Patterns In Gene Expression Data Research Articles

Machine Learning Methodologies for Clustering Gene Expression Data in Cancer

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism. Extracting the hidden patterns in gene expression data helps to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, thousands of gens can be analyzed at a time using clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to understand natural structures and identify interesting patterns in the underlying gene expression data [2]. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, finding useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in drug design. Clustering is a useful method that groups items based on certain similarity measures for understanding the structures, functions, regulation of genes, and cellular processes obtained from gene expression data and providing more insight on a given data set [13].
 This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.

The C-SHIFT Algorithm for Normalizing Covariances.

Omics technologies are powerful tools for analyzing patterns in gene expression data for thousands of genes. Due to a number of systematic variations in experiments, the raw gene expression data is often obfuscated by undesirable technical noises. Various normalization techniques were designed in an attempt to remove these non-biological errors prior to any statistical analysis. One of the reasons for normalizing data is the need for recovering the covariance matrix used in gene network analysis. In this paper, we introduce a novel normalization technique, called the covariance shift (C-SHIFT) method. This normalization algorithm uses optimization techniques together with the blessing of dimensionality philosophy and energy minimization hypothesis for covariance matrix recovery under additive noise (in biology, known as the bias). Thus, it is perfectly suited for the analysis of logarithmic gene expression data. Numerical experiments on synthetic data demonstrate the method's advantage over the classical normalization techniques. Namely, the comparison is made with Rank, Quantile, cyclic LOESS (locally estimated scatterplot smoothing), and MAD (median absolute deviation) normalization methods. We also evaluate the performance of C-SHIFT algorithm on real biological data.

Non-swarm intelligence algorithms: a case study

The case study of plant intelligence inspired novel non-swarm intelligence (NSI) algorithms, namely Venus Flytrap Optimization and Bladder-Worts Suction, concentrated in this paper. These algorithms devised on the prey-hunting mechanisms of the Venus Flytrap (Dionaea Muscipula) and BladderWorts (Utricularia) plants, respectively. A comparative view of these algorithms is discussed. The main-support criterion is the major characteristic of these approaches. The benefits of this main-support criterion and their performances are evidenced with a case study of extracting the highly correlated maximal local patterns in gene expression data through biclustering. The NSI algorithms are proposed for biclustering gene expression data in this paper. The results are compared with existing optimization techniques like PSO and GA, and biclustering approaches like Cheng and Church, OPSM, BiMax, and Plaid approaches. This analysis evidenced the performance of NSI algorithms can yield optimal maximal local patterns with high correlation. Further, various real-time research applications of NSI approaches are also discussed.

Impact of Fuzzy Normalization on Clustering Microarray Temporal Datasets Using Cuckoo Search

Microarrays have reformed biotechnological research in the past decade. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks with larger volume of genes also increases the challenges of comprehending and interpretation of the resulting mass of data. Clustering addresses these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and molecular functions. Clustering techniques are used to examine gene expression data to extract groups of genes from the tested samples based on a similarity criterion. Subspace clustering broadens the traditional clustering by extracting the groups of genes that are highly correlated in different subspace within the dataset. Mining the temporal patterns in high dimensional data is done with computational effort and thus normalization is needed. In this work, normalization using fuzzy logic is applied to the data before clustering. The multi-objective cuckoo search optimization is implemented to extract co-expressed genes over different subspaces. The proposed methods are applied to the real life temporal gene expression datasets in which it extracts the genes that are responsible for the disease grouped in a same cluster. The experiment results prove that the impact of fuzzy normalization on the dataset improves the clustering.

