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Robust and sparse correlation matrix estimation for the analysis of high-dimensional genomics data

BIOINFORMATICS 2018年第4期34卷 625-634页

作者： Serra, Angela Coretto, Pietro Fratello, Michele Tagliaferri, Roberto Univ Salerno Dept Management & Innovat Syst NeuRoNeLab I-84084 Fisciano Sa Italy Univ Salerno Dept Econ & Stat STATLAB I-84084 Fisciano Sa Italy Second Univ Napoli Dept Med Surg Neurol Metab & Ageing Sci Piazza Luigi Miraglia 2 I-80138 Naples Italy

Motivation: microarray technology can be used to study the expression of thousands of genes across a number of different experimental conditions, usually hundreds. The underlying principle is that genes sharing similar expression patterns, across different samples, can be part of the same co-expression system, or they may share the same biological functions. Groups of genes are usually identified based on cluster analysis. Clustering methods rely on the similarity matrix between genes. A common choice to measure similarity is to compute the sample correlation matrix. Dimensionality reduction is another popular data analysis task which is also based on covariance/correlation matrix estimates. Unfortunately, covariance/correlation matrix estimation suffers from the intrinsic noise present in high-dimensional data. Sources of noise are: sampling variations, presents of outlying sample units, and the fact that in most cases the number of units is much larger than the number of genes. Results: In this paper, we propose a robust correlation matrix estimator that is regularized based on adaptive thresholding. The resulting method jointly tames the effects of the high-dimensionality, and data contamination. Computations are easy to implement and do not require hand tunings. Both simulated and real data are analyzed. A Monte Carlo experiment shows that the proposed method is capable of remarkable performances. Our correlation metric is more robust to outliers compared with the existing alternatives in two gene expression datasets. It is also shown how the regularization allows to automatically detect and filter spurious correlations. The same regularization is also extended to other less robust correlation measures. Finally, we apply the ARACNE algorithm on the SyNTreN gene expression data. Sensitivity and specificity of the reconstructed network is compared with the gold standard. We show that ARACNE performs better when it takes the proposed correlation matrix estimator as

关键词： SPARSE matrices GENOMICS microarray technology GENE expression MONTE Carlo method

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Baseline Plasma Cell Gene Signature Predicts Improvement in Systemic Sclerosis Skin Scores Following Treatment With Inebilizumab (MEDI-551) and Correlates With Disease Activity in Systemic Lupus Erythematosus and Chronic Obstructive Pulmonary Disease

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ARTHRITIS & RHEUMATOLOGY 2018年第12期70卷 2087-2095页

作者： Streicher, Katie Sridhar, Sriram Kuziora, Mike Morehouse, Christopher A. Higgs, Brandon W. Sebastian, Yinong Groves, Christopher J. Pilataxi, Fernanda Brohawn, Philip Z. Herbst, Ronald Ranade, Koustubh MedImmune Gaithersburg MD USA

Objective B cells impact the progression of systemic sclerosis (SSc;scleroderma) through multiple pathogenic mechanisms. CD19 inhibition in mice reduced skin thickness, collagen production, and autoantibody levels, consistent with CD19 expression on plasma cells (PCs), the source of antibody production. PC depletion could effectively reduce collagen deposition and inflammation in SSc;therefore, we investigated the effects of PC depletion on SSc disease activity. Methods A PC gene signature was evaluated in SSc skin biopsy samples in 2 phase I clinical trials. We assessed microarray data from tissue from public studies of chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), dermatomyositis (DM), systemic lupus erythematosus (SLE), and atopic dermatitis, as well as blood from a phase IIb clinical trial in SLE. Results The PC signature was elevated in SSc skin specimens compared to healthy donor skin (P = 2.28 x 10(-6)) and correlated with the baseline modified Rodnan skin thickness score (MRSS) (r = 0.64, P = 0.0004). Patients with a high PC signature at baseline showed greater improvement in the MRSS (mean +/- SD change 35 +/- 16%;P = 6.30 x 10(-4)) following anti-CD19 treatment with inebilizumab (MEDI-551) than did patients with a low PC signature at baseline (mean +/- SD change 8 +/- 12%;P = 0.104). The PC signature was overexpressed in tissue from patients with SLE, DM, COPD, interstitial lung disease, and IPF relative to controls (all fold change >2;P < 0.001). The PC signature also differed significantly between SLE patients with mild-to-moderate disease and those with severe disease (SLE Disease Activity Index cutoff at 10) (fold change 1.44;P = 3.90 x 10(-3)) and correlated significantly with the degree of emphysema in COPD (r = 0.53, P = 7.55 x 10(-8)). Conclusion Our results support the notion that PCs have a role in the pathogenesis of SSc and other autoimmune or pulmonary indications. An elevated pretreatment PC signature was

