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      DOMINO: a network‐based active module identification algorithm with reduced rate of false calls

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          Abstract

          Algorithms for active module identification (AMI) are central to analysis of omics data. Such algorithms receive a gene network and nodes' activity scores as input and report subnetworks that show significant over‐representation of accrued activity signal (“active modules”), thus representing biological processes that presumably play key roles in the analyzed conditions. Here, we systematically evaluated six popular AMI methods on gene expression and GWAS data. We observed that GO terms enriched in modules detected on the real data were often also enriched on modules found on randomly permuted data. This indicated that AMI methods frequently report modules that are not specific to the biological context measured by the analyzed omics dataset. To tackle this bias, we designed a permutation‐based method that empirically evaluates GO terms reported by AMI methods. We used the method to fashion five novel AMI performance criteria. Last, we developed DOMINO, a novel AMI algorithm, that outperformed the other six algorithms in extensive testing on GE and GWAS data. Software is available at https://github.com/Shamir‐Lab.

          Abstract

          DOMINO is an algorithm for detecting active network modules with a low rate of false GO term calls. This merit is demonstrated by using EMP, a new procedure that validates GO terms empirically.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              Fast unfolding of communities in large networks

              Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008
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                Author and article information

                Contributors
                rshamir@tau.ac.il
                Journal
                Mol Syst Biol
                Mol Syst Biol
                10.1002/(ISSN)1744-4292
                MSB
                msb
                Molecular Systems Biology
                John Wiley and Sons Inc. (Hoboken )
                1744-4292
                20 January 2021
                January 2021
                : 17
                : 1 ( doiID: 10.1002/msb.v17.1 )
                : e9593
                Affiliations
                [ 1 ] The Blavatnik School of Computer Science Tel Aviv University Tel Aviv Israel
                [ 2 ] Department of Human Molecular Genetics and Biochemistry Sackler School of Medicine Tel Aviv University Tel Aviv Israel
                [ 3 ] Sagol School of Neuroscience Tel Aviv University Tel Aviv Israel
                Author notes
                [*] [* ] Corresponding author. Tel: +972 36405383; E‐mail: rshamir@ 123456tau.ac.il

                [†]

                These authors contributed equally to this work

                Author information
                https://orcid.org/0000-0003-3440-1286
                https://orcid.org/0000-0003-1889-9870
                Article
                MSB20209593
                10.15252/msb.20209593
                7816759
                33471440
                5c26b0f7-89ed-4751-a42f-c01b06c1e67f
                © 2021 The Authors. Published under the terms of the CC BY 4.0 license.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 24 March 2020
                : 09 November 2020
                : 11 November 2020
                Page count
                Figures: 11, Tables: 2, Pages: 16, Words: 10205
                Funding
                Funded by: Israel Science Foundation , open-funder-registry 10.13039/501100003977;
                Award ID: 1339/18
                Award ID: 2118/19
                Funded by: Len Blavatnik And The Blavatnik Family Foundation
                Funded by: Koret‐UC Berkeley‐Tel Aviv University Initiative in Computational Biology and Bioinformatics
                Funded by: Edmond J. Safra Center for Bioinformatics at Tel Aviv University
                Funded by: German Israeli Project DFG
                Award ID: RE 4193/1‐1
                Categories
                Article
                Articles
                Custom metadata
                2.0
                January 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.6 mode:remove_FC converted:20.01.2021

                Quantitative & Systems biology
                biological networks,enrichment analysis,go terms,module discovery,omics,computational biology

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