Individualized markers optimize class prediction of microarray data

BACKGROUND: Identification of molecular markers for the classification of microarray data is a challenging task. Despite the evident dissimilarity in various characteristics of biological samples belonging to the same category, most of the marker – selection and classification methods do not consider this variability. In general, feature selection methods aim at identifying a common set of genes whose combined expression profiles can accurately predict the category of all samples. Here, we argue that this simplified approach is often unable to capture the complexity of a disease phenotype and we propose an alternative method that takes into account the individuality of each patient-sample. RESULTS: Instead of using the same features for the classification of all samples, the proposed technique starts by creating a pool of informative gene-features. For each sample, the method selects a subset of these features whose expression profiles are most likely to accurately predict the sample's category. Different subsets are utilized for different samples and the outcomes are combined in a hierarchical framework for the classification of all samples. Moreover, this approach can innately identify subgroups of samples within a given class which share common feature sets thus highlighting the effect of individuality on gene expression. CONCLUSION: In addition to high classification accuracy, the proposed method offers a more individualized approach for the identification of biological markers, which may help in better understanding the molecular background of a disease and emphasize the need for more flexible medical interventions

EcoCyc: fusing model organism databases with systems biology.

EcoCyc (http://EcoCyc.org) is a model organism database built on the genome sequence of Escherichia coli K-12 MG1655. Expert manual curation of the functions of individual E. coli gene products in EcoCyc has been based on information found in the experimental literature for E. coli K-12-derived strains. Updates to EcoCyc content continue to improve the comprehensive picture of E. coli biology. The utility of EcoCyc is enhanced by new tools available on the EcoCyc web site, and the development of EcoCyc as a teaching tool is increasing the impact of the knowledge collected in EcoCyc

Unsupervised Algorithms for Microarray Sample Stratification

The amount of data made available by microarrays gives researchers the opportunity to delve into the complexity of biological systems. However, the noisy and extremely high-dimensional nature of this kind of data poses significant challenges. Microarrays allow for the parallel measurement of thousands of molecular objects spanning different layers of interactions. In order to be able to discover hidden patterns, the most disparate analytical techniques have been proposed. Here, we describe the basic methodologies to approach the analysis of microarray datasets that focus on the task of (sub)group discovery.Peer reviewe

Statistical approaches of gene set analysis with quantitative trait loci for high-throughput genomic studies.

Recently, gene set analysis has become the first choice for gaining insights into the underlying complex biology of diseases through high-throughput genomic studies, such as Microarrays, bulk RNA-Sequencing, single cell RNA-Sequencing, etc. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Further, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. Hence, a comprehensive overview of the available gene set analysis approaches used for different high-throughput genomic studies is provided. The analysis of gene sets is usually carried out based on gene ontology terms, known biological pathways, etc., which may not establish any formal relation between genotype and trait specific phenotype. Further, in plant biology and breeding, gene set analysis with trait specific Quantitative Trait Loci data are considered to be a great source for biological knowledge discovery. Therefore, innovative statistical approaches are developed for analyzing, and interpreting gene expression data from Microarrays, RNA-sequencing studies in the context of gene sets with trait specific Quantitative Trait Loci. The utility of the developed approaches is studied on multiple real gene expression datasets obtained from various Microarrays and RNA-sequencing studies. The selection of gene sets through differential expression analysis is the primary step of gene set analysis, and which can be achieved through using gene selection methods. The existing methods for such analysis in high-throughput studies, such as Microarrays, RNA-sequencing studies, suffer from serious limitations. For instance, in Microarrays, most of the available methods are either based on relevancy or redundancy measures. Through these methods, the ranking of genes is done on single Microarray expression data, which leads to the selection of spuriously associated, and redundant gene sets. Therefore, newer, and innovative differential expression analytical methods have been developed for Microarrays, and single-cell RNA-sequencing studies for identification of gene sets to successfully carry out the gene set and other downstream analyses. Furthermore, several methods specifically designed for single-cell data have been developed in the literature for the differential expression analysis. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to review the performance of the existing methods. Hence, a comprehensive overview, classification, and comparative study of the available single-cell methods is hereby undertaken to study their unique features, underlying statistical models and their shortcomings on real applications. Moreover, to address one of the shortcomings (i.e., higher dropout events due to lower cell capture rates), an improved statistical method for downstream analysis of single-cell data has been developed. From the users’ point of view, the different developed statistical methods are implemented in various software tools and made publicly available. These methods and tools will help the experimental biologists and genome researchers to analyze their experimental data more objectively and efficiently. Moreover, the limitations and shortcomings of the available methods are reported in this study, and these need to be addressed by statisticians and biologists collectively to develop efficient approaches. These new approaches will be able to analyze high-throughput genomic data more efficiently to better understand the biological systems and increase the specificity, sensitivity, utility, and relevance of high-throughput genomic studies

