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Background: Competitive gene set analysis is a standard exploratory tool for gene expression data. Permutation-based competitive gene set analysis methods are preferable to parametric ones because the latter make strong statistical assumptions which are not always met. For permutation-based methods, we permute samples, as opposed to genes, as doing so preserves the inter-gene correlation structure. Unfortunately, up until now, sample permutation-based methods have required a minimum of six replicates per sample group., Results: We propose a new permutation-based competitive gene set analysis method for multi-group gene expression data with as few as three replicates per group. The method is based on advanced sample permutation technique that utilizes all groups within a data set for pairwise comparisons. We present a comprehensive evaluation of different permutation techniques, using multiple data sets and contrast the performance of our method, mGSZm, with other state of the art methods. We show that mGSZm is robust, and that, despite only using less than six replicates, we are able to consistently identify a high proportion of the top ranked gene sets from the analysis of a substantially larger data set. Further, we highlight other methods where performance is highly variable and appears dependent on the underlying data set being analyzed., Conclusions: Our results demonstrate that robust gene set analysis of multi-group gene expression data is permissible with as few as three replicates. In doing so, we have extended the applicability of such approaches to resource constrained experiments where additional data generation is prohibitively difficult or expensive. An R package implementing the proposed method and supplementary materials are available from the website http://ekhidna.biocenter.helsinki.fi/downloads/pashupati/mGSZm.html .

Motivation: Gene set analysis is the analysis of a set of genes that collectively contribute to a biological process. Most popular gene set analysis methods are based on empirical P-value that requires large number of permutations. Despite numerous gene set analysis methods developed in the past decade, the most popular methods still suffer from serious limitations., Results: We present a gene set analysis method (mGSZ) based on Gene Set Z-scoring function (GSZ) and asymptotic P-values. Asymptotic P-value calculation requires fewer permutations, and thus speeds up the gene set analysis process. We compare the GSZ-scoring function with seven popular gene set scoring functions and show that GSZ stands out as the best scoring function. In addition, we show improved performance of the GSA method when the max-mean statistics is replaced by the GSZ scoring function. We demonstrate the importance of both gene and sample permutations by showing the consequences in the absence of one or the other. A comparison of asymptotic and empirical methods of P-value estimation demonstrates a clear advantage of asymptotic P-value over empirical P-value. We show that mGSZ outperforms the state-of-the-art methods based on two different evaluations. We compared mGSZ results with permutation and rotation tests and show that rotation does not improve our asymptotic P-values. We also propose well-known asymptotic distribution models for three of the compared methods., Availability and Implementation: mGSZ is available as R package from cran.r-project.org., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Background: A central task in contemporary biosciences is the identification of biological processes showing response in genome-wide differential gene expression experiments. Two types of analysis are common. Either, one generates an ordered list based on the differential expression values of the probed genes and examines the tail areas of the list for over-representation of various functional classes. Alternatively, one monitors the average differential expression level of genes belonging to a given functional class. So far these two types of method have not been combined., Results: We introduce a scoring function, Gene Set Z-score (GSZ), for the analysis of functional class over-representation that combines two previous analysis methods. GSZ encompasses popular functions such as correlation, hypergeometric test, Max-Mean and Random Sets as limiting cases. GSZ is stable against changes in class size as well as across different positions of the analysed gene list in tests with randomized data. GSZ shows the best overall performance in a detailed comparison to popular functions using artificial data. Likewise, GSZ stands out in a cross-validation of methods using split real data. A comparison of empirical p-values further shows a strong difference in favour of GSZ, which clearly reports better p-values for top classes than the other methods. Furthermore, GSZ detects relevant biological themes that are missed by the other methods. These observations also hold when comparing GSZ with popular program packages., Conclusion: GSZ and improved versions of earlier methods are a useful contribution to the analysis of differential gene expression. The methods and supplementary material are available from the website http://ekhidna.biocenter.helsinki.fi/users/petri/public/GSZ/GSZscore.html.

Over half of the DNA of mammalian genomes is transcribed, and one of the emerging enigmas in the field of RNA research is intergenic splicing or transcription induced chimerism. We argue that fused low-copy-number transcripts constitute neglected pathological mechanism akin to copy number variation, due to loss of stoichiometric subunit ratios in protein complexes. An obstacle for transcriptomics meta-analysis of published microarrays is the traditional nomenclature of merged transcript neighbors under same accession codes. Tandem transcripts cover 4-20% of genomes but are only loosely overlapping in population. They were most enriched in systems medicine annotations concerning neurology, thalassemia and genital disorders in the GeneGo Inc. MetaCore-MetaDrug(TM) knowledgebase, evaluated with external randomizations here. Clinical transcriptomics is good news since new disease etiologies offer new remedies. We identified homeotic HOX-transfactors centered around BMI-1, the Grb2 adaptor network, the kallikrein system, and thalassemia RNA surveillance as vulnerable hotspot chimeras. As a cure, RNA interference would require verification of chimerism from symptomatic tissue contra healthy control tissue from the same patient.