ICeD-T Provides Accurate Estimates of Immune Cell Abundance in Tumor Samples by Allowing for Aberrant Gene Expression Patterns

Immunotherapies have attracted lots of research interests recently. The need to understand the underlying mechanisms of immunotherapies and to develop precision immunotherapy regimens has spurred great interest in characterizing immune cell composition within the tumor microenvironment. Several methods have been developed to estimate immune cell composition using gene expression data from bulk tumor samples. However, these methods are not flexible enough to handle aberrant patterns of gene expression data, for example, inconsistent cell type-specific gene expression between purified reference samples and tumor samples. We propose a novel statistical method for expression deconvolution called immune cell deconvolution in tumor tissues (ICeD-T). ICeD-T automatically identifies aberrant genes whose expression are inconsistent with the deconvolution model and down-weights their contributions to cell type abundance estimates. We evaluated the performance of ICeD-T versus existing methods in simulation studies and several real data analyses. ICeD-T displayed comparable or superior performance to these competing methods. Applying these methods to assess the relationship between immunotherapy response and immune cell composition, ICeD-T is able to identify significant associations that are missed by its competitors. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Co-expression networks reveal the tissue-specific regulation of transcription and splicing.

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.

Distinct metabolic network states manifest in the gene expression profiles of pediatric inflammatory bowel disease patients and controls

Information on biological networks can greatly facilitate the function-orientated interpretation of high-throughput molecular data. Genome-wide metabolic network models of human cells, in particular, can be employed to contextualize gene expression profiles of patients with the goal of both, a better understanding of individual etiologies and an educated reclassification of (clinically defined) phenotypes. We analyzed publicly available expression profiles of intestinal tissues from treatment-naive pediatric inflammatory bowel disease (IBD) patients and age-matched control individuals, using a reaction-centric metabolic network derived from the Recon2 model. By way of defining a measure of ‘coherence’, we quantified how well individual patterns of expression changes matched the metabolic network. We observed a bimodal distribution of metabolic network coherence in both patients and controls, albeit at notably different mixture probabilities. Multidimensional scaling analysis revealed a bisectional pattern as well that overlapped widely with the metabolic network-based results. Expression differences driving the observed bimodality were related to cellular transport of thiamine and bile acid metabolism, thereby highlighting the crosstalk between metabolism and other vital pathways. We demonstrated how classical data mining and network analysis can jointly identify biologically meaningful patterns in gene expression data.

A contiguous column coherent evolution biclustering algorithm for time-series gene expression data

As a high-throughput detection technology, the gene chips produce huge amount of gene expression data. How to effectively analyze the data has become an urgent need. Biclustering techniques have been used as important tools to find the local patterns in gene expression data. Biclustering is to find submatrices, so that a subset of the genes shows a “highly correlated behavior in a subset of conditions”. However, most existing biclustering algorithms are not able to find biclusters with contiguous columns. Since there is important internal sequential relationship in time-series data, these methods are not suitable for the analysis of time-series data. In order to explore the potential biological information of contiguous time point and find the co-expressed relationship among genes, this paper presents an efficient, accurate algorithm named k-CCC algorithm, to search contiguous coherent evolution biclusters in time-series data. The first step of the algorithm is to transform the original matrix into a difference matrix; then starting from the column pattern consisting of contiguous k columns, we gradually assemble them into patterns composed of more columns. A pattern update strategy is adopted to improve the efficiency of the algorithm. The algorithm can find all the embedded biclusters and show good scalability in simulated tests. Experimental results on real datasets show that the algorithm can find biclusters with statistical significance and strong biological relevance.

Bayesian factor models for the detection of coherent patterns in gene expression data

A common problem in the analysis of gene expression microarray data is the identification of groups of features that are coherently expressed. For example, one often wishes to know whether a group of genes, clustered because of correlation in one data set, are still highly co-expressed in another data set. Alternatively, for some expression array platforms there are many, relatively short probes for each gene of interest. In this case, it is possible that a given probe is not measuring its targeted gene, but rather a different gene with a similar region (called cross-hybridization). Accurate detection of the collection of probe sets (groups of probes targeting the same gene) which demonstrate highly coherent expression patterns is the best approach to the identification of which genes are present in the sample. We develop a Bayesian Factor Model (BFM) to address the general problem of detection of coherent patterns in gene expression data sets. We compare our method to “state of the art” methods for the identification of expressed genes in both synthetic and real data sets, and the results indicate that the BFM outperforms the other procedures for detecting transcripts. We also demonstrate the use of factor analysis to identify the presence/absence status of gene modules (groups of coherently expressed genes). Variation in the number of copies of regions of the genome is a well known and important feature of most cancers. We examine a group of genes, representative of Copy Number Alteration (CNA) in breast cancer, then identify the presence/absence of CNA in this region of the genome for other cancers. Coherent patterns can also be evaluated in high-throughput sequencing data, a novel technology to measure gene expression. We analyze this type of data via factor model and examine the detection calls in terms of read mapping uncertainty.

