Addressing noise in co-expression network construction.
co-expression
gene expression
multidimensional
networks
noise
Journal
Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837
Informations de publication
Date de publication:
17 01 2022
17 01 2022
Historique:
received:
08
08
2021
revised:
25
10
2021
accepted:
28
10
2021
pubmed:
2
12
2021
medline:
8
4
2022
entrez:
1
12
2021
Statut:
ppublish
Résumé
Gene co-expression networks (GCNs) provide multiple benefits to molecular research including hypothesis generation and biomarker discovery. Transcriptome profiles serve as input for GCN construction and are derived from increasingly larger studies with samples across multiple experimental conditions, treatments, time points, genotypes, etc. Such experiments with larger numbers of variables confound discovery of true network edges, exclude edges and inhibit discovery of context (or condition) specific network edges. To demonstrate this problem, a 475-sample dataset is used to show that up to 97% of GCN edges can be misleading because correlations are false or incorrect. False and incorrect correlations can occur when tests are applied without ensuring assumptions are met, and pairwise gene expression may not meet test assumptions if the expression of at least one gene in the pairwise comparison is a function of multiple confounding variables. The 'one-size-fits-all' approach to GCN construction is therefore problematic for large, multivariable datasets. Recently, the Knowledge Independent Network Construction toolkit has been used in multiple studies to provide a dynamic approach to GCN construction that ensures statistical tests meet assumptions and confounding variables are addressed. Additionally, it can associate experimental context for each edge of the network resulting in context-specific GCNs (csGCNs). To help researchers recognize such challenges in GCN construction, and the creation of csGCNs, we provide a review of the workflow.
Identifiants
pubmed: 34850822
pii: 6446269
doi: 10.1093/bib/bbab495
pmc: PMC8769892
pii:
doi:
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press.
Références
PLoS One. 2013;8(2):e55871
pubmed: 23409071
Nucleic Acids Res. 2012 May;40(10):4288-97
pubmed: 22287627
Plant Cell Physiol. 2007 Mar;48(3):381-90
pubmed: 17251202
Front Plant Sci. 2021 Jun 16;12:609684
pubmed: 34220875
Science. 2004 Jun 18;304(5678):1811-4
pubmed: 15166317
Bioinformatics. 2006 Oct 1;22(19):2396-404
pubmed: 16864591
Genome Res. 2011 Jul;21(7):1109-21
pubmed: 21536720
Nat Rev Genet. 2014 Jan;15(1):34-48
pubmed: 24296534
Bioinformatics. 2007 Aug 15;23(16):2096-103
pubmed: 17553854
Plant Physiol. 2017 Sep;175(1):568-583
pubmed: 28768814
Genome Res. 2004 Jun;14(6):1085-94
pubmed: 15173114
Bioinformatics. 2012 Mar 15;28(6):882-3
pubmed: 22257669
PLoS Genet. 2014 Jan;10(1):e1004006
pubmed: 24391511
PLoS Genet. 2006 Aug 18;2(8):e130
pubmed: 16934000
PLoS One. 2011 Feb 18;6(2):e17258
pubmed: 21364756
Sci Rep. 2019 Oct 8;9(1):14431
pubmed: 31594989
PLoS One. 2013 Jul 16;8(7):e68551
pubmed: 23874666
BMC Bioinformatics. 2012 Dec 09;13:328
pubmed: 23217028
Genome Biol. 2014;15(12):550
pubmed: 25516281
Gene. 2016 Nov 15;593(1):225-234
pubmed: 27523473
Nat Rev Genet. 2019 Sep;20(9):536-548
pubmed: 31114032
BMC Syst Biol. 2016 Aug 01;10 Suppl 2:51
pubmed: 27490697
PLoS One. 2013 Dec 31;8(12):e84301
pubmed: 24391934
Sci Rep. 2018 May 3;8(1):6951
pubmed: 29725024
Nucleic Acids Res. 2018 Jul 2;46(W1):W60-W64
pubmed: 29912392
Plant Cell. 2016 Oct;28(10):2365-2384
pubmed: 27655842
Nucleic Acids Res. 2014 Dec 1;42(21):
pubmed: 25294822
PLoS One. 2011 Feb 28;6(2):e17238
pubmed: 21386892
PLoS One. 2015 Jan 20;10(1):e0116776
pubmed: 25602758
Oncotarget. 2018 Jan 13;9(13):10995-11008
pubmed: 29541392
Genome Res. 2009 Nov;19(11):1953-62
pubmed: 19797678
Plant Cell. 2011 Sep;23(9):3101-16
pubmed: 21896882
Nucleic Acids Res. 2013 Jan;41(Database issue):D991-5
pubmed: 23193258
Sci Rep. 2017 Aug 17;7(1):8617
pubmed: 28819158
Stat Appl Genet Mol Biol. 2016 Dec 1;15(6):447-471
pubmed: 27875323
BMC Syst Biol. 2013 Jun 05;7:44
pubmed: 23738693
BMC Bioinformatics. 2005 Sep 14;6:227
pubmed: 16162296
Nucleic Acids Res. 2011 Jan;39(Database issue):D19-21
pubmed: 21062823
Plant Cell. 2018 Dec;30(12):2922-2942
pubmed: 30413654
Bioinformatics. 2018 Jun 15;34(12):2136-2138
pubmed: 29425308
Front Plant Sci. 2019 Oct 31;10:1409
pubmed: 31737022
Bioinformatics. 2004 Sep 22;20(14):2242-50
pubmed: 15130938
PLoS Biol. 2007 Jan;5(1):e8
pubmed: 17214507
Proc Natl Acad Sci U S A. 1998 Dec 8;95(25):14863-8
pubmed: 9843981
Bioinformatics. 2017 Feb 15;33(4):612-614
pubmed: 27993773
In Silico Biol. 2005;5(3):239-49
pubmed: 15984935
Nat Biotechnol. 2010 Feb;28(2):149-56
pubmed: 20118918
Biopreserv Biobank. 2015 Oct;13(5):311-9
pubmed: 26484571
Plant Physiol. 2011 Jul;156(3):1244-56
pubmed: 21606319
Plant Physiol. 2011 Jul;156(3):1316-30
pubmed: 21571672
Stat Appl Genet Mol Biol. 2005;4:Article17
pubmed: 16646834
Bioinformatics. 2016 Jun 15;32(12):i164-i173
pubmed: 27307613
Bioinformatics. 2016 Jan 15;32(2):309-11
pubmed: 26415722
Science. 2003 Oct 10;302(5643):249-55
pubmed: 12934013
Bioinformatics. 2015 Jul 1;31(13):2123-30
pubmed: 25717192
BMC Evol Biol. 2006 Sep 12;6:70
pubmed: 16968540
Sci Rep. 2020 Oct 13;10(1):17089
pubmed: 33051491
Science. 2005 Sep 23;309(5743):2010-3
pubmed: 16179466
NAR Genom Bioinform. 2020 Sep;2(3):lqaa078
pubmed: 33015620
Mol Biol Evol. 2004 Nov;21(11):2058-70
pubmed: 15282333
EURASIP J Bioinform Syst Biol. 2007;:79879
pubmed: 18354736
BMC Bioinformatics. 2007 Aug 14;8:299
pubmed: 17697349