Common and distinct variation in data fusion of designed experimental data.

ABSTRACT:

Introduction

Integrative analysis of multiple data sets can provide complementary information about the studied biological system. However, data fusion of multiple biological data sets can be complicated as data sets might contain different sources of variation due to underlying experimental factors. Therefore, taking the experimental design of data sets into account could be of importance in data fusion concept.

Objectives

In the present work, we aim to incorporate the experimental design information in the integrative analysis of multiple designed data sets.

Methods

Here we describe penalized exponential ANOVA simultaneous component analysis (PE-ASCA), a new method for integrative analysis of data sets from multiple compartments or analytical platforms with the same

SUBMITTER: Alinaghi M 

PROVIDER: S-EPMC6890597 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature