. The expression profiles identified some important differentially expressed genes involved in signal transduction, enzyme activity and apoptotic changes, some of which were verified using quantitative real-time PCR (qRT-PCR). These results provide important clues on the molecular mechanism of BmCPV invasion and resistance mechanism of silkworms against BmCPV infection.     

Relationships of differential gene expression in leaves with heterosis and heterozygosity in a rice diallel cross

Using differential display analysis, we assessed the patterns of differential gene expression in hybrids relative to their parents in a diallel cross involving 8 elite rice lines. The analysis revealed several patterns of differential expression including: (1) bands present in one parent and F₁ but absent in the other parent, (2) bands observed in both parents but not in the F₁, (3) bands occurring in only one parent but not in the F₁ or the other parent, and, (4) bands detected only in the F₁ but in neither of the parents. Relationships between differential gene expression and heterosis and marker heterozygosity were evaluated using data for RFLPs, SSRs and a number of agronomic characters. The analysis showed that there was very little correlation between patterns of differential expression and the F₁ means for all six agronomic traits. Differentially expressed fragments that occurred only in one parent but not in the other parent or in F₁ in each of the respective crosses were positively correlated with heterosis and heterozygosity. And conversely, fragments that were detected in F₁s but in neither of the respective parents were negatively correlated with heterosis and heterozygosity. The remaining patterns of differential expression were not correlated with heterosis or heterozygosity. The relationships between the patterns of differential expression and heterosis observed in this study were not consistent with expectations based on dominance or overdominance hypotheses.     

Use of a simple semiquantitative method for appraisal of green fluorescent protein gene expression in transgenic tobacco plants

We have applied a simple method for evaluation of gfp gene expression in plants using a CCD camera and computerized processing of images. Transgenic tobacco plants were obtained by Agrobacterium tumefaciens-mediated transfer of plasmid T-DNA bearing a m-gfp5-ER sequence governed by the 35S promoter together with the nptII selectable marker gene. Presence of the gfp gene in plants was confirmed by a polymerase chain reaction method. Mean brightness values measured using image analysis software showed differences between transgenic and control plants and suggest the possibility of rapid selection of transgenic individuals among regenerants and their progenies.     

Myocardial gene expression profiling of rewarming shock in a rodent model of accidental hypothermia

Background

Severe accidental hypothermia represents a cardiovascular emergency associated with high mortality and poor recovery of cardiac function. The biochemical changes occurring within the heart during the development of hypothermia and subsequent resuscitation are not known.

Methods

By mRNA expression profiling, we have characterized gene expression changes occurring within the myocardium in an intact rat model of accidental hypothermia during cooling to a core temperature of 15 °C and subsequent rewarming to 37 °C. During the rewarming phase, these animals develop a profound low-output cardiac failure.

Results

Hypothermia induces expression of known mediators of thermotolerance, including heat-shock protein 70 and several factors involved in protection against apoptotic cell death. Upregulation of genes involved in autophagy and increased abundance of autophagosomal vesicles suggest involvement of autophagic degeneration in the development of myocardial dysfunction occurring during rewarming from hypothermia. Rewarming from hypothermia also induces expression of several pro-inflammatory genes involved in the nuclear factor kappa B (NFκB) signaling cascade.

Conclusions

Our data demonstrate that rewarming from hypothermia is associated with the induction of a cellular stress–response, including upregulation of autophagy and activation of pro-inflammatory signaling cascades. These data provide a framework for understanding the molecular changes that occur during induction of and rewarming from severe hypothermia, and identifies potential targets for cardioprotective interventions in resuscitation of victims of hypothermia.