Sparse Proteomics Analysis – a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data

BMC Bioinformatics

Research Article

Alexander Leichtle¹  Jan Vybiral²  Nada Cvetkovic³  Niklas Wulkow³  Tim O. F. Conrad⁴  Christof Schütte⁴  Martin Genzel⁵  Gitta Kutyniok⁵ 

[1]Center of Laboratory Medicine, Inselspital - Bern University Hospital, Düsternbrooker Weg 20, 24105, Bern, Switzerland

[2]Department of Mathematical Analysis, Charles University, Düsternbrooker Weg 20, Prague, Czech Republic

[3]Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany

[4]Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany

[5]Zuse Institute Berlin, Takustr. 7, Berlin, Germany

[6]Department of Mathematics, Technische Universität Berlin, Düsternbrooker Weg 20, Berlin, Germany

关键词: Machine learning;    Feature selection;    Classification;    Compressed sensing;    Sparsity;    Proteomics;    Mass spectrometry;    Clinical data;    Biomarker;   

DOI  :  10.1186/s12859-017-1565-4

 received in 2016-08-27, accepted in 2017-02-24,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundHigh-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible.ResultsWe present a new algorithm, Sparse Proteomics Analysis (SPA), based on the theory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. We show (1) how our method performs on artificial and real-world data-sets, (2) that its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data, and (3) that it is robust against random and systematic noise. We further demonstrate the applicability of our algorithm to two previously published clinical data-sets.

【 授权许可】

CC BY   
© The Author(s) 2017

【 图 表 】

Fig. 7

Fig. 3

Fig. 2

12951_2015_155_Article_IEq74.gif

Fig. 2

Fig. 8

Fig. 5

Fig. 5

Fig. 1

Fig. 2

Fig. 4

Fig. 1

Fig. 2

12951_2016_246_Article_IEq1.gif

Fig. 1

Fig. 6

Fig. 7

12951_2015_155_Article_IEq73.gif

Fig. 6

12951_2015_155_Article_IEq72.gif

Fig. 3

Fig. 3

Fig. 1

Fig. 2

Fig. 2

Fig. 1

Fig. 2

Fig. 2

Fig. 4

Fig. 1

Fig. 3

12947_2017_100_Article_IEq1.gif

Fig. 1

Fig. 1

Fig. 3

Fig. 10