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A challenging task in time-course microarray data analysis is to cluster genes meaningfully combining the information provided by multiple replicates covering the same key time points. This paper proposes a novel cluster merging method to accomplish this goal obtaining groups with highly correlated genes. The main idea behind the proposed method is to generate a clustering starting from groups created based on individual temporal series (representing different biological replicates measured in the same time points) and merging them by taking into account the frequency by which two genes are assembled together in each clustering. The gene groups at the level of individual time series are generated using several shape-based clustering methods. This study is focused on a real-world time series microarray task with the aim to find co-expressed genes related to the production and growth of a certain bacteria. The shape-based clustering methods used at the level of individual time series rely on identifying similar gene expression patterns over time which, in some models, are further matched to the pattern of production/growth. The proposed cluster merging method is able to produce meaningful gene groups which can be naturally ranked by the level of agreement on the clustering among individual time series. The list of clusters and genes is further sorted based on the information correlation coefficient and new problem-specific relevant measures. Computational experiments and results of the cluster merging method are analyzed from a biological perspective and further compared with the clustering generated based on the mean value of time series and the same shape-based algorithm.

Although garlic induces apoptosis in cancer cells, it is unclear whether the effects are similar to those of cisplatin against bladder cancer (BC). Therefore, this study investigated whether garlic extracts and cisplatin show similar activity when used to treat BC. The effect of garlic on T24 BC cell line was examined in a BALB/C-nude mouse xenograft model and compared with that of cisplatin. Tissue microarray analysis and gene network analysis were performed to identify differences in gene expression by control tumors and tumors exposed to garlic extract or cisplatin. Investigation of gene expression based on tissues from 165 BC patients and normal controls was then performed to identify common targets of garlic and cisplatin. Tumor volume and tumor weight in cisplatin (0.05mg/kg)- and garlic-treated mice were significantly smaller than those in negative control mice. However, cisplatin-treated mice also showed a significant reduction in body weight. Microarray analysis of tumor tissue identified 515 common anticancer genes in the garlic and cisplatin groups ($p<0.01$). Gene network analysis of 252 of these genes using the Cytoscape and ClueGo software packages mapped 17 genes and 9 gene ontologies to gene networks. BC (NMIBC and MIBC) patients with low expression of centromere protein M (CENPM) showed significantly better progression-free survival than those with high expression. Garlic extract shows anticancer activity in vivo similar to that of cisplatin, with no evident of side effects. Both appear to act by targeting protein-DNA complex assembly; in particular, expression of CENPM.

Gene-expression microarray datasets often consist of a limited number of samples with a large number of gene-expression measurements, usually on the order of thousands. Therefore, dimensionality reduction is critical prior to any classification task. In this work, the iterative feature perturbation method (IFP), an embedded gene selector, is introduced and applied to four microarray cancer datasets: colon cancer, leukemia, Moffitt colon cancer, and lung cancer. We compare results obtained by IFP to those of support vector machine-recursive feature elimination (SVM-RFE) and the t-test as a feature filter using a linear support vector machine as the base classifier. Analysis of the intersection of gene sets selected by the three methods across the four datasets was done. Additional experiments included an initial pre-selection of the top 200 genes based on their p values. IFP and SVM-RFE were then applied on the reduced feature sets. These results showed up to 3.32% average performance improvement for IFP across the four datasets. A statistical analysis (using the Friedman/Holm test) for both scenarios showed the highest accuracies came from the t-test as a filter on experiments without gene pre-selection. IFP and SVM-RFE had greater classification accuracy after gene pre-selection. Analysis showed the t-test is a good gene selector for microarray data. IFP and SVM-RFE showed performance improvement on a reduced by t-test dataset. The IFP approach resulted in comparable or superior average class accuracy when compared to SVM-RFE on three of the four datasets. The same or similar accuracies can be obtained with different sets of genes.

This review compares different methods to identify differentially expressed genes in two-sample cDNA arrays. A two-sample experiment is a commonly used design to compare relative mRNA abundance between two different samples. This simple design is customarily used by biologists as a first screening before relying on more complex designs. Statistical techniques are quite well developed for such simple designs. For the identification of differentially expressed genes, four methods were described and compared: a fold test, a t-test (Long et al., 2001), SAM (Tusher et al., 2001) and an ANOVA-based bootstrap method (Kerr and Churchill, 2001). Mutual comparison of these methods clearly illustrates each method's advantages and pitfalls. Our analyses showed that the most reliable predictions are made by the combined use of different methods, each of which is based on a different statistic. The ANOVA-based bootstap method used in this study performed rather poorly in identifying differentially expressed genes.

