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Estimating the Number of Genes That Are Differentially Expressed in Both of Two Independent Experiments

JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2012年第4期17卷 583-600页

作者： Orr, Megan Liu, Peng Nettleton, Dan Iowa State Univ Dept Stat Ames IA 50011 USA N Dakota State Univ Dept Stat Fargo ND 58108 USA

A common procedure for estimating the number of genes that are differentially expressed (DE) in two experiments involves two steps. In the first step, data from the two experiments are separately analyzed to produce a list of genes declared to be DE in each experiment. Usually, each list is produced using a method that attempts to control the false discovery rate (FDR) in each experiment at some desired level alpha. In the second step, the number of genes common to both lists is used as an estimate of the number of genes DE in both experiments. A problem with this approach is that the resulting estimates can vary greatly with alpha, and the value of alpha that produces the best estimate for any given pair of experiments is difficult to predict. We propose a method that uses the p-values from both experiments simultaneously to produce one estimate-which does not depend on FDR level alpha-for the number of genes that are DE in both experiments. We use two simulation studies (one involving independent, normally distributed data and one involving microarray data) to compare the performances of our proposed method, the commonly used method, and another method proposed in literature to test for consistency of replicate experiments. The results of the simulation studies demonstrate the advantages of our approach. We conclude the article by estimating the number of genes that are DE in both of two experiments involving gene expressions in maize leaves.

关键词： False discovery rate lambda-estimator microarray data analysis Multiple testing

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Improved significance test for DNA microarray data: temporal effects of shear stress on endothelial genes

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PHYSIOLOGICAL GENOMICS 2002年第1期12卷 1-11页

作者： Zhao, YH Chen, BPC Miao, H Yuan, SL Li, YS Hu, YL Rocke, DM Chien, S Univ Calif San Diego Whitaker Inst Biomed Engn La Jolla CA 92093 USA Univ Calif San Diego Dept Bioengn La Jolla CA 92093 USA Univ Calif Davis Dept Appl Sci Davis CA 95616 USA

Statistical methods for identifying differentially expressed genes from microarray data are evolving. We developed a test for the statistical significance of differential expression as a function of time. When applied to microarray data obtained from endothelial cells exposed to shearing for different durations, the new multi-group test (G-test) identified three times as many genes as the one-way ANOVA at the same significance level. Using simulated data, we showed that this increase in sensitivity was achieved without sacrificing specificity. Several genes known to respond to shear stress by Northern blotting were identified by the G-test at P less than or equal to 0.01 (but not by ANOVA), with similar temporal patterns. The validity and utility of the G-test were further supported by the examination of a few more example genes in relation to the present knowledge of their regulatory mechanisms. This new significance test may have broad application for the analysis of gene-expression studies and, in fact, to other biological studies in general.

关键词： biological variability microarray data analysis differential gene expression longitudinal analysis statistical test

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Promoter-motif extraction from co-regulated genes and their relevance to co-expression using E. coli as a model

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BRIEFINGS IN FUNCTIONAL GENOMICS 2023年第2期22卷 204-216页

作者： Nayarisseri, Anuraj Bhrdwaj, Anushka Khan, Arshiya Sharma, Khushboo Shaheen, Uzma Selvaraj, Chandrabose Khan, Mohammad Aqueel Abhirami, Rajaram Pravin, Muthuraja Arun Shri, Gurunathan Rubha Raje, Dhanjay Singh, Sanjeev Kumar Eminent Biosci 91 Sect AMahalakshmi Nagar Indore 452010 Madhya Pradesh India Sil Res Lab Eminent Biosci Mahalakshmi Nagar Indore 452010 India LeGene Biosci Pvt Ltd Bioinformat Res Lab 91 Sect AMahalakshmi Nagar Indore 452010 India Alagappa Univ Dept Bioinformat Comp Aided Drug Designing & Mol Modeling Lab Karaikkudi 630003 India Ctr Biomed Res Dept Data Sci SGPGIMS Campus Raebareli Rd Lucknow 226014 India

Gene expression varies due to the intrinsic stochasticity of transcription or as a reaction to external perturbations that generate cellular mutations. Co-regulation, co-expression and functional similarity of substances have been employed for indoctrinating the process of the transcriptional paradigm. The difficult process of analysing complicated proteomes and biological switches has been made easier by technical improvements, and microarray technology has flourished as a viable platform. Therefore, this research enables microarray to cluster genes that are co-expressed and co-regulated into specific segments. Copious search algorithms have been employed to ascertain diacritic motifs or a combination of motifs that are performing regular expression, and their relevant information corresponding to the gene patterns is also documented. The associated genes co-expression and relevant cis-elements are further explored by engaging Escherichia coli as a model organism. Various clustering algorithms have also been used to generate classes of genes with similar expression profiles. A promoter database 'EcoPromDB' has been developed by referring RegulonDB database;this promoter database is freely available at and is divided into two sub-groups, depending upon the results of co-expression and co-regulation analyses.

