Integration Strategies of Cross-Platform Microarray Data Sets in Multiclass Classification Problem

Sebastian Student; Alicja Płuciennik; Krzysztof Łakomiec; Agata Wilk; Wojciech Bensz; Krzysztof Fujarewicz

doi:10.1007/978-3-030-24308-1_48

Integration Strategies of Cross-Platform Microarray Data Sets in Multiclass Classification Problem

Sebastian Student
, Alicja Płuciennik
, Krzysztof Łakomiec
, Agata Wilk
, Wojciech Bensz
, Krzysztof Fujarewicz

Department of Systems Biology and Engineering (RAU/RAU1)

Silesian University of Technology
WASKO S.A.

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

1 Citation (Scopus)

Abstract

Despite the increasing amount of available gene expression data, integrative analysis is still hindered by its high susceptibility to microenvironment fluctuations, resulting in inter-experiment variability known as batch effects. Therefore the development of data integration strategy is now more necessary than ever. Although several normalization algorithms have already been proposed, we believe that an effective model must rely on data migration between schemes. In this paper we apply this approach to a set of microarray data from core needle biopsy of breast cancers spanning different microarray platforms, and demonstrate its effectiveness in data preparation for unsupervised analysis and multiclass classification tasks. We propose a custom tool dedicated to defining the model structure. Additionally, we compare several pipelines of data processing, combining data normalization with different batch effect correction methods.

Original language	English
Title of host publication	Computational Science and Its Applications – ICCSA 2019 - 19th International Conference, 2019, Proceedings
Editors	Sanjay Misra, Osvaldo Gervasi, Beniamino Murgante, Elena Stankova, Vladimir Korkhov, Carmelo Torre, Eufemia Tarantino, Ana Maria A.C. Rocha, David Taniar, Bernady O. Apduhan
Publisher	Springer Verlag
Pages	602-612
Number of pages	11
ISBN (Print)	9783030243074
DOIs	https://doi.org/10.1007/978-3-030-24308-1_48
Publication status	Published - 2019
Event	19th International Conference on Computational Science and Its Applications, ICCSA 2019 - Saint Petersburg, Russian Federation Duration: 1 Jul 2019 → 4 Jul 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11623 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Computational Science and Its Applications, ICCSA 2019
Country/Territory	Russian Federation
City	Saint Petersburg
Period	1/07/19 → 4/07/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Batch effect
Data integration
Multiclass classification

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-24308-1_48

Cite this

Student, S., Płuciennik, A., Łakomiec, K., Wilk, A., Bensz, W., & Fujarewicz, K. (2019). Integration Strategies of Cross-Platform Microarray Data Sets in Multiclass Classification Problem. In S. Misra, O. Gervasi, B. Murgante, E. Stankova, V. Korkhov, C. Torre, E. Tarantino, A. M. A. C. Rocha, D. Taniar, & B. O. Apduhan (Eds.), Computational Science and Its Applications – ICCSA 2019 - 19th International Conference, 2019, Proceedings (pp. 602-612). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11623 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-24308-1_48

Student, Sebastian ; Płuciennik, Alicja ; Łakomiec, Krzysztof et al. / Integration Strategies of Cross-Platform Microarray Data Sets in Multiclass Classification Problem. Computational Science and Its Applications – ICCSA 2019 - 19th International Conference, 2019, Proceedings. editor / Sanjay Misra ; Osvaldo Gervasi ; Beniamino Murgante ; Elena Stankova ; Vladimir Korkhov ; Carmelo Torre ; Eufemia Tarantino ; Ana Maria A.C. Rocha ; David Taniar ; Bernady O. Apduhan. Springer Verlag, 2019. pp. 602-612 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{4263309aaabb418ab5b6ac8a4f9d7e15,

title = "Integration Strategies of Cross-Platform Microarray Data Sets in Multiclass Classification Problem",

abstract = "Despite the increasing amount of available gene expression data, integrative analysis is still hindered by its high susceptibility to microenvironment fluctuations, resulting in inter-experiment variability known as batch effects. Therefore the development of data integration strategy is now more necessary than ever. Although several normalization algorithms have already been proposed, we believe that an effective model must rely on data migration between schemes. In this paper we apply this approach to a set of microarray data from core needle biopsy of breast cancers spanning different microarray platforms, and demonstrate its effectiveness in data preparation for unsupervised analysis and multiclass classification tasks. We propose a custom tool dedicated to defining the model structure. Additionally, we compare several pipelines of data processing, combining data normalization with different batch effect correction methods.",

keywords = "Batch effect, Data integration, Multiclass classification",

author = "Sebastian Student and Alicja P{\l}uciennik and Krzysztof {\L}akomiec and Agata Wilk and Wojciech Bensz and Krzysztof Fujarewicz",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 19th International Conference on Computational Science and Its Applications, ICCSA 2019 ; Conference date: 01-07-2019 Through 04-07-2019",

year = "2019",

doi = "10.1007/978-3-030-24308-1\_48",

language = "English",

isbn = "9783030243074",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "602--612",

