دورية أكاديمية
أعرض في EDS

Deconvolution from bulk gene expression by leveraging sample-wise and gene-wise similarities and single-cell RNA-Seq data.

التفاصيل البيبلوغرافية
العنوان: Deconvolution from bulk gene expression by leveraging sample-wise and gene-wise similarities and single-cell RNA-Seq data.
المؤلفون: Wang C; Department of Automation, Xiamen University, Xiamen, China., Lin Y; Department of Automation, Xiamen University, Xiamen, China., Li S; Department of Automation, Xiamen University, Xiamen, China., Guan J; Department of Automation, Xiamen University, Xiamen, China. jtguan@xmu.edu.cn.; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China. jtguan@xmu.edu.cn.; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China. jtguan@xmu.edu.cn.
المصدر: BMC genomics [BMC Genomics] 2024 Sep 18; Vol. 25 (1), pp. 875. Date of Electronic Publication: 2024 Sep 18.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: BioMed Central Country of Publication: England NLM ID: 100965258 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2164 (Electronic) Linking ISSN: 14712164 NLM ISO Abbreviation: BMC Genomics Subsets: MEDLINE
أسماء مطبوعة: Original Publication: London : BioMed Central, [2000-
مواضيع طبية MeSH: Single-Cell Analysis*/methods , RNA-Seq*/methods , Algorithms* , Gene Expression Profiling*/methods, Humans ; Sequence Analysis, RNA/methods ; Computational Biology/methods ; Transcriptome ; Single-Cell Gene Expression Analysis
مستخلص: Background: The widely adopted bulk RNA-seq measures the gene expression average of cells, masking cell type heterogeneity, which confounds downstream analyses. Therefore, identifying the cellular composition and cell type-specific gene expression profiles (GEPs) facilitates the study of the underlying mechanisms of various biological processes. Although single-cell RNA-seq focuses on cell type heterogeneity in gene expression, it requires specialized and expensive resources and currently is not practical for a large number of samples or a routine clinical setting. Recently, computational deconvolution methodologies have been developed, while many of them only estimate cell type composition or cell type-specific GEPs by requiring the other as input. The development of more accurate deconvolution methods to infer cell type abundance and cell type-specific GEPs is still essential.
Results: We propose a new deconvolution algorithm, DSSC, which infers cell type-specific gene expression and cell type proportions of heterogeneous samples simultaneously by leveraging gene-gene and sample-sample similarities in bulk expression and single-cell RNA-seq data. Through comparisons with the other existing methods, we demonstrate that DSSC is effective in inferring both cell type proportions and cell type-specific GEPs across simulated pseudo-bulk data (including intra-dataset and inter-dataset simulations) and experimental bulk data (including mixture data and real experimental data). DSSC shows robustness to the change of marker gene number and sample size and also has cost and time efficiencies.
Conclusions: DSSC provides a practical and promising alternative to the experimental techniques to characterize cellular composition and heterogeneity in the gene expression of heterogeneous samples.
(© 2024. The Author(s).)
References: Genome Res. 2021 Oct;31(10):1794-1806. (PMID: 34301624)
BMC Genomics. 2012 Nov 12;13:610. (PMID: 23145530)
PLoS One. 2009 Jul 01;4(7):e6098. (PMID: 19568420)
Nat Commun. 2018 Nov 9;9(1):4735. (PMID: 30413720)