We have recently introduced a rank-based test statistic, RankProducts (RP), as a new non-parametric method for detecting differentially expressed genes in microarray experiments. It has been shown to generate surprisingly good results with biological datasets. The basis for this performance and the limits of the method are, however, little understood. Here we explore the performance of such rank-based approaches under a variety of conditions using simulated microarray data, and compare it with classical Wilcoxon rank sums and t-statistics, which form the basis of most alternative differential gene expression detection techniques.

We show that for realistic simulated microarray datasets, RP is more powerful and accurate for sorting genes by differential expression than t-statistics or Wilcoxon rank sums — in particular for replicate numbers below 10, which are most commonly used in biological experiments.

Its relative performance is particularly strong when the data are contaminated by non-normal random noise or when the samples are very inhomogenous, e.g. because they come from different time points or contain a mixture of affected and unaffected cells.

However, RP assumes equal measurement variance for all genes and tends to give overly optimistic p-values when this assumption is violated. It is therefore essential that proper variance stabilizing normalization is performed on the data before calculating the RP values. Where this is impossible, another rank-based variant of RP (average ranks) provides a useful alternative with very similar overall performance.

The Perl scripts implementing the simulation and evaluation are available upon request. Implementations of the RP method are available for download from the authors website ().

This commentary is concerned with a formula for the false discovery rate (FDR) which frequently serves as a basis for its estimation. This formula is valid under some quite special conditions, motivating us to further discuss probabilistic models behind the commonly accepted FDR concept with a special focus on problems arising in microarray data analysis. We also present a simulation study designed to assess the effects of inter-gene correlations on some theoretical results based on such models.

A neuro-fuzzy inference system that recognizes the expression patterns of genes in microarray gene expression (MGE) data, called GeneCFE-ANFIS, is proposed to infer gene interactions. In this study, three primary features are utilized to extract genes' expression patterns and used as inputs to the neuro-fuzzy inference system. The proposed algorithm learns expression patterns from the known genetic interactions, such as the interactions confirmed by qRT-PCR experiments or collected through text-mining technique by surveying previously published literatures, and then predicts other gene interactions according to the learned patterns. The proposed neuro-fuzzy inference system was applied to a public yeast MGE dataset. Two simulations were conducted and checked against 112 pairs of qRT-PCR confirmed gene interactions and 77 TFs (Transcriptional Factors) pairs collected from literature respectively to evaluate the performance of the proposed algorithm.

We develop a general method to identify gene networks from pair-wise correlations between genes in a microarray data set and apply it to a public prostate cancer gene expression data from 69 primary prostate tumors. We define the degree of a node as the number of genes significantly associated with the node and identify hub genes as those with the highest degree. The correlation network was pruned using transcription factor binding information in VisANT (http://visant.bu.edu/) as a biological filter. The reliability of hub genes was determined using a strict permutation test. Separate networks for normal prostate samples, and prostate cancer samples from African Americans (AA) and European Americans (EA) were generated and compared. We found that the same hubs control disease progression in AA and EA networks. Combining AA and EA samples, we generated networks for low (<7) and high (≥7) Gleason grade tumors. A comparison of their major hubs with those of the network for normal samples identified two types of changes associated with disease: (i) Some hub genes increased their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with gain of regulatory control in cancer (e.g. possible turning on of oncogenes). (ii) Some hubs reduced their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with loss of regulatory control in cancer (e.g. possible loss of tumor suppressor genes). A striking result was that for both AA and EA tumor samples, STAT5a, CEBPB and EGR1 are major hubs that gain neighbors compared to the normal prostate network. Conversely, HIF-lα is a major hub that loses connections in the prostate cancer network compared to the normal prostate network. We also find that the degree of these hubs changes progressively from normal to low grade to high grade disease, suggesting that these hubs are master regulators of prostate cancer and marks disease progression. STAT5a was identified as a central hub, with ~120 neighbors in the prostate cancer network and only 81 neighbors in the normal prostate network. Of the 120 neighbors of STAT5a, 57 are known cancer related genes, known to be involved in functional pathways associated with tumorigenesis. Our method is general and can easily be extended to identify and study networks associated with any two phenotypes.

