Dimension Reduction for the Integrative Analysis of Multilevel Omics Data

Gerhard Thallinger; Bettina Pucher; Natascha Fladischer; Oana Alina Zeleznik

Dimension Reduction for the Integrative Analysis of Multilevel Omics Data

Gerhard Thallinger, Bettina Pucher, Natascha Fladischer, Oana Alina Zeleznik

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

Abstract

Biological systems are increasingly studied at multiple omics levels simultaneously. Sequential analysis of the individual omics levels does not take the interconnected nature of the data into account. Integrative analysis of the multilevel data using dimension reduction techniques facilitate the identification of a larger number and more relevant features than sequential methods. We apply principal component-,
correspondence- and multiple co-inertia analysis to transcriptome and proteome data from 57 cancer cell lines and show that MCIA allows identification of a more complete set of relevant features and more accurate classification of the cancer types.

Original language	English
Title of host publication	Proceedings of SIAM ALA 2018
Publication status	Published - May 2018

Fields of Expertise

Human- & Biotechnology
Information, Communication & Computing

Treatment code (Nähere Zuordnung)

Basic - Fundamental (Grundlagenforschung)

UN SDGs

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

Cite this

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title = "Dimension Reduction for the Integrative Analysis of Multilevel Omics Data",

abstract = "Biological systems are increasingly studied at multiple omics levels simultaneously. Sequential analysis of the individual omics levels does not take the interconnected nature of the data into account. Integrative analysis of the multilevel data using dimension reduction techniques facilitate the identification of a larger number and more relevant features than sequential methods. We apply principal component-,correspondence- and multiple co-inertia analysis to transcriptome and proteome data from 57 cancer cell lines and show that MCIA allows identification of a more complete set of relevant features and more accurate classification of the cancer types.",

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AU - Fladischer, Natascha

AU - Zeleznik, Oana Alina

PY - 2018/5

Y1 - 2018/5

N2 - Biological systems are increasingly studied at multiple omics levels simultaneously. Sequential analysis of the individual omics levels does not take the interconnected nature of the data into account. Integrative analysis of the multilevel data using dimension reduction techniques facilitate the identification of a larger number and more relevant features than sequential methods. We apply principal component-,correspondence- and multiple co-inertia analysis to transcriptome and proteome data from 57 cancer cell lines and show that MCIA allows identification of a more complete set of relevant features and more accurate classification of the cancer types.

AB - Biological systems are increasingly studied at multiple omics levels simultaneously. Sequential analysis of the individual omics levels does not take the interconnected nature of the data into account. Integrative analysis of the multilevel data using dimension reduction techniques facilitate the identification of a larger number and more relevant features than sequential methods. We apply principal component-,correspondence- and multiple co-inertia analysis to transcriptome and proteome data from 57 cancer cell lines and show that MCIA allows identification of a more complete set of relevant features and more accurate classification of the cancer types.

M3 - Conference paper

BT - Proceedings of SIAM ALA 2018

ER -