Published since May 1, 2002

Statistical Applications in Genetics and Molecular Biology

ISSN: 1544-6115

Editor-in-chief: Guido Sanguinetti

Impact Factor: 0.419

About this journal

Objective
Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.

What scholars are saying about Statistical Applications in Genetics and Molecular Biology

“This journal will keep statisticians up to date on the thinking behind the development and validation of molecular biology based classifiers for diagnostic testing for use in such areas as early detection of disease or recurrence, risk stratification, prognosis, prediction of treatment response, monitoring, and drug dosing.”

Gene Pennello, Ph.D., Center for Devices and Radiological Health, FDA

“Statistical applications in Genetics and Genomics are very important areas of research. This journal is one of the high-quality journals that I use, and that my students use as well.”

Carl Lee, Professor of Mathematics, Central Michigan University

Topics

Linkage mapping
Association studies
Gene regulatory networks
Protein structure prediction
High-throughput data analysis
Molecular evolution and phylogenetic trees
Multi-omics data integration
Genetic and epigenetic data modelling

Article formats
Original Research Articles, Review Articles, Software Application Notes.

Your Benefits

Your benefits

Theoretical and computational innovations in the statistical analysis of biological data and systems
Practical applications of statistical ideas to computational biology, genomics, and systems biology and medicine
Specialized expertise in genomics, molecular, cellular and systems biology
Internationally recognized authors and editorial board
Rapid online publication of contributions as “ahead-of-print” versions
High quality peer review
Rapid “ahead-of-print” online publication of accepted papers
Open Access publication option available

History

Content available since 2002 (Volume 1, Issue 1)

Previously published by Berkeley Electronic Press

Other publications in the field

Previous Titles

Details on availability and prices of recent back volumes and issues will be provided on request from service@degruyter.com. All volumes of the majority of our journals* with the noted exception of the current volume are being offered by

Schmidt Periodicals GmbH, Bad Feilnbach
Tel.: (+49) 80 64 – 221
Fax: (+49) 80 64 – 557
Email: schmidt@periodicals.com
http://www.periodicals.com

(* For periodicals not included in this agreement, please refer to the distribution address provided on the homepage of the periodical.)

Abstracting and Indexing

Statistical Applications in Genetics and Molecular Biology is covered by the following services:

AGRICOLA (National Agricultural Library)
Baidu Scholar
Cabells Journalytics
Case
Chemical Abstracts Service (CAS) - CAplus
Chemical Abstracts Service (CAS) - SciFinder
Chemical Engineering and Biotechnology Abstracts
CNKI Scholar (China National Knowledge Infrastructure)
CNPIEC - cnpLINKer
Current Index to Statistics
Dimensions
EBSCO (relevant databases)
EBSCO Discovery Service
EconBiz
EMBASE
Genamics JournalSeek
Google Scholar
Japan Science and Technology Agency (JST)
J-Gate
Journal Citation Reports/Science Edition
JournalTOCs
KESLI-NDSL (Korean National Discovery for Science Leaders)
Mathematical Reviews (MathSciNet)
Medline
Meta
Microsoft Academic
MyScienceWork
Naver Academic
Naviga (Softweco)
Primo Central (ExLibris)
Publons
PubMed
QOAM (Quality Open Access Market)
ReadCube
Reaxys
Research Papers in Economics (RePEc)
SCImago (SJR)
SCOPUS
Semantic Scholar
Sherpa/RoMEO
Summon (ProQuest)
TDNet
Text Mining
Ulrich's Periodicals Directory/ulrichsweb
WanFang Data
Web of Science - Biological Abstracts
Web of Science - BIOSIS Previews
Web of Science - Current Contents/Life Sciences
Web of Science - Science Citation Index Expanded
WorldCat (OCLC)
Yewno Discover
zbMATH Open

Supplementary Materials

Topics

External Links

Online Submission of Manuscripts

Volume 20 Issue 4-6

December 2021

Accessible December 31, 2021

Frontmatter

Page range: i-ii

Download PDF

Research Articles

Open Access December 14, 2021

Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE)