MSL: A Measure to Evaluate Three-dimensional Patterns in Gene Expression Data.

Microarray technology is highly used in biological research environments due to its ability to monitor the RNA concentration levels. The analysis of the data generated represents a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior. Biclustering relaxes the constraints for grouping, allowing genes to be evaluated only under a subset of the conditions. Triclustering appears for the analysis of longitudinal experiments in which the genes are evaluated under certain conditions at several time points. These triclusters provide hidden information in the form of behavior patterns from temporal experiments with microarrays relating subsets of genes, experimental conditions, and time points. We present an evaluation measure for triclusters called Multi Slope Measure, based on the similarity among the angles of the slopes formed by each profile formed by the genes, conditions, and times of the tricluster.

Bi-Force: large-scale bicluster editing and its application to gene expression data biclustering

The explosion of the biological data has dramatically reformed today's biological research. The need to integrate and analyze high-dimensional biological data on a large scale is driving the development of novel bioinformatics approaches. Biclustering, also known as ‘simultaneous clustering’ or ‘co-clustering’, has been successfully utilized to discover local patterns in gene expression data and similar biomedical data types. Here, we contribute a new heuristic: ‘Bi-Force’. It is based on the weighted bicluster editing model, to perform biclustering on arbitrary sets of biological entities, given any kind of pairwise similarities. We first evaluated the power of Bi-Force to solve dedicated bicluster editing problems by comparing Bi-Force with two existing algorithms in the BiCluE software package. We then followed a biclustering evaluation protocol in a recent review paper from Eren et al. (2013) (A comparative analysis of biclustering algorithms for gene expressiondata. Brief. Bioinform., 14:279–292.) and compared Bi-Force against eight existing tools: FABIA, QUBIC, Cheng and Church, Plaid, BiMax, Spectral, xMOTIFs and ISA. To this end, a suite of synthetic datasets as well as nine large gene expression datasets from Gene Expression Omnibus were analyzed. All resulting biclusters were subsequently investigated by Gene Ontology enrichment analysis to evaluate their biological relevance. The distinct theoretical foundation of Bi-Force (bicluster editing) is more powerful than strict biclustering. We thus outperformed existing tools with Bi-Force at least when following the evaluation protocols from Eren et al. Bi-Force is implemented in Java and integrated into the open source software package of BiCluE. The software as well as all used datasets are publicly available at http://biclue.mpi-inf.mpg.de.

Possibilistic biclustering algorithm for discovering value-coherent overlapping δ-biclusters

One of the important tools for analyzing gene expression data is biclustering method. It focuses on finding a subset of genes and a subset of experimental conditions that together exhibit coherent behavior. However, most of the existing biclustering algorithms find exclusive biclusters, which is inappropriate in the context of biology. Since biological processes are not independent of each other, many genes may participate in multiple different processes. Hence, nonexclusive biclustering algorithms are required for finding overlapping biclusters. In this regard, a novel possibilistic biclustering algorithm is presented here to find highly overlapping biclusters of larger volume with mean squared residue lower than a predefined threshold. It judiciously incorporates the concept of possibilistic clustering algorithm into biclustering framework. The integration enables efficient selection of highly overlapping coherent biclusters with mean squared residue lower than a given threshold. The detailed formulation of the proposed possibilistic biclustering algorithm, along with a mathematical analysis on the convergence property, is presented. Some quantitative indices are introduced for evaluating the quality of generated biclusters. The effectiveness of the algorithm, along with a comparison with other algorithms, is demonstrated both qualitatively and quantitatively on yeast gene expression data set. In general, the proposed algorithm shows excellent performance at finding patterns in gene expression data.