We present MultiGO, a web-enabled tool for the identification of biologically relevant gene sets from hierarchically clustered gene expression trees (http://ekhidna.biocenter.helsinki.fi/poxo/multigo). High-throughput gene expression measuring techniques, such as microarrays, are nowadays often used to monitor the expression of thousands of genes. Since these experiments can produce overwhelming amounts of data, computational methods that assist the data analysis and interpretation are essential. MultiGO is a tool that automatically extracts the biological information for multiple clusters and determines their biological relevance, and hence facilitates the interpretation of the data. Since the entire expression tree is analysed, MultiGO is guaranteed to report all clusters that share a common enriched biological function, as defined by Gene Ontology annotations. The tool also identifies a plausible cluster set, which represents the key biological functions affected by the experiment. The performance is demonstrated by analysing drought-, cold- and abscisic acid-related expression data sets from Arabidopsis thaliana. The analysis not only identified known biological functions, but also brought into focus the less established connections to defense-related gene clusters. Thus, in comparison to analyses of manually selected gene lists, the systematic analysis of every cluster can reveal unexpected biological phenomena and produce much more comprehensive biological insights to the experiment of interest.

Background: High throughput methods of the genome era produce vast amounts of data in the form of gene lists. These lists are large and difficult to interpret without advanced computational or bioinformatic tools. Most existing methods analyse a gene list as a single entity although it is comprised of multiple gene groups associated with separate biological functions. Therefore it is imperative to define and visualize gene groups with unique functionality within gene lists., Results: In order to analyse the functional heterogeneity within a gene list, we have developed a method that clusters genes to groups with homogenous functionalities. The method uses Non-negative Matrix Factorization (NMF) to create several clustering results with varying numbers of clusters. The obtained clustering results are combined into a simple graphical presentation showing the functional groups over-represented in the analyzed gene list. We demonstrate its performance on two data sets and show results that improve upon existing methods. The comparison also shows that our method creates a more simplified view that aids in discovery of biological themes within the list and discards less informative classes from the results., Conclusion: The presented method and associated software are useful for the identification and interpretation of biological functions associated with gene lists and are especially useful for the analysis of large lists.

Background: Conventionally, the first step in analyzing the large and high-dimensional data sets measured by microarrays is visual exploration. Dendrograms of hierarchical clustering, self-organizing maps (SOMs), and multidimensional scaling have been used to visualize similarity relationships of data samples. We address two central properties of the methods: (i) Are the visualizations trustworthy, i.e., if two samples are visualized to be similar, are they really similar? (ii) The metric. The measure of similarity determines the result; we propose using a new learning metrics principle to derive a metric from interrelationships among data sets., Results: The trustworthiness of hierarchical clustering, multidimensional scaling, and the self-organizing map were compared in visualizing similarity relationships among gene expression profiles. The self-organizing map was the best except that hierarchical clustering was the most trustworthy for the most similar profiles. Trustworthiness can be further increased by treating separately those genes for which the visualization is least trustworthy. We then proceed to improve the metric. The distance measure between the expression profiles is adjusted to measure differences relevant to functional classes of the genes. The genes for which the new metric is the most different from the usual correlation metric are listed and visualized with one of the visualization methods, the self-organizing map, computed in the new metric., Conclusions: The conjecture from the methodological results is that the self-organizing map can be recommended to complement the usual hierarchical clustering for visualizing and exploring gene expression data. Discarding the least trustworthy samples and improving the metric still improves it.