关键词： Gene regulation Co-expression Motif discovery microarray data analysis E coli coli promoter database EcopromDB

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Self-service infrastructure container for data intensive application

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2014年第1期3卷 1-21页

作者： Musa, Ibrahim K. Walker, Stuart D. Owen, Anne M. Harrison, Andrew P. Univ Essex Sch Comp Sci & Elect Engn Wivenhoe Pk Colchester CO4 3SQ Essex England Univ Essex Dept Math Sci & Biol Sci Colchester CO4 3SQ Essex England

Cloud based scientific data management - storage, transfer, analysis, and inference extraction - is attracting interest. In this paper, we propose a next generation cloud deployment model suitable for data intensive applications. Our model is a flexible and self-service container-based infrastructure that delivers - network, computing, and storage resources together with the logic to dynamically manage the components in a holistic manner. We demonstrate the strength of our model with a bioinformatics application. Dynamic algorithms for resource provisioning and job allocation suitable for the chosen dataset are packaged and delivered in a privileged virtual machine as part of the container. We tested the model on our private internal experimental cloud that is built on low-cost commodity hardware. We demonstrate the capability of our model to create the required network and computing resources and allocate submitted jobs. The results obtained shows the benefits of increased automation in terms of both a significant improvement in the time to complete a data analysis and a reduction in the cost of analysis. The algorithms proposed reduced the cost of performing analysis by 50% at 15 GB of data analysis. The total time between submitting a job and writing the results after analysis also reduced by more than 1 hr at 15 GB of data analysis.

关键词： Cloud computing microarray data analysis Bioinformatics Cells-As-A-Service

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Gene selection for tumor classification using a novel bio-inspired multi-objective approach

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GENOMICS 2018年第1期110卷 10-17页

作者： Dashtban, M. Balafar, Mohammadali Suravajhala, Prashanth Univ Tabriz Fac Elect & Comp Engn Dept Comp Engn Tabriz Iran Birla Inst Sci Res Jaipur 302001 Rajasthan India Bioclues Org Hyderabad 500072 Telangana India

Identifying the informative genes has always been a major step in microarray data analysis. The complexity of various cancer datasets makes this issue still challenging. In this paper, a novel Bio-inspired Multi-objective algorithm is proposed for gene selection in microarray data classification specifically in the binary domain of feature selection. The presented method extends the traditional Bat Algorithm with refined formulations, effective multi-objective operators, and novel local search strategies employing social learning concepts in designing random walks. A hybrid model using the Fisher criterion is then applied to three widely-used microarray cancer datasets to explore significant biomarkers which reveal the effectiveness of the proposed method for genomic analysis. Experimental results unveil new combinations of informative biomarkers have association with other studies.

关键词： Cancer classification Gene selection Bat algorithm Feature selection Evolutionary algorithms microarray data analysis

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There is no silver bullet - a guide to low-level data transforms and normalisation methods for microarray data

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BRIEFINGS IN BIOINFORMATICS 2005年第1期6卷 86-97页

作者： Kreil, DP Russell, RR Univ Cambridge Cambridge CB2 3EH England Univ Cambridge Cavendish Lab Inference Grp Cambridge CB3 0HE England Univ Cambridge Dept Genet Cambridge CB2 3EH England

To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficult to make the right choice of data transformation and normalisation methods to achieve this end. In this tutorial paper we review the problem and a selection of solutions, explaining the basic principles behind normalisation procedures and providing guidance for their application.

关键词： microarrays experimental bias data normalisation low-level data-transforms microarray data analysis

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A graph spectrum based geometric biclustering algorithm

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JOURNAL OF THEORETICAL BIOLOGY 2013年 317卷 200-211页

作者： Wang, Doris Z. Yan, Hong City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