关键词： THERAPEUTIC use of monoclonal antibodies ANTIGENS ATOPIC dermatitis BIOPSY DERMATOMYOSITIS PULMONARY emphysema GENES INTERSTITIAL lung diseases OBSTRUCTIVE lung diseases SYSTEMIC lupus erythematosus SYSTEMIC scleroderma SYMPTOMS SEVERITY of illness index microarray technology IDIOPATHIC pulmonary fibrosis

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Myriads: P-value-based multiple testing correction

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BIOINFORMATICS 2018年第6期34卷 1043-1045页

作者： Carvajal-Rodriguez, Antonio Univ Vigo Dept Biochem Genet & Immunol Address Fac Biol Vigo Spain

Motivation: There are many multiple testing correction methods. Some of them are robust to various dependencies in the data while others are not. Some of the implementations have problems for managing high dimensional list of P-values as currently demanded by microarray and other omic technologies. Results: The program Myriads, formerly SGoF+, provides some of the most important P-value-based correction methods jointly with a test of dependency and a P-value simulator. Myriads easily manage hundreds of thousands of P-values.

关键词： GENOME statistics P-value (Statistics) microarray technology BREAST cancer SIMULATION methods & models

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Well-trained sonographers are worth their weight in gold: ultrasound in systemic sclerosis

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RHEUMATOLOGY 2018年第11期57卷 1883-1884页

作者： Ruaro, Barbara Sakellariou, Garifallia Cutolo, Maurizio Iagnocco, Annamaria Univ Genoa Dept Internal Med Res Lab Policlin San Martino Genoa Italy Univ Genoa Dept Internal Med Acad Div Clin Rheumatol Policlin San Martino Genoa Italy Univ Pavia Chair & Div Rheumatol Policlin San Matteo Fdn Pavia Italy Univ Torino Dipartimento Sci Clin & Biol Turin Italy

The author conveys their thoughts on the use of ultrasonography (US) in systemic sclerosis (SSc). There is said to be a paucity of data in support of its advantages over high-resolution computed tomography. Power Doppler US in SSc patients may be also used to examine vasculopathy. A high prevalence of synovial hypertrophy, joint effusion, and bone damage was previously evidenced in SSc patients with arthralgia.

关键词： SKIN disease diagnosis FIBROSIS DIAGNOSIS DOPPLER ultrasonography ALLIED health personnel CLINICAL competence COMPUTED tomography INTERSTITIAL lung diseases MUSCULOSKELETAL system diseases RESPIRATORY function tests SERIAL publications SYSTEMIC scleroderma TENOSYNOVITIS ULTRASONIC imaging SYNOVITIS DISEASE prevalence microarray technology OLIGONUCLEOTIDE arrays ARTHRALGIA MEDICAL artifacts SYMPTOMS

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Hunting for hematopoietic transcriptional networks

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2018年第40期115卷 9818-9820页

作者： Kaushansky, Kenneth SUNY Stony Brook Sch Med Stony Brook NY 11794 USA

The article discusses the findings on the cellular development of blood patelets. It mentions the role of microarray technology in gene-expression research, hematopoietic stem, progenitor, and mature cell populations. It also mentions the information on megakaryocyte development along with the use of bioinformatics in the research.