Agents acting as noncompetitive N-methyl-D-aspartate (NMDA)/glutamate receptor antagonists induce the expression of several genes in limbic cortical regions, such as the cingulate, retrosplenial, and entorhinal cortices. These include important regulatory genes such as the neurotrophin brain-derived neurotrophic factor (BDNF), its receptor trkB, and c-fos. We applied expression profiling methods to find genes coregulated with BDNF following treatment with the prototypical NMDA/glutamate receptor antagonist MK-801. Expression profiling provides a useful technique for describing the molecular and transcriptional level events that follow various processes. We illustrate the utility of microarrays to find novel ESTs regulated by MK-801. We also used expression profiling with microarrays to characterize the levels of transcription factor cAMP response element modulator (CREM) and inducible cAMP early repressor (ICER) isoforms that are induced by MK-801. These factors may act as the eventual repressors for BDNF expression via competition and heterodimerization with phosphorylated CREB, a transcription factor important for BDNF expression. Finally, we find and confirm the regulation of Erp29, RTNI, and an ABC transporter by antagonism of NMDA/glutamate receptors as potential stress related molecules in brain. The emerging picture generated by using these expression profiling approaches, identifies several of what likely will be many molecules that take part in the complex events that occur during BDNF signaling mediated by blockade of NMDA/ glutamate receptors.

Cluster structure of gene expression data obtained from DNA microarrays is analyzed and visualized with the Self-Organizing Map (SOM) algorithm. The SOM forms a non-linear mapping of the data to a two-dimensional map grid that can be used as an exploratory data analysis tool for generating hypotheses on the relationships, and ultimately of the function of the genes. Similarity relationships within the data and cluster structures can be visualized and interpreted. The methods are demonstrated by computing a SOM of yeast genes. The relationships of known functional classes of genes are investigated by analyzing their distribution on the SOM, the cluster structure is visualized by the U-matrix method, and the clusters are characterized in terms of the properties of the expression profiles of the genes. Finally, it is shown that the SOM visualizes the similarity of genes in a more trustworthy way than two alternative methods, multidimensional scaling and hierarchical clustering.

Colorectal cancer (CRC) genome is unstable and different types of instabilities, such as chromosomal instability (CIN) and microsatellite instability (MSI) are thought to reflect distinct cancer initiating mechanisms. Although 85% of sporadic CRC reveal CIN, 15% reveal mismatch repair (MMR) malfunction and MSI, the hallmarks of Lynch syndrome with inherited heterozygous germline mutations in MMR genes. Our study was designed to comprehensively follow genome-wide expression changes and their implications during colon tumorigenesis. We conducted a long-term feeding experiment in the mouse to address expression changes arising in histologically normal colonic mucosa as putative cancer preceding events, and the effect of inherited predisposition (Mlh1+/-) and Western-style diet (WD) on those. During the 21-month experiment, carcinomas developed mainly in WD-fed mice and were evenly distributed between genotypes. Unexpectedly, the heterozygote (B6.129-Mlh1tm1Rak) mice did not show MSI in their CRCs. Instead, both wildtype and heterozygote CRC mice showed a distinct mRNA expression profile and shortage of several chromosomal segregation gene-specific transcripts (Mlh1, Bub1, Mis18a, Tpx2, Rad9a, Pms2, Cenpe, Ncapd3, Odf2 and Dclre1b) in their colon mucosa, as well as an increased mitotic activity and abundant numbers of unbalanced/atypical mitoses in tumours. Our genome-wide expression profiling experiment demonstrates that cancer preceding changes are already seen in histologically normal colon mucosa and that decreased expressions of Mlh1 and other chromosomal segregation genes may form a field-defect in mucosa, which trigger MMR-proficient, chromosomally unstable CRC. [ABSTRACT FROM AUTHOR]

A better understanding of atherogenesis at the level of gene expression could lead to the identification of new therapeutic strategies for vascular diseases. With DNA array technology, it is possible to identify multiple, simultaneous changes in gene expression in small tissue samples from atherosclerotic arteries. We analyzed gene expression in normal arteries and in immunohistologically characterized human advanced atherosclerotic lesions using an array of 18 376 cDNA fragments. The array method was first validated by detecting a group of genes (n=17) that were already known to be connected to atherogenesis. These genes included e.g. Apolipoprotein E, CD68, TIMP and phospholipase D. Next we detected 75 differentially expressed genes that were previously not connected to atherogenesis. A subgroup of genes involved in cell signaling and proliferation was selected for further analyzes with in situ hybridization and RT-PCR which confirmed array results by showing induction in advanced lesions of Janus kinase 1 (JAK-1) which is an important signaling molecule in activated macrophages; VEGF receptor-2 which mediates angiogenic and vasculoprotective effects of VEGF; and an unknown gene, which mapped on chromosome 19. It is concluded that DNA array technology enables fast screening of gene expression in small samples of atherosclerotic lesions. The technique will be useful for the identification of new factors, such as JAK-1 and VEGF receptor-2, which may play an important role in atherogenesis. [Copyright &y& Elsevier]