Using partially ordered sets to represent and predict true patterns of gene response to treatments

Background Advances in biotechnology have empowered highthroughput measurement of gene expression levels for tens of thousands of genes simultaneously. This means that one sample size must be used for all genes in most experimental designs [1,2], which implies that patterns of response of highly variantly expressed genes might not be measured accurately. Response patterns of gene expression data with multiple treatments have been characterized using post hoc pairwise comparisons by several researchers [3,4]. Nevertheless, these researchers did not address how to cope with highly variantly expressed genes with inaccurate patterns due to having too few experimental samples.

A Survey on Different Feature Selection Methods for Microarray Data Analysis

In the field of medical science diseases diagnosis by Tissue microarrays is one of the active areas of research .There are various gene selection techniques in the literature. Gene selection provides genes subsets that are capable to describe in which category those gene are (active, hyperactive or silent).Various application areas like combinatorial chemistry, text mining, multivariate imaging, or bioinformatics are using huge data sets. The problem has been addressed of selection of a small subset of genes from broad patterns of gene expression data, recorded on DNA micro-arrays for cancer classification. Usually till now survey paper discuss various conventional & evolutionary methods of gene selection like filters, wrappers methods.

MDAG: A web tool for analyzing, visualizing, and interpreting response patterns in gene expression data with multiple treatments

Background: We previously introduced a method based on post hoc pairwise comparisons to analyze gene expression responses. This method utilized directed graphs to represent gene response to all treatment pairs. It has been found useful in identifying structure-activity relationships among drugs and differentiating genes sharing similar functional pathways. Directed graphs are descriptive, visually expressive and can benefit subsequent functional analysis. Results: mDAG is a web-based software package based on this established method for the analysis, visualization, and interpretation of patterns of responses in gene expression data involving multiple treatments. Genes with the same directed graph patterns hypothetically share similar biological function, which may be further analyzed using external tools. To facilitate subsequent functional analysis, several well-known tools have been incorporated into mDAG to help users explore hypotheses about gene function and regulation. This tool is useful for any studies that analyze comparatively response patterns in gene expression data with multiple treatments (chemicals, cell types, etc.). Availability: The (server/personal/demo) software is freely available at http://cetus.cs. memphis.edu/mdag.

CGCDB: A web‐based resource for the investigation of gene coexpression in CHO cell culture

Coexpression analysis is a powerful, widely used methodology for the investigation of underlying patterns in gene expression data. This "guilt-by-association" approach aims to find groups of genes with closely correlated expression profiles. Observation of consistent correlations across phenotypically diverse samples indicates that these genes have a shared function. We have recently described the application of weighted gene coexpression network analysis (WGCNA) to a 295 sample production CHO cell line microarray dataset and elucidated groups of genes related to growth rate and cell-specific productivity (Qp). In this study, we present the CHO gene coexpression database (CGCDB), a web-based system, designed specifically for researchers in the CHO community to provide user-friendly access to these gene-gene coexpression patterns. In addition to correlation between genes, the direct correlations between probesets and either growth rate or Qp are provided. Results are presented to the user via an interactive network diagram and in a downloadable tabular format. It is hoped that this resource will allow researchers to prioritize cell line engineering and/or biomarker candidates to enhance CHO-based cell culture for the production of biotherapeutics. www.cgcdb.org.

Importance of Correlation between Gene Expression Levels: Application to the Type I Interferon Signature in Rheumatoid Arthritis

BackgroundThe analysis of gene expression data shows that many genes display similarity in their expression profiles suggesting some co-regulation. Here, we investigated the co-expression patterns in gene expression data and proposed a correlation-based research method to stratify individuals.Methodology/Principal FindingsUsing blood from rheumatoid arthritis (RA) patients, we investigated the gene expression profiles from whole blood using Affymetrix microarray technology. Co-expressed genes were analyzed by a biclustering method, followed by gene ontology analysis of the relevant biclusters. Taking the type I interferon (IFN) pathway as an example, a classification algorithm was developed from the 102 RA patients and extended to 10 systemic lupus erythematosus (SLE) patients and 100 healthy volunteers to further characterize individuals. We developed a correlation-based algorithm referred to as Classification Algorithm Based on a Biological Signature (CABS), an alternative to other approaches focused specifically on the expression levels. This algorithm applied to the expression of 35 IFN-related genes showed that the IFN signature presented a heterogeneous expression between RA, SLE and healthy controls which could reflect the level of global IFN signature activation. Moreover, the monitoring of the IFN-related genes during the anti-TNF treatment identified changes in type I IFN gene activity induced in RA patients.ConclusionsIn conclusion, we have proposed an original method to analyze genes sharing an expression pattern and a biological function showing that the activation levels of a biological signature could be characterized by its overall state of correlation.