Pharmacoproteomic characterisation of human colon and rectal cancer

Most molecular cancer therapies act on protein targets but data on the proteome status of patients and cellular models for proteome-guided pre-clinical drug sensitivity studies are only beginning to emerge. Here, we profiled the proteomes of 65 colorectal cancer (CRC) cell lines to a depth of > 10,000 proteins using mass spectrometry. Integration with proteomes of 90 CRC patients and matched transcriptomics data defined integrated CRC subtypes, highlighting cell lines representative of each tumour subtype. Modelling the responses of 52 CRC cell lines to 577 drugs as a function of proteome profiles enabled predicting drug sensitivity for cell lines and patients. Among many novel associations, MERTK was identified as a predictive marker for resistance towards MEK1/2 inhibitors and immunohistochemistry of 1,074 CRC tumours confirmed MERTK as a prognostic survival marker. We provide the proteomic and pharmacological data as a resource to the community to, for example, facilitate the design of innovative prospective clinical trials. © 2017 The Authors. Published under the terms of the CC BY 4.0 licens

Incorporating biological information into linear models: A Bayesian approach to the selection of pathways and genes

The vast amount of biological knowledge accumulated over the years has allowed researchers to identify various biochemical interactions and define different families of pathways. There is an increased interest in identifying pathways and pathway elements involved in particular biological processes. Drug discovery efforts, for example, are focused on identifying biomarkers as well as pathways related to a disease. We propose a Bayesian model that addresses this question by incorporating information on pathways and gene networks in the analysis of DNA microarray data. Such information is used to define pathway summaries, specify prior distributions, and structure the MCMC moves to fit the model. We illustrate the method with an application to gene expression data with censored survival outcomes. In addition to identifying markers that would have been missed otherwise and improving prediction accuracy, the integration of existing biological knowledge into the analysis provides a better understanding of underlying molecular processes.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS463 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Computational models and approaches for lung cancer diagnosis

The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, the aim of this study is to developed novel lung cancer diagnostic models. New algorithms are proposed to analyse the biological data and extract knowledge that assists in achieving accurate diagnosis results

Gene selection and disease prediction from gene expression data using a two-stage hetero-associative memory

In general, gene expression microarrays consist of a vast number of genes and very few samples, which represents a critical challenge for disease prediction and diagnosis. This paper develops a two-stage algorithm that integrates feature selection and prediction by extending a type of hetero-associative neural networks. In the first level, the algorithm generates the associative memory, whereas the second level picks the most relevant genes.With the purpose of illustrating the applicability and efficiency of the method proposed here, we use four different gene expression microarray databases and compare their classification performance against that of other renowned classifiers built on the whole (original) feature (gene) space. The experimental results show that the two-stage hetero-associative memory is quite competitive with standard classification models regarding the overall accuracy, sensitivity and specificity. In addition, it also produces a significant decrease in computational efforts and an increase in the biological interpretability of microarrays because worthless (irrelevant and/or redundant) genes are discarded

VARIATIONS IN MICROARRAY BASED GENE EXPRESSION PROFILING: IDENTIFYING SOURCES AND IMPROVING RESULTS

Two major issues hinder the application of microarray based gene expression profiling in clinical laboratories as a diagnostic or prognostic tool. The first issue is the sheer volume and high-dimensionality of gene expression data from microarray experiments, which require advanced algorithms to extract meaningful gene expression patterns that correlate with biological impact. The second issue is the substantial amount of variation in microarray gene expression data, which impairs the performance of analysis method and makes sharing or integrating microarray data very difficult. Variations can be introduced by all possible sources including the DNA microarray technology itself and the experimental procedures. Many of these variations have not been characterized, measured, or linked to the sources. In the first part of this dissertation, a decision tree learning method was demonstrated to perform as well as more popularly accepted classification methods in partitioning cancer samples with microarray data. More importantly, results demonstrate that variation introduced into microarray data by tissue sampling and tissue handling compromised the performance of classification methods. In the second part of this dissertation, variations introduced by the T7 based in vitro transcription labeling methods were investigated in detail. Results demonstrated that individual amplification methods significantly biased gene expression data even though the methods compared in this study were all derivatives of the T7 RNA polymerase based in vitro transcription labeling approach. Variations observed can be partially explained by the number of biotinylated nucleotides used for labeling and the incubation time of the in vitro transcription experiments. These variations can generate discordant gene expression results even using the same RNA samples and cannot be corrected by post experiment analysis including advanced normalization techniques. Studies in this dissertation stress the concept that experimental and analytical methods must work together. This dissertation also emphasizes the importance of standardizing the DNA microarray technology and experimental procedures in order to optimize gene expression analysis and create quality standards compatible with the clinical application of this technology. These findings should be taken into account especially when comparing data from different platforms, and in standardizing protocols for clinical applications in pathology