Normalization regarding non-random missing values in high-throughput mass spectrometry data

Pei Wang; Hua Tang; Heidi Zhang; Jeffrey Whiteaker; Amanda G. Paulovich; Martin Mcintosh

Normalization regarding non-random missing values in high-throughput mass spectrometry data

Pei Wang, Hua Tang, Heidi Zhang, Jeffrey Whiteaker, Amanda G. Paulovich, Martin Mcintosh

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

61 Scopus citations

Abstract

We propose a two-step normalization procedure for high-throughput mass spectrometry (MS) data, which is a necessary step in biomarker clustering or classification. First, a global normalization step is used to remove sources of systematic variation between MS profiles due to, for instance, varying amounts of sample degradation over time. A probability model is then used to investigate the intensity-dependent missing events and provides possible substitutions for the missing values. We illustrate the performance of the method with a LC-MS data set of synthetic protein mixtures.

Original language	English
Title of host publication	Proceedings of the Pacific Symposium on Biocomputing 2006, PSB 2006
Pages	315-326
Number of pages	12
State	Published - 2006
Externally published	Yes
Event	11th Pacific Symposium on Biocomputing 2006, PSB 2006 - Maui, HI, United States Duration: 3 Jan 2006 → 7 Jan 2006

Publication series

Name	Proceedings of the Pacific Symposium on Biocomputing 2006, PSB 2006

Conference

Conference	11th Pacific Symposium on Biocomputing 2006, PSB 2006
Country/Territory	United States
City	Maui, HI
Period	3/01/06 → 7/01/06

Cite this

@inproceedings{9a318e297f24430e80052498730ff796,

title = "Normalization regarding non-random missing values in high-throughput mass spectrometry data",

abstract = "We propose a two-step normalization procedure for high-throughput mass spectrometry (MS) data, which is a necessary step in biomarker clustering or classification. First, a global normalization step is used to remove sources of systematic variation between MS profiles due to, for instance, varying amounts of sample degradation over time. A probability model is then used to investigate the intensity-dependent missing events and provides possible substitutions for the missing values. We illustrate the performance of the method with a LC-MS data set of synthetic protein mixtures.",

author = "Pei Wang and Hua Tang and Heidi Zhang and Jeffrey Whiteaker and Paulovich, {Amanda G.} and Martin Mcintosh",

year = "2006",

language = "English",

isbn = "9812564632",

series = "Proceedings of the Pacific Symposium on Biocomputing 2006, PSB 2006",

pages = "315--326",

booktitle = "Proceedings of the Pacific Symposium on Biocomputing 2006, PSB 2006",

note = "11th Pacific Symposium on Biocomputing 2006, PSB 2006 ; Conference date: 03-01-2006 Through 07-01-2006",

}

Wang, P, Tang, H, Zhang, H, Whiteaker, J, Paulovich, AG & Mcintosh, M 2006, Normalization regarding non-random missing values in high-throughput mass spectrometry data. in Proceedings of the Pacific Symposium on Biocomputing 2006, PSB 2006. Proceedings of the Pacific Symposium on Biocomputing 2006, PSB 2006, pp. 315-326, 11th Pacific Symposium on Biocomputing 2006, PSB 2006, Maui, HI, United States, 3/01/06.

Normalization regarding non-random missing values in high-throughput mass spectrometry data. / Wang, Pei; Tang, Hua; Zhang, Heidi et al.
Proceedings of the Pacific Symposium on Biocomputing 2006, PSB 2006. 2006. p. 315-326 (Proceedings of the Pacific Symposium on Biocomputing 2006, PSB 2006).

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

TY - GEN

T1 - Normalization regarding non-random missing values in high-throughput mass spectrometry data

AU - Wang, Pei

AU - Tang, Hua

AU - Zhang, Heidi

AU - Whiteaker, Jeffrey

AU - Paulovich, Amanda G.

AU - Mcintosh, Martin

PY - 2006

Y1 - 2006

N2 - We propose a two-step normalization procedure for high-throughput mass spectrometry (MS) data, which is a necessary step in biomarker clustering or classification. First, a global normalization step is used to remove sources of systematic variation between MS profiles due to, for instance, varying amounts of sample degradation over time. A probability model is then used to investigate the intensity-dependent missing events and provides possible substitutions for the missing values. We illustrate the performance of the method with a LC-MS data set of synthetic protein mixtures.

AB - We propose a two-step normalization procedure for high-throughput mass spectrometry (MS) data, which is a necessary step in biomarker clustering or classification. First, a global normalization step is used to remove sources of systematic variation between MS profiles due to, for instance, varying amounts of sample degradation over time. A probability model is then used to investigate the intensity-dependent missing events and provides possible substitutions for the missing values. We illustrate the performance of the method with a LC-MS data set of synthetic protein mixtures.

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M3 - Conference contribution

C2 - 17094249

AN - SCOPUS:33747838164

SN - 9812564632

SN - 9789812564634

T3 - Proceedings of the Pacific Symposium on Biocomputing 2006, PSB 2006

SP - 315

EP - 326

BT - Proceedings of the Pacific Symposium on Biocomputing 2006, PSB 2006

T2 - 11th Pacific Symposium on Biocomputing 2006, PSB 2006

Y2 - 3 January 2006 through 7 January 2006

ER -

Normalization regarding non-random missing values in high-throughput mass spectrometry data

Abstract

Publication series

Conference

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