slfm: An R Package to Evaluate Coherent Patterns in Microarray Data via Factor Analysis

The development of simulation-based methods, such as Markov chain Monte Carlo (MCMC), has contributed to an increased interest in the Bayesian framework as an alternative to deal with factor models. Many studies have used Bayesian factor analysis to explore gene expression data. We are particularly interested in the application of a sparse latent factor model (SLFM) based on sparsity priors (mixtures) to assess the significance of factors. The SLFM measures how strong the observed coherent expression pattern is in the data, which is an important source of information to evaluate gene activity. In the literature, this type of model has shown better results than other approaches intended for identification of patterns and metagene groups related to the underlying biology. However, a full Bayesian factor model relying on MCMC algorithms has an expensive computational cost, which makes it unattractive for general users. In this paper, we present the package slfm which uses C++ implementation via Rcpp to improve the computational performance of the SLFM within the widely used statistical tool R. We investigate real and simulated microarray data related to breast cancer

Unsupervised Bayesian linear unmixing of gene expression microarrays

Background: This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters. Results: Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here. Conclusions: The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores collected during the study. Using a constrained model allows recovery of all the inflammatory genes in a single factor

Reconstructing regulatory networks from high-throughput post-genomic data using MCMC methods

Modern biological research aims to understand when genes are expressed and how certain genes in uence the expression of other genes. For organizing and visualizing gene expression activity gene regulatory networks are used. The architecture of these networks holds great importance, as they enable us to identify inconsistencies between hypotheses and observations, and to predict the behavior of biological processes in yet untested conditions. Data from gene expression measurements are used to construct gene regulatory networks. Along with the advance of high-throughput technologies for measuring gene expression statistical methods to predict regulatory networks have also been evolving. This thesis presents a computational framework based on a Bayesian modeling technique using state space models (SSM) for the inference of gene regulatory networks from time-series measurements. A linear SSM consists of observation and hidden state equations. The hidden variables can unfold effects that cannot be directly measured in an experiment, such as missing gene expression. We have used a Bayesian MCMC approach based on Gibbs sampling for the inference of parameters. However the task of determining the dimension of the hidden state space variables remains crucial for the accuracy of network inference. For this we have used the Bayesian evidence (or marginal likelihood) as a yardstick. In addition, the Bayesian approach also provides the possibility of incorporating prior information, based on literature knowledge. We compare marginal likelihoods calculated from the Gibbs sampler output to the lower bound calculated by a variational approximation. Before using the algorithm for the analysis of real biological experimental datasets we perform validation tests using numerical experiments based on simulated time series datasets generated by in-silico networks. The robustness of our algorithm can be measured by its ability to recapture the input data and generating networks using the inferred parameters. Our developed algorithm, GBSSM, was used to infer a gene network using E. coli data sets from the different stress conditions of temperature shift and acid stress. The resulting model for the gene expression response under temperature shift captures the effects of global transcription factors, such as fnr that control the regulation of hundreds of other genes. Interestingly, we also observe the stress-inducible membrane protein OsmC regulating transcriptional activity involved in the adaptation mechanism under both temperature shift and acid stress conditions. In the case of acid stress, integration of metabolomic and transcriptome data suggests that the observed rapid decrease in the concentration of glycine betaine is the result of the activation of osmoregulators which may play a key role in acid stress adaptation

Robust Detection and Genotyping of Single Feature Polymorphisms from Gene Expression Data

It is well known that Affymetrix microarrays are widely used to predict genome-wide gene expression and genome-wide genetic polymorphisms from RNA and genomic DNA hybridization experiments, respectively. It has recently been proposed to integrate the two predictions by use of RNA microarray data only. Although the ability to detect single feature polymorphisms (SFPs) from RNA microarray data has many practical implications for genome study in both sequenced and unsequenced species, it raises enormous challenges for statistical modelling and analysis of microarray gene expression data for this objective. Several methods are proposed to predict SFPs from the gene expression profile. However, their performance is highly vulnerable to differential expression of genes. The SFPs thus predicted are eventually a reflection of differentially expressed genes rather than genuine sequence polymorphisms. To address the problem, we developed a novel statistical method to separate the binding affinity between a transcript and its targeting probe and the parameter measuring transcript abundance from perfect-match hybridization values of Affymetrix gene expression data. We implemented a Bayesian approach to detect SFPs and to genotype a segregating population at the detected SFPs. Based on analysis of three Affymetrix microarray datasets, we demonstrated that the present method confers a significantly improved robustness and accuracy in detecting the SFPs that carry genuine sequence polymorphisms when compared to its rivals in the literature. The method developed in this paper will provide experimental genomicists with advanced analytical tools for appropriate and efficient analysis of their microarray experiments and biostatisticians with insightful interpretation of Affymetrix microarray data