Qing Xia, Jeffrey A. Thompson, Devin C. Koestler

Page range: 101-119

More Download PDF

Abstract

Batch-effects present challenges in the analysis of high-throughput molecular data and are particularly problematic in longitudinal studies when interest lies in identifying genes/features whose expression changes over time, but time is confounded with batch. While many methods to correct for batch-effects exist, most assume independence across samples; an assumption that is unlikely to hold in longitudinal microarray studies. We propose B atch effect R eduction of m I croarray data with D ependent samples usin G E mpirical Bayes ( BRIDGE ), a three-step parametric empirical Bayes approach that leverages technical replicate samples profiled at multiple timepoints/batches, so-called “bridge samples”, to inform batch-effect reduction/attenuation in longitudinal microarray studies. Extensive simulation studies and an analysis of a real biological data set were conducted to benchmark the performance of BRIDGE against both ComBat and longitudinal ComBat . Our results demonstrate that while all methods perform well in facilitating accurate estimates of time effects, BRIDGE outperforms both ComBat and longitudinal ComBat in the removal of batch-effects in data sets with bridging samples, and perhaps as a result, was observed to have improved statistical power for detecting genes with a time effect. BRIDGE demonstrated competitive performance in batch effect reduction of confounded longitudinal microarray studies, both in simulated and a real data sets, and may serve as a useful preprocessing method for researchers conducting longitudinal microarray studies that include bridging samples.

December 28, 2021

Inference of genetic regulatory networks with regulatory hubs using vector autoregressions and automatic relevance determination with model selections

Chi-Kan Chen

Page range: 121-143

Abstract

The inference of genetic regulatory networks (GRNs) reveals how genes interact with each other. A few genes can regulate many genes as targets to control cell functions. We present new methods based on the order-1 vector autoregression (VAR1) for inferring GRNs from gene expression time series. The methods use the automatic relevance determination (ARD) to incorporate the regulatory hub structure into the estimation of VAR1 in a Bayesian framework. Several sparse approximation schemes are applied to the estimated regression weights or VAR1 model to generate the sparse weighted adjacency matrices representing the inferred GRNs. We apply the proposed and several widespread reference methods to infer GRNs with up to 100 genes using simulated, DREAM4 in silico and experimental E. coli gene expression time series. We show that the proposed methods are efficient on simulated hub GRNs and scale-free GRNs using short time series simulated by VAR1s and outperform reference methods on small-scale DREAM4 in silico GRNs and E. coli GRNs. They can utilize the known major regulatory hubs to improve the performance on larger DREAM4 in silico GRNs and E. coli GRNs. The impact of nonlinear time series data on the performance of proposed methods is discussed.

Software and Application Note

November 9, 2021

Optimizing weighted gene co-expression network analysis with a multi-threaded calculation of the topological overlap matrix

Min Shuai, Dongmei He, Xin Chen

Page range: 145-153

Abstract

Biomolecular networks are often assumed to be scale-free hierarchical networks. The weighted gene co-expression network analysis (WGCNA) treats gene co-expression networks as undirected scale-free hierarchical weighted networks. The WGCNA R software package uses an Adjacency Matrix to store a network, next calculates the topological overlap matrix (TOM), and then identifies the modules (sub-networks), where each module is assumed to be associated with a certain biological function. The most time-consuming step of WGCNA is to calculate TOM from the Adjacency Matrix in a single thread. In this paper, the single-threaded algorithm of the TOM has been changed into a multi-threaded algorithm (the parameters are the default values of WGCNA). In the multi-threaded algorithm, Rcpp was used to make R call a C++ function, and then C++ used OpenMP to start multiple threads to calculate TOM from the Adjacency Matrix. On shared-memory MultiProcessor systems, the calculation time decreases as the number of CPU cores increases. The algorithm of this paper can promote the application of WGCNA on large data sets, and help other research fields to identify sub-networks in undirected scale-free hierarchical weighted networks. The source codes and usage are available at https://github.com/do-somethings-haha/multi-threaded_calculate_unsigned_TOM_from_unsigned_or_signed_Adjacency_Matrix_of_WGCNA.

