Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data.
Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data.
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DOI：
10.1038/s41598-021-84514-0
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发表时间：
2021-03-04
影响因子：
4.6
通讯作者：
Shen R
中科院分区：
文献类型：
--
作者：
Zhao Y;Chang C;Hannum M;Lee J;Shen R
在大块肿瘤或单细胞中的多模式分子谱数据正在快速积累。有一个很大的需要发展的统计和计算方法，以揭示复杂的数据类型的分子结构，对生物学的发现。在这里，我们介绍星云，一种新的贝叶斯综合聚类分析的高维多模态分子数据，以确定直接可解释的集群和相关的生物标志物在一个统一的和生物学上合理的框架。为了提高计算效率，变分贝叶斯方法被开发来近似联合后验分布，以实现在高维设置的模型推断。我们描述了一个泛癌症数据分析的基因组，表观基因组和转录组的变化，在近9000个肿瘤样本的典型致癌信号通路，免疫和干性表型，与国家的最先进的聚类方法进行比较。我们证明，星云具有独特的优势，揭示模式的基础上共享的途径改变，提供生物学和临床的见解超越肿瘤类型和组织学的泛癌症分析设置。我们还说明了星云在外周血样本中的免疫细胞分解的单细胞数据中的实用性。
Multi-modal molecular profiling data in bulk tumors or single cells are accumulating at a fast pace. There is a great need for developing statistical and computational methods to reveal molecular structures in complex data types toward biological discoveries. Here, we introduce Nebula, a novel Bayesian integrative clustering analysis for high dimensional multi-modal molecular data to identify directly interpretable clusters and associated biomarkers in a unified and biologically plausible framework. To facilitate computational efficiency, a variational Bayes approach is developed to approximate the joint posterior distribution to achieve model inference in high-dimensional settings. We describe a pan-cancer data analysis of genomic, epigenomic, and transcriptomic alterations in close to 9000 tumor samples across canonical oncogenic signaling pathways, immune and stemness phenotype, with comparisons to state-of-the-art clustering methods. We demonstrate that Nebula has the unique advantage of revealing patterns on the basis of shared pathway alterations, offering biological and clinical insights beyond tumor type and histology in the pan-cancer analysis setting. We also illustrate the utility of Nebula in single cell data for immune cell decomposition in peripheral blood samples.
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癌症基因组地图集中的致癌信号通路。
影响因子：
64.5
作者：
Sanchez-Vega F;Mina M;Armenia J;Chatila WK;Luna A;La KC;Dimitriadoy S;Liu DL;Kantheti HS;Saghafinia S;Chakravarty D;Daian F;Gao Q;Bailey MH;Liang WW;Foltz SM;Shmulevich I;Ding L;Heins Z;Ochoa A;Gross B;Gao J;Zhang H;Kundra R;Kandoth C;Bahceci I;Dervishi L;Dogrusoz U;Zhou W;Shen H;Laird PW;Way GP;Greene CS;Liang H;Xiao Y;Wang C;Iavarone A;Berger AH;Bivona TG;Lazar AJ;Hammer GD;Giordano T;Kwong LN;McArthur G;Huang C;Tward AD;Frederick MJ;McCormick F;Meyerson M;Cancer Genome Atlas Research Network;Van Allen EM;Cherniack AD;Ciriello G;Sander C;Schultz N
通讯作者：
Schultz N
影响因子：
64.5
作者：
Malta TM;Sokolov A;Gentles AJ;Burzykowski T;Poisson L;Weinstein JN;Kamińska B;Huelsken J;Omberg L;Gevaert O;Colaprico A;Czerwińska P;Mazurek S;Mishra L;Heyn H;Krasnitz A;Godwin AK;Lazar AJ;Cancer Genome Atlas Research Network;Stuart JM;Hoadley KA;Laird PW;Noushmehr H;Wiznerowicz M
通讯作者：
Wiznerowicz M
影响因子：
48
作者：
Angermueller C;Clark SJ;Lee HJ;Macaulay IC;Teng MJ;Hu TX;Krueger F;Smallwood S;Ponting CP;Voet T;Kelsey G;Stegle O;Reik W
通讯作者：
Reik W
影响因子：
5.8
作者：
Lanckriet, GRG;De Bie, T;Noble, WS
通讯作者：
Noble, WS
影响因子：
64.8
作者：
Curtis, Christina;Shah, Sohrab P.;Chin, Suet-Feung;Turashvili, Gulisa;Rueda, Oscar M.;Dunning, Mark J.;Speed, Doug;Lynch, Andy G.;Samarajiwa, Shamith;Yuan, Yinyin;Graef, Stefan;Ha, Gavin;Haffari, Gholamreza;Bashashati, Ali;Russell, Roslin;McKinney, Steven;Langerod, Anita;Green, Andrew;Provenzano, Elena;Wishart, Gordon;Pinder, Sarah;Watson, Peter;Markowetz, Florian;Murphy, Leigh;Ellis, Ian;Purushotham, Arnie;Borresen-Dale, Anne-Lise;Brenton, James D.;Tavare, Simon;Caldas, Carlos;Aparicio, Samuel
通讯作者：
Aparicio, Samuel