A theory of optimal differential gene expression

We investigate a model of optimal regulation, intended to describe large-scale differential gene expression. Relations between the optimal expression patterns and the function of genes are deduced from an optimality principle: the regulators have to maximise a fitness function which they influence directly via a cost term, and indirectly via their control on important cell variables, such as metabolic fluxes. According to the model, the optimal linear response to small perturbations reflects the regulators' functions, namely their linear influences on the cell variables. The optimal behaviour can be realised by a linear feedback mechanism. Known or assumed properties of response coefficients lead to predictions about regulation patterns. A symmetry relation predicted for deletion experiments is verified with gene expression data. Where the optimality assumption is valid, our results justify the use of expression data for functional annotation and for pathway reconstruction and suggest the use of linear factor models for the analysis of gene expression data.     

Integrated analysis of gene expression and copy number data on gene shaving using independent component analysis

DNA microarray gene expression and microarray-based comparative genomic hybridization (aCGH) have been widely used for biomedical discovery. Because of the large number of genes and the complex nature of biological networks, various analysis methods have been proposed. One such method is "gene shaving," a procedure which identifies subsets of the genes with coherent expression patterns and large variation across samples. Since combining genomic information from multiple sources can improve classification and prediction of diseases, in this paper we proposed a new method, "ICA gene shaving" (ICA, independent component analysis), for jointly analyzing gene expression and copy number data. First we used ICA to analyze joint measurements, gene expression and copy number, of a biological system and project the data onto statistically independent biological processes. Next, we used these results to identify patterns of variation in the data and then applied an iterative shaving method. We investigated the properties of our proposed method by analyzing both simulated and real data. We demonstrated that the robustness of our method to noise using simulated data. Using breast cancer data, we showed that our method is superior to the Generalized Singular Value Decomposition (GSVD) gene shaving method for identifying genes associated with breast cancer.     

Feature Selection for Gene Expression Using Model-Based Entropy

Gene expression data usually contain a large number of genes but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best discriminate biological samples of different types. Using machine learning techniques, traditional gene selection based on empirical mutual information suffers the data sparseness issue due to the small number of samples. To overcome the sparseness issue, we propose a model-based approach to estimate the entropy of class variables on the model, instead of on the data themselves. Here, we use multivariate normal distributions to fit the data, because multivariate normal distributions have maximum entropy among all real-valued distributions with a specified mean and standard deviation and are widely used to approximate various distributions. Given that the data follow a multivariate normal distribution, since the conditional distribution of class variables given the selected features is a normal distribution, its entropy can be computed with the log-determinant of its covariance matrix. Because of the large number of genes, the computation of all possible log-determinants is not efficient. We propose several algorithms to largely reduce the computational cost. The experiments on seven gene data sets and the comparison with other five approaches show the accuracy of the multivariate Gaussian generative model for feature selection, and the efficiency of our algorithms.     

Modelling regulatory pathways in E. coli from time series expression profiles

MOTIVATION: Cells continuously reprogram their gene expression network as they move through the cell cycle or sense changes in their environment. In order to understand the regulation of cells, time series expression profiles provide a more complete picture than single time point expression profiles. Few analysis techniques, however, are well suited to modelling such time series data. RESULTS: We describe an approach that naturally handles time series data with the capabilities of modelling causality, feedback loops, and environmental or hidden variables using a Dynamic Bayesian network. We also present a novel way of combining prior biological knowledge and current observations to improve the quality of analysis and to model interactions between sets of genes rather than individual genes. Our approach is evaluated on time series expression data measured in response to physiological changes that affect tryptophan metabolism in E. coli. Results indicate that this approach is capable of finding correlations between sets of related genes.     

Effects of intermittent media replacement on the gene expression of differentiating neural progenitor cells

A culture medium provides the major environmental conditions for cells in vitro. Replenishment of a culture medium causes an abrupt change in the extracellular environment for maintaining cells in a certain state. As a primitive form of a complex system, a stem cell is likely to be influenced by culture conditions that can change the destination of development. To understand how the change in extracellular environment can influence a biological system, we studied the effect of culture media replacement on the gene expression of differentiating neural progenitor cells. From time-series microarray gene expression data of neural progenitor cells, we observed a periodic wave that was synchronized with intermittent culture media replacement. We identified three modes that mostly contribute to the periodic patterns in gene expression and investigated mode-related genes that are sensitive to the changes in the extracellular environment. The biological significance of the three modes was explored, such as progressive development and cell fate decision, extracellular matrix reassembly, and cell growth regulation in response to stress. In addition, we explored systemic influences of media replacement on differentiating neural progenitor cells. Intermittent culture media replacement interrupts expression of genes that participate in the major processes of differentiating neural progenitor cells. This study shows how the abrupt changes in the cell environment influence gene expression systematically.     

