Testing for differentially expressed genes with microarray data

This paper compares the type I error and power of the one- and two-sample t-tests, and the one- and two-sample permutation tests for detecting differences in gene expression between two microarray samples with replicates using Monte Carlo simulations. When data are generated from a normal distribution, type I errors and powers of the one-sample parametric t-test and one-sample permutation test are very close, as are the two-sample t-test and two-sample permutation test, provided that the number of replicates is adequate. When data are generated from a t-distribution, the permutation tests outperform the corresponding parametric tests if the number of replicates is at least five. For data from a two-color dye swap experiment, the one-sample test appears to perform better than the two-sample test since expression measurements for control and treatment samples from the same spot are correlated. For data from independent samples, such as the one-channel array or two-channel array experiment using reference design, the two-sample t-tests appear more powerful than the one-sample t-tests.     

Multivariate approach for selecting sets of differentially expressed genes

An important problem addressed using cDNA microarray data is the detection of genes differentially expressed in two tissues of interest. Currently used approaches ignore the multidimensional structure of the data. However it is well known that correlation among covariates can enhance the ability to detect less pronounced differences. We use the Mahalanobis distance between vectors of gene expressions as a criterion for simultaneously comparing a set of genes and develop an algorithm for maximizing it. To overcome the problem of instability of covariance matrices we propose a new method of combining data from small-scale random search experiments. We show that by utilizing the correlation structure the multivariate method, in addition to the genes found by the one-dimensional criteria, finds genes whose differential expression is not detectable marginally.     

Transcriptome analysis of differentially expressed genes and pathways associated with mitoxantrone treatment prostate cancer

The global physiological function of specifically expressed genes of mitoxantrone (MTX)‐resistant prostate cancer (PCa) is unclear. In this study, gene expression pattern from microarray data was investigated for identifying differentially expressed genes (DEGs) in MTX‐resistant PCa xenografts. Human PCa cell lines DU145 and PC3 were cultured in vitro and xenografted into severe combined immunodeficiency (SCID) mice, treated with MTX intragastrically, three times a week until all mice relapsed. Gene expression profiles of the xenografts from castrated mice were performed with Affymetrix human whole genomic oligonucleotide microarray. The Cytoscape software was used to investigate the relationship between proteins and the signalling transduction network. A total of 355 overlapping genes were differentially expressed in MTX‐resistant DU145R and PC3R xenografts. Of these, 16 genes were selected to be validated by quantitative real‐time PCR (qRT‐PCR) in these xenografts, and further tested in a set of formalin‐fixed, paraffin‐embedded and optimal cutting temperature (OCT) clinical tumour samples. Functional and pathway enrichment analyses revealed that these DEGs were closely related to cellular activity, androgen synthesis, DNA damage and repair, also involved in the ERK/MAPK, PI3K/serine‐threonine protein kinase, also known as protein kinase B, PKB (AKT) and apoptosis signalling pathways. This exploratory analysis provides information about potential candidate genes and may bring new insights into the molecular cascade involvement in MTX‐resistant PCa.     

Nonparametric methods for identifying differentially expressed genes in microarray data

MOTIVATION: Gene expression experiments provide a fast and systematic way to identify disease markers relevant to clinical care. In this study, we address the problem of robust identification of differentially expressed genes from microarray data. Differentially expressed genes, or discriminator genes, are genes with significantly different expression in two user-defined groups of microarray experiments. We compare three model-free approaches: (1). nonparametric t-test, (2). Wilcoxon (or Mann-Whitney) rank sum test, and (3). a heuristic method based on high Pearson correlation to a perfectly differentiating gene ('ideal discriminator method'). We systematically assess the performance of each method based on simulated and biological data under varying noise levels and p-value cutoffs. RESULTS: All methods exhibit very low false positive rates and identify a large fraction of the differentially expressed genes in simulated data sets with noise level similar to that of actual data. Overall, the rank sum test appears most conservative, which may be advantageous when the computationally identified genes need to be tested biologically. However, if a more inclusive list of markers is desired, a higher p-value cutoff or the nonparametric t-test may be appropriate. When applied to data from lung tumor and lymphoma data sets, the methods identify biologically relevant differentially expressed genes that allow clear separation of groups in question. Thus the methods described and evaluated here provide a convenient and robust way to identify differentially expressed genes for further biological and clinical analysis.     

Adaptive thresholds to detect differentially expressed genes in microarray data

To detect changes in gene expression data from microarrays, a fixed threshold for fold difference is used widely. However, it is not always guaranteed that a threshold value which is appropriate for highly expressed genes is suitable for lowly expressed genes. In this study, aiming at detecting truly differentially expressed genes from a wide expression range, we proposed an adaptive threshold method (AT). The adaptive thresholds, which have different values for different expression levels, are calculated based on two measurements under the same condition. The sensitivity, specificity and false discovery rate (FDR) of AT were investigated by simulations. The sensitivity and specificity under various noise conditions were greater than 89.7% and 99.32%, respectively. The FDR was smaller than 0.27. These results demonstrated the reliability of the method.     

Discovering tightly regulated and differentially expressed gene sets in whole genome expression data

MOTIVATION: Recently, a new type of expression data is being collected which aims to measure the effect of genetic variation on gene expression in pathways. In these datasets, expression profiles are constructed for multiple strains of the same model organism under the same condition. The goal of analyses of these data is to find differences in regulatory patterns due to genetic variation between strains, often without a phenotype of interest in mind. We present a new method based on notions of tight regulation and differential expression to look for sets of genes which appear to be significantly affected by genetic variation. RESULTS: When we use categorical phenotype information, as in the Alzheimer's and diabetes datasets, our method finds many of the same gene sets as gene set enrichment analysis. In addition, our notion of correlated gene sets allows us to focus our efforts on biological processes subjected to tight regulation. In murine hematopoietic stem cells, we are able to discover significant gene sets independent of a phenotype of interest. Some of these gene sets are associated with several blood-related phenotypes. AVAILABILITY: The programs are available by request from the authors.     