Biclustering is capable of performing simultaneous clustering on two dimensions of a data matrix and has many applications in pattern classification. For example, in microarray experiments, a subset of genes is co-expressed in a subset of conditions, and biclustering algorithms can be used to detect the coherent patterns in the data for further analysis of function. In this paper, we present a graph spectrum based geometric biclustering (GSGBC) algorithm. In the geometrical view, biclusters can be seen as different linear geometrical patterns in high dimensional spaces. Based on this, the modified Hough transform is used to find the Hough vector (HV) corresponding to sub-bicluster patterns in 2D spaces. A graph can be built regarding each HV as a node. The graph spectrum is utilized to identify the eigengroups in which the sub-biclusters are grouped naturally to produce larger biclusters. Through a comparative study, we find that the GSGBC achieves as good a result as GBC and outperforms other kinds of biclustering algorithms. Also, compared with the original geometrical biclustering algorithm, it reduces the computing time complexity significantly. We also show that biologically meaningful biclusters can be identified by our method from real microarray gene expression data. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Geometric biclustering Hough transform Graph spectra microarray data analysis

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DK-means: a deterministic K-means clustering algorithm for gene expression analysis

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PATTERN analysis AND APPLICATIONS 2019年第2期22卷 649-667页

作者： Jothi, R. Mohanty, Sraban Kumar Ojha, Aparajita Pandit Deendayal Petr Univ Sch Technol Dept Comp Engn Gandhinagar India Indian Inst Informat Technol Design & Mfg Jabalpu Jabalpur Madhya Pradesh India

Clustering has been widely applied in interpreting the underlying patterns in microarray gene expression profiles, and many clustering algorithms have been devised for the same. K-means is one of the popular algorithms for gene data clustering due to its simplicity and computational efficiency. But, K-means algorithm is highly sensitive to the choice of initial cluster centers. Thus, the algorithm easily gets trapped with local optimum if the initial centers are chosen randomly. This paper proposes a deterministic initialization algorithm for K-means (DK-means) by exploring a set of probable centers through a constrained bi-partitioning approach. The proposed algorithm is compared with classical K-means with random initialization and improved K-means variants such as K-means++ and MinMax algorithms. It is also compared with three deterministic initialization methods. Experimental analysis on gene expression datasets demonstrates that DK-means achieves improved results in terms of faster and stable convergence, and better cluster quality as compared to other algorithms.

关键词： K-means clustering algorithm Initial cluster centers Gene expression clustering microarray data analysis

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Rotation tests

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STATISTICS AND COMPUTING 2005年第1期15卷 53-60页

作者： Langsrud, O MATFORSK N-1430 As Norway

This paper describes a generalised framework for doing Monte Carlo tests in multivariate linear regression. The rotation methodology assumes multivariate normality and is a true generalisation of the classical multivariate tests-any imaginable test statistic is allowed. The generalised test statistics are dependent on the unknown covariance matrix. Rotation testing handles this problem by conditioning on sufficient statistics. Compared to permutation tests, we replace permutations by proper random rotations. Permutation tests avoid the multinormal assumption, but they are limited to relatively simple models. On the other hand, a rotation test can, in particular, be applied to any multivariate generalisation of the univariate F-test. As an important application, a detailed description of how each single response p-value can be non-conservatively adjusted for multiplicity is given. This method is exact and non-conservative (unlike Bonferroni), and it is a generalisation of the ordinary F-test (except for the computation by simulations). Hence, this paper offers an exact Monte Carlo solution to a classical problem of multiple testing.

关键词： conditional inference multiple testing random orthogonal matrix adjusted p-value multiple endpoints spherical distribution microarray data analysis

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The Five-Gene-Network data analysis with Local Causal Discovery Algorithm Using Causal Bayesian Networks

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CHALLENGES OF SYSTEMS BIOLOGY: COMMUNITY EFFORTS TO HARNESS BIOLOGICAL COMPLEXITY 2009年第1期1158卷 93-101页

作者： Yoo, Changwon Brilz, Erik M. Univ Montana Missoula MT 59812 USA

Using microarray experiments, we can model causal relationships of genes measured through mRNA expression levels. To this end, it is desirable to compare experiments of the system under complete interventions of some genes, such as by knock out of some genes, with experiments of the system under no interventions. However, it. is expensive and difficult to conduct wet lab experiments of complete interventions of genes in a biological system. Thus, it will be helpful if we can discover promising causal relationships among genes with no interventions or incomplete interventions, such as by applying a treatment. that has unknown effects to modeled genes, in order to identify promising genes to perturb in the system dial can later be verified in wet laboratories. In this paper we use causal Bayesian networks to implement, a causal discovery algorithm-the equivalence local implicit. latent variable scoring method (EquLIM)-that identifies promising causal relationships even with a small dataset generated from no or incomplete interventions. We then apply EquLIM to analyze the five-gene-network data and compare EquLIM's predictions with true causal pairwise relationships between the genes.

关键词： causal Bayesian networks causal discovery gene networks high throughput data analysis microarray data analysis

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