MAGIA, a web-based tool for miRNA and Genes Integrated Analysis

MAGIA (miRNA and genes integrated analysis) is a novel web tool for the integrative analysis of target predictions, miRNA and gene expression data. MAGIA is divided into two parts: the query section allows the user to retrieve and browse updated miRNA target predictions computed with a number of different algorithms (PITA, miRanda and Target Scan) and Boolean combinations thereof. The analysis section comprises a multistep procedure for (i) direct integration through different functional measures (parametric and non-parametric correlation indexes, a variational Bayesian model, mutual information and a meta-analysis approach based on P-value combination) of mRNA and miRNA expression data, (ii) construction of bipartite regulatory network of the best miRNA and mRNA putative interactions and (iii) retrieval of information available in several public databases of genes, miRNAs and diseases and via scientific literature text-mining. MAGIA is freely available for Academic users at http://gencomp.bio.unipd.it/magia

Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression Data

Gene expression time course data can be used not only to detect differentially expressed genes but also to find temporal associations among genes. The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from transcriptomic data is addressed. A network reconstruction algorithm was developed that uses statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. The multinomial hypothesis testing-based network reconstruction allows for explicit specification of the false-positive rate, unique from all extant network inference algorithms. The method is superior to dynamic Bayesian network modeling in a simulation study. Temporal gene expression data from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol are used for modeling. Genes from major neuronal pathways are identified as putative components of the alcohol response mechanism. Nine of these genes have associations with alcohol reported in literature. Several other potentially relevant genes, compatible with independent results from literature mining, may play a role in the response to alcohol. Additional, previously unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism

Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression Data

Gene expression time course data can be used not only to detect differentially expressed genes but also to find temporal associations among genes. The prsoblem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from transcriptomic data is addressed. A network reconstruction algorithm was developed that uses statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. The multinomial hypothesis testing-based network reconstruction allows for explicit specification of the false-positive rate, unique from all extant network inference algorithms. The method is superior to dynamic Bayesian network modeling in a simulation study. Temporal gene expression data from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol are used for modeling. Genes from major neuronal pathways are identified as putative components of the alcohol response mechanism. Nine of these genes have associations with alcohol reported in literature. Several other potentially relevant genes, compatible with independent results from literature mining, may play a role in the response to alcohol. Additional, previously unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism

Profiling time course expression of virus genes---an illustration of Bayesian inference under shape restrictions

There have been several studies of the genome-wide temporal transcriptional program of viruses, based on microarray experiments, which are generally useful in the construction of gene regulation network. It seems that biological interpretations in these studies are directly based on the normalized data and some crude statistics, which provide rough estimates of limited features of the profile and may incur biases. This paper introduces a hierarchical Bayesian shape restricted regression method for making inference on the time course expression of virus genes. Estimates of many salient features of the expression profile like onset time, inflection point, maximum value, time to maximum value, area under curve, etc. can be obtained immediately by this method. Applying this method to a baculovirus microarray time course expression data set, we indicate that many biological questions can be formulated quantitatively and we are able to offer insights into the baculovirus biology.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS258 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Modeling and visualizing uncertainty in gene expression clusters using Dirichlet process mixtures

Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture (DPM) models provide a nonparametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model-based clustering methods have been to short time series data. In this paper, we present a case study of the application of nonparametric Bayesian clustering methods to the clustering of high-dimensional nontime series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a DPM model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data

Towards knowledge-based gene expression data mining

The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing microarray analysis with data and knowledge from diverse available sources. In this review, we report on the plethora of gene expression data mining techniques and focus on their evolution toward knowledge-based data analysis approaches. In particular, we discuss recent developments in gene expression-based analysis methods used in association and classification studies, phenotyping and reverse engineering of gene networks