ICA order selection based on consistency: Application to genotype data

Jiayu Chen; Vince D. Calhoun; Jingyu Liu

doi:10.1109/EMBC.2012.6345943

ICA order selection based on consistency: Application to genotype data

Jiayu Chen, Vince D. Calhoun, Jingyu Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

16 Scopus citations

Abstract

Independent component analysis (ICA), a blind source separation method, has been shown to be a useful approach to identify genetic components representing combined effects from multiple mutations. However, the ICA order selection for genotype data has been a challenge, since a genetic component usually accounts for a small amount of variance of the data, and makes it difficult to distinguish true signals from background. To address this issue, we propose to select ICA order based on consistency and implement three strategies in this study. Simulations demonstrate robust performances of all three strategies where the selected orders lead to optimal results regardless of ICA performances.

Original language	English (US)
Title of host publication	2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
Pages	360-363
Number of pages	4
DOIs	https://doi.org/10.1109/EMBC.2012.6345943
State	Published - 2012
Externally published	Yes
Event	34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States Duration: Aug 28 2012 → Sep 1 2012

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Other

Other	34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Country/Territory	United States
City	San Diego, CA
Period	8/28/12 → 9/1/12

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC.2012.6345943

Cite this

Chen, J., Calhoun, V. D., & Liu, J. (2012). ICA order selection based on consistency: Application to genotype data. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012 (pp. 360-363). Article 6345943 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/EMBC.2012.6345943

ICA order selection based on consistency: Application to genotype data. / Chen, Jiayu; Calhoun, Vince D.; Liu, Jingyu.
2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012. 2012. p. 360-363 6345943 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, J, Calhoun, VD & Liu, J 2012, ICA order selection based on consistency: Application to genotype data. in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012., 6345943, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 360-363, 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012, San Diego, CA, United States, 8/28/12. https://doi.org/10.1109/EMBC.2012.6345943

Chen J, Calhoun VD, Liu J. ICA order selection based on consistency: Application to genotype data. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012. 2012. p. 360-363. 6345943. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2012.6345943

@inproceedings{00a1935dee6040daaeeb37732525b002,

title = "ICA order selection based on consistency: Application to genotype data",

abstract = "Independent component analysis (ICA), a blind source separation method, has been shown to be a useful approach to identify genetic components representing combined effects from multiple mutations. However, the ICA order selection for genotype data has been a challenge, since a genetic component usually accounts for a small amount of variance of the data, and makes it difficult to distinguish true signals from background. To address this issue, we propose to select ICA order based on consistency and implement three strategies in this study. Simulations demonstrate robust performances of all three strategies where the selected orders lead to optimal results regardless of ICA performances.",

author = "Jiayu Chen and Calhoun, {Vince D.} and Jingyu Liu",

year = "2012",

doi = "10.1109/EMBC.2012.6345943",

language = "English (US)",

isbn = "9781424441198",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

pages = "360--363",

booktitle = "2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012",

note = "34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 ; Conference date: 28-08-2012 Through 01-09-2012",

}

TY - GEN

T1 - ICA order selection based on consistency

T2 - 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012

AU - Chen, Jiayu

AU - Calhoun, Vince D.

AU - Liu, Jingyu

PY - 2012

Y1 - 2012

N2 - Independent component analysis (ICA), a blind source separation method, has been shown to be a useful approach to identify genetic components representing combined effects from multiple mutations. However, the ICA order selection for genotype data has been a challenge, since a genetic component usually accounts for a small amount of variance of the data, and makes it difficult to distinguish true signals from background. To address this issue, we propose to select ICA order based on consistency and implement three strategies in this study. Simulations demonstrate robust performances of all three strategies where the selected orders lead to optimal results regardless of ICA performances.

AB - Independent component analysis (ICA), a blind source separation method, has been shown to be a useful approach to identify genetic components representing combined effects from multiple mutations. However, the ICA order selection for genotype data has been a challenge, since a genetic component usually accounts for a small amount of variance of the data, and makes it difficult to distinguish true signals from background. To address this issue, we propose to select ICA order based on consistency and implement three strategies in this study. Simulations demonstrate robust performances of all three strategies where the selected orders lead to optimal results regardless of ICA performances.

UR - http://www.scopus.com/inward/record.url?scp=84881056973&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84881056973&partnerID=8YFLogxK

U2 - 10.1109/EMBC.2012.6345943

DO - 10.1109/EMBC.2012.6345943

M3 - Conference contribution

C2 - 23365904

AN - SCOPUS:84881056973

SN - 9781424441198

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 360

EP - 363

BT - 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012

Y2 - 28 August 2012 through 1 September 2012

ER -

ICA order selection based on consistency: Application to genotype data

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this