Gene signatures reflect the marked heterogeneity of tissue-resident macrophages

Tissue-resident macrophages play an important role in defense against pathogens and perform key functions in organ homeostasis, innate and adaptive immunity. Tissue macrophages originate from blood monocytes that infiltrate virtually every organ in the body. Macrophages in different tissues share many characteristics, including their ability to migrate, phagocytose particles, metabolize lipids and present antigens. Morphologically they are quite heterogeneous, and some distinct functions have been reported. The gene expression profile of macrophages is reflective of both their shared and distinct biological functions. Here, we show that macrophages from murine spleen, liver and peritoneum display dramatically different expression profiles. Clusters of genes were found to represent unique biological functions related to adhesion, antigen presentation, phagocytosis, lipid metabolism and signal transduction. Some gene families, such as integrins, are differentially expressed among the macrophages resident in different tissues, suggesting that the tissue of residence influences their biological function.     

Knowledge-based gene expression classification via matrix factorization

MOTIVATION: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks. RESULTS: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.     

The influence of adiponectin on the transcriptomic profile of porcine luteal cells

Reproductive functions are closely related to nutritional status. Recent studies suggest that adiponectin may be a hormonal link between them. Adiponectin is an adipocytokine, abundantly expressed in adipose tissues. It plays a dominant role in lipid and carbohydrate metabolism by stimulating fatty acid oxidation, decreasing plasma triglycerides, and increasing cells’ sensitivity to insulin and has direct antiatherosclerotic effects. The hormone is also postulated to play a modulatory role in the regulation of the reproductive system. The aim of this study was to identify differentially expressed genes (DE-genes) in response to adiponectin treatment of porcine luteal ovarian cells. The global expression of genes in the porcine ovary was investigated using the Porcine (V2) Two-color gene expression microarray, 4?×?44 (Agilent, USA). Analysis of the microarray data showed that 701 genes were differentially expressed and 389 genes showed a fold change greater than 1.2 (p?<?0.05). Among this number, 186 genes were up-regulated and 203 were down-regulated. The list of DE-genes was used for gene ontology analyses. The biological process list was generated from up-regulated and down-regulated DE-genes. We found that up-regulated products of DE-genes take part in 30 biological processes and down-regulated products in 9. Analysis of the interaction network among DE-genes showed that adiponectin interacts with genes involved in important processes in luteal cells. These results provide a basis for future work describing the detailed interactions and relationships explaining local regulation of adiponectin actions in the ovary of pigs.     

A decomposition model to track gene expression signatures: preview on observer-independent classification of ovarian cancer

MOTIVATION: A number of algorithms and analytical models have been employed to reduce the multidimensional complexity of DNA array data and attempt to extract some meaningful interpretation of the results. These include clustering, principal components analysis, self-organizing maps, and support vector machine analysis. Each method assumes an implicit model for the data, many of which separate genes into distinct clusters defined by similar expression profiles in the samples tested. A point of concern is that many genes may be involved in a number of distinct behaviours, and should therefore be modelled to fit into as many separate clusters as detected in the multidimensional gene expression space. The analysis of gene expression data using a decomposition model that is independent of the observer involved would be highly beneficial to improve standard and reproducible classification of clinical and research samples. RESULTS: We present a variational independent component analysis (ICA) method for reducing high dimensional DNA array data to a smaller set of latent variables, each associated with a gene signature. We present the results of applying the method to data from an ovarian cancer study, revealing a number of tissue type-specific and tissue type-independent gene signatures present in varying amounts among the samples surveyed. The observer independent results of such molecular analysis of biological samples could help identify patients who would benefit from different treatment strategies. We further explore the application of the model to similar high-throughput studies.     

Clustering of gene expression data using a local shape-based similarity measure

MOTIVATION: Microarray technology enables the study of gene expression in large scale. The application of methods for data analysis then allows for grouping genes that show a similar expression profile and that are thus likely to be co-regulated. A relationship among genes at the biological level often presents itself by locally similar and potentially time-shifted patterns in their expression profiles. RESULTS: Here, we propose a new method (CLARITY; Clustering with Local shApe-based similaRITY) for the analysis of microarray time course experiments that uses a local shape-based similarity measure based on Spearman rank correlation. This measure does not require a normalization of the expression data and is comparably robust towards noise. It is also able to detect similar and even time-shifted sub-profiles. To this end, we implemented an approach motivated by the BLAST algorithm for sequence alignment.We used CLARITY to cluster the times series of gene expression data during the mitotic cell cycle of the yeast Saccharomyces cerevisiae. The obtained clusters were related to the MIPS functional classification to assess their biological significance. We found that several clusters were significantly enriched with genes that share similar or related functions.     

Empirical evidence of the applicability of functional clustering through gene expression classification

The availability of a great range of prior biological knowledge about the roles and functions of genes and gene-gene interactions allows us to simplify the analysis of gene expression data to make it more robust, compact, and interpretable. Here, we objectively analyze the applicability of functional clustering for the identification of groups of functionally related genes. The analysis is performed in terms of gene expression classification and uses predictive accuracy as an unbiased performance measure. Features of biological samples that originally corresponded to genes are replaced by features that correspond to the centroids of the gene clusters and are then used for classifier learning. Using 10 benchmark data sets, we demonstrate that functional clustering significantly outperforms random clustering without biological relevance. We also show that functional clustering performs comparably to gene expression clustering, which groups genes according to the similarity of their expression profiles. Finally, the suitability of functional clustering as a feature extraction technique is evaluated and discussed.