Volume 20 (2021)

Volume 19 (2020)

Volume 18 (2019)

Volume 17 (2018)

Volume 16 (2017)

Volume 15 (2016)

Volume 14 (2015)

Volume 13 (2014)

Volume 12 (2013)

Volume 11 (2012)

Volume 7 (2008)

Volume 6 (2007)

Issue 1

Volume 5 (2006)

Issue 1

Volume 4 (2005)

Issue 1

Volume 3 (2004)

Issue 1

Volume 2 (2003)

Issue 1

Volume 1 (2002)

Issue 1

5-year Impact Factor	0.783
Cite Score	1.2
Impact Factor	0.419
Journal Citation Indicator	0.17
Mathematical Citation Quotient	0.06
SCImago Journal Rank	0.239
Source Normalized Impact per Paper	0.353

Submit

Submission
Online Submission of Manuscripts

Your benefits of publishing with us

Highly ranked statistics journal in the ISI Science Citation Index
International Editorial Board with experts from various fields in the relevant area
Decisions on your submission made as soon as possible
Every article easily discoverable because of Search Engine Optimization (SEO) and comprehensive abstracting and indexing services
Secure archiving by De Gruyter and the independent archiving service Portico
Professional sales and marketing activities
Ahead-of-print publishing ensures faster processing of fully proof-read, DOI-citable articles

Submission process

Submit your paper via our Scholar One submission management tool following the Instructions for Authors
After logging in you will be guided through every step of the submission process
In case of any problems the editorial staff will make its best to answer all your queries

Please note

Before submitting your article please have a look at our Copyright Transfer Agreement
Once your article is accepted you have the option to publish it open access
Our Repository Policy allows you to distribute 30 PDF copies of your published article to colleagues (the PDF has to include the information that it is an author's copy). Please also feel free to distribute the link to the online abstract
If you have any general questions please visit our FAQ for authors

We look forward to receiving your manuscript!

Hybrid Open Access

In this journal, authors have the option to publish their article under an open access license. Open Access allows you as an author to retain copyright and share your findings with colleagues and interested parties worldwide without any restraints.
Please note that authors from institutions with which we have a transformative agreement can publish open access without paying an article processing charge (APC). More information on the eligible institutions and articles can be found under the "Funding and Support" tab here.

Editorial

Editor-in-Chief
Guido Sanguinetti, International School of Advanced Studies Trieste

Founding Editors
Nicholas P. Jewell, University of California Berkeley
Gary Churchill, The Jackson Laboratory
Elizabeth Thompson, University of Washington

Associate Editors
Mark Beaumont, University of Bristol
Tim Beißbarth, University of Göttingen
Harald Binder, Universitty of Freiburg
Giulio Caravagna, University of Trieste
Frank Dondelinger, Lancaster University
Mayetri Gupta, University of Glasgow
Antti Honkela, University of Helsinki
Alan Hubbard, University of California
Dirk Husmeier, University of Glasgow
Hongkai Ji, Johns Hopkins University
Sunduz Keles, University of Wisconsin
Shili Lin, Ohio State University
Ping Ma, University of Illinois at Urbana-Champaign
Vincent Macaulay, University of Glasgow
Paul Marjoram, University of Southern California
Bart Mertens, Leiden University Medical Centre
Sach Mukherjee, German Center for Neurodegenerative Diseases Bonn
Olle Nerman, Chalmers University of Technology
Enrico G. Petretto, Imperial College London
John Pinney, Imperial College London
Magnus Rattray, University of Manchester
Andrey Rzhetsky, University of Chicago
Nicolas Salamin, University of Lausanne
Michael Stumpf, University of Melbourne
Catalina Vallejos, University of Edinburgh
Mark van der Laan, University of California Berkeley
Arndt von Haeseler, Center for Integrative Bioinformatics Vienna
Ernst Wit, University of Groningen
Christopher Yau, University of Birmingham

SAGMB Editorial Office
Walter de Gruyter GmbH
Genthiner Str. 13
10785 Berlin
Germany
Tel. +49-30-26005-176
Fax: +49-30-26005-352
E-mail: sagmb.editorial@degruyter.com

(Deutsch)

Online ISSN: 1544-6115
Type: Journal
Language: English
Publisher: De Gruyter
First published: May 1, 2002
Publication Frequency: 6 Issues per Year
Audience: Researchers and practitioners interested in statistical applications in genetics and molecular biology