Mining and Analysing Spatio-Temporal Patterns of Gene Expression in An Integrative database Framework

SummaryMining patterns of gene expression provides a crucial approach in discovering knowledge such as finding genetic networks that underpin the embryonic development. Analysis of mining results and evaluation of their relevance in the domain remains a major concern. In this paper we describe our explorative studies in support of solutions to facilitate the analysis and interpretation of mining results. In our particular case we describe a solution that is found in the extension of the Gene Expression Management System (GEMS), i.e. an integrative framework for spatio-temporal organization of gene expression patterns of zebrafish to a framework supporting data mining, data analysis and patterns interpretation As a proof of principle, the GEMS has been equipped with data mining functionality suitable for spatio-temporal tracking, thereby generating added value to the submission of data for data mining and analysis. The analysis of the genetic networks is based on the availability of domain ontologies which dynamically provides meaning to the discovered patterns of gene expression data. Combination of data mining with the already presently available capabilities of GEMS will significantly augment current data processing and functional analysis strategies

Finding Biomarker Signatures in Pooled Sample Designs: A Simulation Framework for Methodological Comparisons

Detection of discriminating patterns in gene expression data can be accomplished by using various methods of statistical learning. It has been proposed that sample pooling in this context would have negative effects; however, pooling cannot always be avoided. We propose a simulation framework to explicitly investigate the parameters of patterns, experimental design, noise, and choice of method in order to find out which effects on classification performance are to be expected. We use a two-group classification task and simulated gene expression data with independent differentially expressed genes as well as bivariate linear patterns and the combination of both. Our results show a clear increase of prediction error with pool size. For pooled training sets powered partial least squares discriminant analysis outperforms discriminance analysis, random forests, and support vector machines with linear or radial kernel for two of three simulated scenarios. The proposed simulation approach can be implemented to systematically investigate a number of additional scenarios of practical interest.

Abstract 3977: Gene-expression signature specifically associated with triple-negative breast cancer pertaining to asian ethnicity

Abstract Triple-negative breast cancer is a subtype of breast cancer with strong aggressiveness of tumor behavior and distinct patterns of metastasis. This unique phenotype of breast cancer is given by its clinically negative expression of estrogen receptor (ER), progesterone receptors (PR), and HER2 protein. Triple-negative breast cancer patients often suffer from ineffective hormone therapies, high recurrence rate, and a predilection for visceral metastasis. An accurate gene-expression signature specifically associated with triple-negative breast cancer will facilitate the identification of the existence of this disease and provide insights of its etiology. DNA microarray analysis was used to evaluate gene-expression profiles of 181 Asian breast cancer patients. There were 48 breast cancer patients who were triple-negative, and 133 of them were non-triple-negative. Several clinical information were matched in each group, including lymphovascular invasion, age, stage, grade, tumor size, tubule formation, nuclear pleomorphism, and mitotic count. After all 181 microarray data were normalized with quantile normalization, significantly expressed genes differentiated from the triple-negative group and non-triple-negative group were extracted using SAM (Significant Analysis of Microarray) and T-test. A panel of 50 genes was selected as the gene-expression signature for triple-negative breast cancer for Asian ethnicity, based on their unique patterns of gene-expression data using hierarchical clustering. Pathway analysis of the signature genes for triple-negative breast cancer was then performed in the lab. Genetic interactions of significantly expressed genes among different pathways found in this study will be shown and elaborated on the poster. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3977.