关键词： microarray technology MEGAKARYOCYTES BIOINFORMATICS PROGENITOR cells BLOOD platelet examination

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The Cell Cycle Progression Score: Unclear Role in Renal Cell Carcinoma

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EUROPEAN UROLOGY 2018年第1期74卷 128-129页

作者： Ueno, Daiki Dancik, Garrett M. Shuch, Brian Yale Sch Med Dept Urol New Haven CT 06510 USA Eastern Connecticut State Univ Dept Comp Sci Willmantic CT USA

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Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for microarray Classification

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COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018年第1期2018卷 1-11页

作者： Xu, Jiucheng Mu, Huiyu Wang, Yun Huang, Fangzhou Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Peoples R China Engn Technol Res Ctr Comp Intelligence & Data Min Xinxiang 453007 Henan Peoples R China

The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman's rank correlation coefficient (SLLE-SC2), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman's rank correlation coefficient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible.

关键词： GENE expression FEATURE selection (Machine learning) microarray technology RANK correlation (Statistics) EMBEDDING theorems

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Proteomics and transcriptomics in atrial fibrillation

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Herzschrittmachertherapie und Elektrophysiologie 2018年第1期29卷 70-75页

作者： Sühling, Marc Wolke, Carmen Scharf, Christian Lendeckel, Uwe Institute of Medical Biochemistry and Molecular Biology University Medicine Greifswald Ernst Moritz Arndt University Greifswald Ferdinand-Sauerbruch-Straße Greifswald 17475 Germany Department of Otorhinolaryngology Head and Neck Surgery University Medicine Greifswald Ernst Moritz Arndt University Greifswald Greifswald Germany

Atrial fibrillation (AF) is the most common tachyarrhythmia. AF, due to substantial remodeling processes initiated in the atria, is a typically self-sustaining and progressive disease. Atrial remodeling has been intensively investigated at the molecular level in recent decades. Although the application of “omics” technologies has already significantly contributed to our current understanding of the pathophysiology of AF, the complexity of the latter and the large heterogeneity of AF patients remained a major limitation. With the advent of novel “omics” and by applying integrative approaches, it will be possible to extract more information and push boundaries. The present review will summarize the contribution of transcriptomics and proteomics to our understanding of the pathophysiology of AF. © 2018, Springer Medizin Verlag GmbH, ein Teil von Springer Nature.

关键词： 2D-DIGE microarray technology Rapid-pacing RNA sequencing Transcriptome

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Granular Fuzzy Possibilistic C-Means Clustering approach to DNA microarray problem

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KNOWLEDGE-BASED SYSTEMS 2017年 133卷 53-65页

作者： Hung Quoc Truong Long Thanh Ngo Pedrycz, Witold Le Quy Don Tech Univ Dept Informat Syst Hanoi Vietnam Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia Polish Acad Sci Warsaw Syst Res Inst Warsaw Poland

Deoxyribonucleic acid (DNA) microarray is an important technology, which supports a simultaneous measurement of thousands of genes for biological analysis. With the rapid development of the gene expression data characterized by uncertainty and being of high dimensionality, there is a genuine need for advanced processing techniques. With this regard, Fuzzy Possibilistic C-Means Clustering (FPCM) and Granular Computing (GrC) are introduced with the aim to solve problems of feature selection and outlier detection. In this study, by taking advantage of the FPCM and GrC, an Advanced Fuzzy Possibilistic C-Means Clustering based on Granular Computing (GrFPCM) is proposed to select features as a preprocessing phase for clustering problems while the developed granular space is used to cope with uncertainty. Experiments were completed for various gene expression datasets and a comparative analysis is reported. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Fuzzy clustering Fuzzy Possibilistic C-Means Clustering Granular computing Feature selection microarray technology DNA analysis Gene expression data

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Editorial Comment from Dr Miyamoto to Male infertility in Sertoli cell-only syndrome: An investigation of autosomal gene defects

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INTERNATIONAL JOURNAL OF UROLOGY 2019年第2期26卷 298-298页

作者： Miyamoto, Toshinobu Asahikawa Med Univ Dept Obstet & Gynecol Asahikawa Hokkaido Japan

The article focuses on the absence of germ cells in males with infertility which is associated with on-obstructive azoospermia (NOA) and Sertoli cellÃ¢â‚¬Âonly syndrome.

关键词： MALE infertility OLIGOSPERMIA COMPARATIVE genomic hybridization COMPLEMENTARY DNA microarray technology GERM cells

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