Inferring gene regulatory relationships with a high-dimensional robust approach.
Inferring gene regulatory relationships with a high-dimensional robust approach.
复制标题
通过高维鲁棒方法推断基因调节关系。
DOI：
10.1002/gepi.22047
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发表时间：
2017-07
影响因子：
2.1
通讯作者：
Ma S
中科院分区：
文献类型：
--
作者：
Zang Y;Zhao Q;Zhang Q;Li Y;Zhang S;Ma S
Gene expression (GE) levels have important biological and clinical implications. They are regulated by copy number alterations (CNAs). Modeling the regulatory relationships between GEs and CNAs facilitates understanding disease biology and can also have value in translational medicine. The expression level of a gene can be regulated by its cis-acting as well as trans-acting CNAs, and the set of trans-acting CNAs is usually not known, which poses a high-dimensional selection and estimation problem. Most of the existing studies share a common limitation in that they cannot accommodate long-tailed distributions or contamination of GE data. In this study, we develop a high-dimensional robust regression approach to infer the regulatory relationships between GEs and CNAs. A high-dimensional regression model is used to accommodate the effects of both cis-acting and trans-acting CNAs. A DPD (density power divergence) loss function is used to accommodate long-tailed GE distributions and contamination. Penalization is adopted for regularized estimation and selection of relevant CNAs. The proposed approach is effectively realized using a coordinate descent algorithm. Simulation shows that it has competitive performance compared to the nonrobust benchmark and the robust LAD (least absolute deviation) approach. We analyze TCGA (The Cancer Genome Atlas) data on cutaneous melanoma and study GE-CNA regulations in the RAP (regulation of apoptosis) pathway, which further demonstrates satisfactory performance of the proposed approach.
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DOI：
10.1056/nejmoa1510764
发表时间：
2015-11-19
期刊：
The New England journal of medicine
影响因子：
--
作者：
Sparano JA;Gray RJ;Makower DF;Pritchard KI;Albain KS;Hayes DF;Geyer CE Jr;Dees EC;Perez EA;Olson JA Jr;Zujewski J;Lively T;Badve SS;Saphner TJ;Wagner LI;Whelan TJ;Ellis MJ;Paik S;Wood WC;Ravdin P;Keane MM;Gomez Moreno HL;Reddy PS;Goggins TF;Mayer IA;Brufsky AM;Toppmeyer DL;Kaklamani VG;Atkins JN;Berenberg JL;Sledge GW
通讯作者：
Sledge GW
影响因子：
5.8
作者：
Shi, Xingjie;Zhao, Qing;Ma, Shuangge
通讯作者：
Ma, Shuangge
影响因子：
8.8
作者：
Deng, MC;Eisen, HJ;Hunt, S
通讯作者：
Hunt, S
影响因子：
48
作者：
Marbach D;Lamparter D;Quon G;Kellis M;Kutalik Z;Bergmann S
通讯作者：
Bergmann S
影响因子：
0.9
作者：
Fujisawa, Hironori;Eguchi, Shinto
通讯作者：
Eguchi, Shinto