editor = "Sanjay Misra and Osvaldo Gervasi and Beniamino Murgante and Elena Stankova and Vladimir Korkhov and Carmelo Torre and Eufemia Tarantino and Rocha, \{Ana Maria A.C.\} and David Taniar and Apduhan, \{Bernady O.\}",

booktitle = "Computational Science and Its Applications {\textendash} ICCSA 2019 - 19th International Conference, 2019, Proceedings",

address = "Germany",

}

Student, S, Płuciennik, A, Łakomiec, K , Wilk, A, Bensz, W & Fujarewicz, K 2019, Integration Strategies of Cross-Platform Microarray Data Sets in Multiclass Classification Problem. in S Misra, O Gervasi, B Murgante, E Stankova, V Korkhov, C Torre, E Tarantino, AMAC Rocha, D Taniar & BO Apduhan (eds), Computational Science and Its Applications – ICCSA 2019 - 19th International Conference, 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11623 LNCS, Springer Verlag, pp. 602-612, 19th International Conference on Computational Science and Its Applications, ICCSA 2019, Saint Petersburg, Russian Federation, 1/07/19. https://doi.org/10.1007/978-3-030-24308-1_48

Integration Strategies of Cross-Platform Microarray Data Sets in Multiclass Classification Problem. / Student, Sebastian; Płuciennik, Alicja; Łakomiec, Krzysztof et al.
Computational Science and Its Applications – ICCSA 2019 - 19th International Conference, 2019, Proceedings. ed. / Sanjay Misra; Osvaldo Gervasi; Beniamino Murgante; Elena Stankova; Vladimir Korkhov; Carmelo Torre; Eufemia Tarantino; Ana Maria A.C. Rocha; David Taniar; Bernady O. Apduhan. Springer Verlag, 2019. p. 602-612 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11623 LNCS).

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

TY - GEN

T1 - Integration Strategies of Cross-Platform Microarray Data Sets in Multiclass Classification Problem

AU - Student, Sebastian

AU - Płuciennik, Alicja

AU - Łakomiec, Krzysztof

AU - Wilk, Agata

AU - Bensz, Wojciech

AU - Fujarewicz, Krzysztof

PY - 2019

Y1 - 2019

N2 - Despite the increasing amount of available gene expression data, integrative analysis is still hindered by its high susceptibility to microenvironment fluctuations, resulting in inter-experiment variability known as batch effects. Therefore the development of data integration strategy is now more necessary than ever. Although several normalization algorithms have already been proposed, we believe that an effective model must rely on data migration between schemes. In this paper we apply this approach to a set of microarray data from core needle biopsy of breast cancers spanning different microarray platforms, and demonstrate its effectiveness in data preparation for unsupervised analysis and multiclass classification tasks. We propose a custom tool dedicated to defining the model structure. Additionally, we compare several pipelines of data processing, combining data normalization with different batch effect correction methods.

AB - Despite the increasing amount of available gene expression data, integrative analysis is still hindered by its high susceptibility to microenvironment fluctuations, resulting in inter-experiment variability known as batch effects. Therefore the development of data integration strategy is now more necessary than ever. Although several normalization algorithms have already been proposed, we believe that an effective model must rely on data migration between schemes. In this paper we apply this approach to a set of microarray data from core needle biopsy of breast cancers spanning different microarray platforms, and demonstrate its effectiveness in data preparation for unsupervised analysis and multiclass classification tasks. We propose a custom tool dedicated to defining the model structure. Additionally, we compare several pipelines of data processing, combining data normalization with different batch effect correction methods.

KW - Batch effect

KW - Data integration

KW - Multiclass classification

UR - https://www.scopus.com/pages/publications/85068985645

U2 - 10.1007/978-3-030-24308-1_48

DO - 10.1007/978-3-030-24308-1_48

M3 - Conference contribution

AN - SCOPUS:85068985645

SN - 9783030243074

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 602

EP - 612

BT - Computational Science and Its Applications – ICCSA 2019 - 19th International Conference, 2019, Proceedings

A2 - Misra, Sanjay

A2 - Gervasi, Osvaldo

A2 - Murgante, Beniamino

A2 - Stankova, Elena

A2 - Korkhov, Vladimir

A2 - Torre, Carmelo

A2 - Tarantino, Eufemia

A2 - Rocha, Ana Maria A.C.

A2 - Taniar, David

A2 - Apduhan, Bernady O.

PB - Springer Verlag

T2 - 19th International Conference on Computational Science and Its Applications, ICCSA 2019

Y2 - 1 July 2019 through 4 July 2019

ER -

Student S, Płuciennik A, Łakomiec K , Wilk A, Bensz W, Fujarewicz K. Integration Strategies of Cross-Platform Microarray Data Sets in Multiclass Classification Problem. In Misra S, Gervasi O, Murgante B, Stankova E, Korkhov V, Torre C, Tarantino E, Rocha AMAC, Taniar D, Apduhan BO, editors, Computational Science and Its Applications – ICCSA 2019 - 19th International Conference, 2019, Proceedings. Springer Verlag. 2019. p. 602-612. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-24308-1_48

Integration Strategies of Cross-Platform Microarray Data Sets in Multiclass Classification Problem

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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