TY - JOUR
T1 - Rearrangement of incomplete multi-omics datasets combined with ComDim for evaluating replicate cross-platform variability and batch influence
AU - Puig-Castellví, Francesc
AU - Jouan-Rimbaud Bouveresse, Delphine
AU - Mazéas, Laurent
AU - Chapleur, Olivier
AU - Rutledge, Douglas N.
N1 - Funding Information:
This work is part of the DIGESTOMIC project funded by the French National Research Agency (ANR-16-CE05-0014).
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/11/15
Y1 - 2021/11/15
N2 - Multi-omics studies can highlight the interrelationships among data across different layers of biological information. However, methods for the unsupervised analysis of multi-block data do not take the individual variability across batches into account and cannot deal with omics datasets when they present different numbers of replicates. We have explored three different data arrangement strategies to tackle these limitations. Several multi-block methods can be used to decipher the common variations across blocks and to determine the contribution of each block to each common component. In this study the ComDim method was used to compare these rearrangement strategies for three multi-omics datasets. We found that arranging the data using the ‘replicate by blocks’ strategy, where each block comprises data from only one replicate independently of its data type, provided the most insightful results. ComDim allowed the evaluation of the variability across the replicate blocks, confirming the existence of batch effects in some of the studies. Moreover, since the contributions of these batch effects were separated from the other contributions, the coordinated biological responses common across the different blocks was characterized for each data type.
AB - Multi-omics studies can highlight the interrelationships among data across different layers of biological information. However, methods for the unsupervised analysis of multi-block data do not take the individual variability across batches into account and cannot deal with omics datasets when they present different numbers of replicates. We have explored three different data arrangement strategies to tackle these limitations. Several multi-block methods can be used to decipher the common variations across blocks and to determine the contribution of each block to each common component. In this study the ComDim method was used to compare these rearrangement strategies for three multi-omics datasets. We found that arranging the data using the ‘replicate by blocks’ strategy, where each block comprises data from only one replicate independently of its data type, provided the most insightful results. ComDim allowed the evaluation of the variability across the replicate blocks, confirming the existence of batch effects in some of the studies. Moreover, since the contributions of these batch effects were separated from the other contributions, the coordinated biological responses common across the different blocks was characterized for each data type.
KW - Exploratory methods
KW - Incomplete data
KW - Metabonomics
KW - Multi-block data analysis
KW - Multi-omics analyses
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U2 - 10.1016/j.chemolab.2021.104422
DO - 10.1016/j.chemolab.2021.104422
M3 - Article
AN - SCOPUS:85115886733
VL - 218
SP - 1
EP - 11
JO - Chemometrics and Intelligent Laboratory Systems
JF - Chemometrics and Intelligent Laboratory Systems
SN - 0169-7439
M1 - 104422
ER -