We describe a new method based on principal component analysis and robust consensus ensemble clustering to identify and elucidate the subtypes of breast cancer disease. The method was applied to microarray gene expression data using micro-dissection of samples from 36 breast cancer patients with at least two of three pathological stages of disease. Controls were normal breast epithelial cells from 3 disease free patients. Our method identified an optimum set of genes and strong, stable clusters which correlated well with clinical classification into Luminal, Basal and Her2+ subtypes based on ER, PR and Her2 status. It also revealed a hierarchical portrait of disease progression through various grades and stages and identified genes and functional pathways for each stage, grade and disease subtype. We found that gene expression heterogeneity across subtypes is much greater than the heterogeneity of progression from DCIS to IDC within a subtype, suggesting that the disease subtypes are distinct disease processes. The averaging over data perturbations and clustering methods is critical in the robust identification of subtypes and gene markers for grade and progression.

Background. Existing proposed pathogenesis for preeclampsia (PE) was only applied for early onset subtype and did not consider pre-pregnancy and competing risks. We aimed to decipher PE subtypes by identifying related transcriptome that represents endometrial maturation and histologic chorioamnionitis. Methods. We utilized eight arrays of mRNA expression for discovery (n=289), and other eight arrays for validation (n=352). Differentially expressed genes (DEGs) were overlapped between those of: (1) healthy samples from endometrium, decidua, and placenta, and placenta samples under histologic chorioamnionitis; and (2) placenta samples for each of the subtypes. They were all possible combinations based on four axes: (1) pregnancy-induced hypertension; (2) placental dysfunction-related diseases (e.g., fetal growth restriction [FGR]); (3) onset; and (4) severity. Results. The DEGs of endometrium at late-secretory phase, but none of decidua, significantly overlapped with those of any subtypes with: (1) early onset (p-values ≤0.008); (2) severe hypertension and proteinuria (p-values ≤0.042); or (3) chronic hypertension and/or severe PE with FGR (p-values ≤0.042). Although sharing the same subtypes whose DEGs with which significantly overlap, the gene regulation was mostly counter-expressed in placenta under chorioamnionitis (n=13/18, 72.22%; odds ratio [OR] upper bounds ≤0.21) but co-expressed in late-secretory endometrium (n=3/9, 66.67%; OR lower bounds ≥1.17). Neither the placental DEGs at first-nor second-trimester under normotensive pregnancy significantly overlapped with those under late-onset, severe PE without FGR. Conclusions. We identified the transcriptome of endometrial maturation in placental dysfunction that distinguished early- and late-onset PE, and indicated chorioamnionitis as a PE competing risk. This study implied a feasibility to develop and validate the pathogenesis models that include pre-pregnancy and competing risks to decide if it is needed to collect prospective data for PE starting from pre-pregnancy including chorioamnionitis information.

The microarray technology has paved the way to high-dimensional data analysis for molecular classification, the large number of features (genes) making the need for feature selection techniques more crucial than ever. From various ranking-based filter procedures to classifier-based wrapper techniques, many studies have devised their own flavor of feature selection methods. However, in the context of highly multiclass microarray data, only a handful of them have delved into the effect of redundancy in the feature set on classification accuracy. We present a generic, fast feature selection method for datasets with large number of classes. Our feature selection method is capable of achieving low-biased accuracies comparable to those of previous studies while at the same time providing insight into the effect of relationships between genes on the classification accuracies.

Plants respond to abiotic stresses, such as drought, high salinity, and cold, to acquire stress tolerance. Molecular and genomic studies have shown that a number of genes with various functions are induced by abiotic stresses, and that various transcription factors are involved in the regulation of stress-inducible genes in Arabidopsis and rice. These gene products function not only in stress tolerance but also in stress response. In this review, recent progress in the analysis of complex cascades of gene expression in drought and cold stress responses is summarized. Various genes involved in stress tolerance are also discussed for their application to molecular breeding of drought, salinity, and/or cold stress tolerance.