Bioinformatics. 2018 Jun 1;34(11):1969-1979. (PMID: 29351586)
Nat Cancer. 2022 Apr;3(4):505-517. (PMID: 35469013)
Nat Biotechnol. 2019 Jul;37(7):773-782. (PMID: 31061481)
BMC Bioinformatics. 2013 Mar 07;14:89. (PMID: 23497278)
Elife. 2017 Nov 13;6:. (PMID: 29130882)
Proc Natl Acad Sci U S A. 2015 Dec 22;112(51):15672-7. (PMID: 26644564)
Bioinformatics. 2019 Jul 15;35(14):i436-i445. (PMID: 31510660)
Nat Commun. 2019 May 17;10(1):2209. (PMID: 31101809)
J Vis Exp. 2010 Jul 10;(41):. (PMID: 20644514)
Epigenetics. 2013 Mar;8(3):290-302. (PMID: 23426267)
Bioinformatics. 2013 Sep 1;29(17):2211-2. (PMID: 23825367)
J Stat Softw. 2010;33(1):1-22. (PMID: 20808728)
Cell. 2015 May 21;161(5):1202-1214. (PMID: 26000488)
PLoS One. 2011;6(11):e27156. (PMID: 22110609)
Infect Genet Evol. 2012 Jul;12(5):913-21. (PMID: 21930246)
Cell Syst. 2016 Oct 26;3(4):346-360.e4. (PMID: 27667365)
Mol Syst Biol. 2014 Feb 28;10:720. (PMID: 24586061)
Nat Methods. 2017 Feb 28;14(3):216-217. (PMID: 28245219)
Nat Methods. 2010 Apr;7(4):287-9. (PMID: 20208531)
PLoS One. 2012;7(7):e41361. (PMID: 22848472)
Nat Methods. 2015 May;12(5):453-7. (PMID: 25822800)
BMC Bioinformatics. 2010 Jan 14;11:27. (PMID: 20070912)
Cell. 2016 Oct 6;167(2):566-580.e19. (PMID: 27716510)
Genome Res. 2020 Feb;30(2):195-204. (PMID: 31992614)
Nat Neurosci. 2012 Jan 22;15(3):487-95, S1-2. (PMID: 22267162)
Nat Commun. 2020 Apr 24;11(1):1971. (PMID: 32332754)
Cell. 2016 Aug 25;166(5):1308-1323.e30. (PMID: 27565351)
BMC Bioinformatics. 2017 Feb 13;18(1):105. (PMID: 28193155)
Nat Commun. 2019 Jan 22;10(1):380. (PMID: 30670690)
Neuroepidemiology. 2005;25(4):163-75. (PMID: 16103727)
Curr Opin Immunol. 2013 Oct;25(5):571-8. (PMID: 24148234)
PLoS One. 2018 Mar 1;13(3):e0193067. (PMID: 29494600)
PLoS Comput Biol. 2019 May 6;15(5):e1006976. (PMID: 31059559)
Nat Commun. 2019 Jul 5;10(1):2975. (PMID: 31278265)
Bioinformatics. 2017 Apr 15;33(8):1179-1186. (PMID: 28088763)
Nat Rev Cancer. 2012 Mar 15;12(4):298-306. (PMID: 22419253)
Nat Commun. 2021 Oct 20;12(1):6106. (PMID: 34671028)
Proc Natl Acad Sci U S A. 2015 Jun 9;112(23):7285-90. (PMID: 26060301)
Nat Rev Genet. 2016 Jul 4;17(8):441-58. (PMID: 27376489)
Eur J Haematol. 1994 May;52(5):267-75. (PMID: 8020626)
Genome Biol. 2019 Sep 4;20(1):190. (PMID: 31484546)
Cell Syst. 2016 Oct 26;3(4):385-394.e3. (PMID: 27693023)
Nat Genet. 2013 Oct;45(10):1113-20. (PMID: 24071849)
Brief Bioinform. 2021 Jan 18;22(1):416-427. (PMID: 31925417)
Nat Commun. 2018 Nov 29;9(1):5068. (PMID: 30498206)
Nat Commun. 2020 Nov 6;11(1):5650. (PMID: 33159064)
Bioinformatics. 2003 Jan 22;19(2):185-93. (PMID: 12538238)
PLoS Comput Biol. 2019 Dec 2;15(12):e1007510. (PMID: 31790389)
Cell Metab. 2016 Oct 11;24(4):593-607. (PMID: 27667667)
Nucleic Acids Res. 2021 May 7;49(8):e48. (PMID: 33524140)
Bioinformatics. 2013 Apr 15;29(8):1083-5. (PMID: 23428642)
Blood. 2016 Aug 25;128(8):e20-31. (PMID: 27365425)
معلومات مُعتمدة: 2021ZD0112600 National Science and Technology Major Project; 61803320 National Natural Science Foundation of China; 2022J05012 Natural Science Foundation of Fujian Province of China; Scip20240104 Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, China
فهرسة مساهمة: Keywords: Cell type abundance; Cell type-specific gene expression profile; Deconvolution; Similarity matrix; Single-cell RNA-seq data
تواريخ الأحداث: Date Created: 20240918 Date Completed: 20240919 Latest Revision: 20240921
رمز التحديث: 20240921
مُعرف محوري في PubMed: PMC11409548
DOI: 10.1186/s12864-024-10728-x
PMID: 39294558
قاعدة البيانات: MEDLINE
الوصف
تدمد:1471-2164
DOI:10.1186/s12864-024-10728-x