TY  - JOUR
A1  - Schmidt, Florian
A1  - List, Markus
A1  - Cukuroglu, Engin
A1  - Köhler, Sebastian
A1  - Göke, Jonathan
A1  - Schulz, Marcel Holger
T1  - An ontology-based method for assessing batch effect adjustment approaches in heterogeneous datasets
T2  - Bioinformatics
N2  - Motivation: International consortia such as the Genotype-Tissue Expression (GTEx) project, The Cancer Genome Atlas (TCGA) or the International Human Epigenetics Consortium (IHEC) have produced a wealth of genomic datasets with the goal of advancing our understanding of cell differentiation and disease mechanisms. However, utilizing all of these data effectively through integrative analysis is hampered by batch effects, large cell type heterogeneity and low replicate numbers. To study if batch effects across datasets can be observed and adjusted for, we analyze RNA-seq data of 215 samples from ENCODE, Roadmap, BLUEPRINT and DEEP as well as 1336 samples from GTEx and TCGA. While batch effects are a considerable issue, it is non-trivial to determine if batch adjustment leads to an improvement in data quality, especially in cases of low replicate numbers.
Results: We present a novel method for assessing the performance of batch effect adjustment methods on heterogeneous data. Our method borrows information from the Cell Ontology to establish if batch adjustment leads to a better agreement between observed pairwise similarity and similarity of cell types inferred from the ontology. A comparison of state-of-the art batch effect adjustment methods suggests that batch effects in heterogeneous datasets with low replicate numbers cannot be adequately adjusted. Better methods need to be developed, which can be assessed objectively in the framework presented here.
Y1  - 2018
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/51697
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-516977
SN  - 1460-2059
SN  - 1367-4803
N1  - This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
VL  - 34
IS  - 17
SP  - i908
EP  - i916
PB  - Oxford Univ. Press
CY  - Oxford
ER  -