Prioritization of differentially expressed genes through integrating public expression data

Differentially expressed gene (DEG) analysis is a major approach for interpreting phenotype differences and produces a large number of candidate genes. Given that it is burdensome to validate too many genes through benchwork, an urgent need exists for DEG prioritization. Here, a novel method is proposed for prioritizing bona fide DEGs by constructing the normal range of gene expression through integrating public expression data. Prioritization was performed by ranking the differences in cumulative probability for genes in case and control groups. DEGs from a study on pig muscle tissue were used to evaluate the prioritization accuracy. The results showed that the method reached an area under the receiver operating characteristic curve of 96.42% and can effectively shorten the list of candidate genes from a differential expression experiment to find novel causal genes. Our method can be easily extended to other tissues or species to promote functional research in broad applications.     

Detection of differentially expressed gene sets in a partially paired microarray data set

Partially paired data sets often occur in microarray experiments (Kim et al., 2005; Liu, Liang and Jang, 2006). Discussions of testing with partially paired data are found in the literature (Lin and Stivers 1974; Ekbohm, 1976; Bhoj, 1978). Bhoj (1978) initially proposed a test statistic that uses a convex combination of paired and unpaired t statistics. Kim et al. (2005) later proposed the t3 statistic, which is a linear combination of paired and unpaired t statistics, and then used it to detect differentially expressed (DE) genes in colorectal cancer (CRC) cDNA microarray data. In this paper, we extend Kim et al.'s t3 statistic to the Hotelling's T2 type statistic Tp for detecting DE gene sets of size p. We employ Efron's empirical null principle to incorporate inter-gene correlation in the estimation of the false discovery rate. Then, the proposed Tp statistic is applied to Kim et al's CRC data to detect the DE gene sets of sizes p=2 and p=3. Our results show that for small p, particularly for p=2 and marginally for p=3, the proposed Tp statistic compliments the univariate procedure by detecting additional DE genes that were undetected in the univariate test procedure. We also conduct a simulation study to demonstrate that Efron's empirical null principle is robust to the departure from the normal assumption.     

A mixture model approach to detecting differentially expressed genes with microarray data

An exciting biological advancement over the past few years is the use of microarray technologies to measure simultaneously the expression levels of thousands of genes. The bottleneck now is how to extract useful information from the resulting large amounts of data. An important and common task in analyzing microarray data is to identify genes with altered expression under two experimental conditions. We propose a nonparametric statistical approach, called the mixture model method (MMM), to handle the problem when there are a small number of replicates under each experimental condition. Specifically, we propose estimating the distributions of a t -type test statistic and its null statistic using finite normal mixture models. A comparison of these two distributions by means of a likelihood ratio test, or simply using the tail distribution of the null statistic, can identify genes with significantly changed expression. Several methods are proposed to effectively control the false positives. The methodology is applied to a data set containing expression levels of 1,176 genes of rats with and without pneumococcal middle ear infection.     

Microarray data quality control improves the detection of differentially expressed genes

Microarrays have become a routine tool for biomedical research. Data quality assessment is an essential part of the analysis, but it is still not easy to perform objectively or in an automated manner, and as a result it is often neglected. Here, we compared two strategies of array-level quality control using five publicly available microarray experiments: outlier removal and array weights. We also compared them against no outlier removal and random array removal. We find that removing outlier arrays can improve the signal-to-noise ratio and thus strengthen the power of detecting differentially expressed genes. Using array weights is similarly effective, but its applicability is more limited. The quality metrics presented here are implemented in the Bioconductor package arrayQualityMetrics.     

A tool for comparing different statistical methods on identifying differentially expressed genes

Background  

Many different statistical methods have been developed to deal with two group comparison microarray experiments. Most often, a substantial number of genes may be selected or not, depending on which method was actually used. Practical guidance on the application of these methods is therefore required. We developed a procedure based on bootstrap and a criterion to allow viewing and quantifying differences between method-dependent selections. We applied this procedure on three datasets that cover a range of possible sample sizes to compare three well known methods, namely: t-test, LPE and SAM.     

Analysis of differentially expressed genes in abiotic stress response and their role in signal transduction pathways

The study of abiotic stress response of plants is important because they have to cope with environmental changes to survive. The plant genomes have evolved to meet environmental challenges. Salt, temperature, and drought are the main abiotic stresses. The tolerance and response to stress vary differently in plants. The idea was to analyze the genes showing differential expression under abiotic stresses. There are many pathways connecting the perception of external stimuli to cellular responses. In plants, these pathways play an important role in the transduction of abiotic stresses. In the present study, the gene expression data have been analyzed for their involvement in different steps of signaling pathways. The conserved genes were analyzed for their role in each pathway. The functional annotations of these genes and their response under abiotic stresses in other plant species were also studied. The enzymes of signal pathways, showing similarity with conserved genes, were analyzed for their role in different abiotic stresses. Our findings will help to understand the expression of genes in response to various abiotic stresses. These genes may be used to study the response of different abiotic stresses in other plant species and the molecular basis of stress tolerance.     

CIT: identification of differentially expressed clusters of genes from microarray data

Cluster Identification Tool (CIT) is a microarray analysis program that identifies differentially expressed genes. Following division of experimental samples based on a parameter of interest, CIT uses a statistical discrimination metric and permutation analysis to identify clusters of genes or individual genes that best differentiate between the experimental groups. CIT integrates with the freely available CLUSTER and TREEVIEW programs to form a more complete microarray analysis package.