Multi-block analysis method for Lipidomics data set
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Data modelling methods that are capable of maintaining block structure, as for example the block structure of the blocks of lipid classes, are called multi-block methods e.g. Consensus Principal Component Analysis (CPCA) and Multi-block Partial Least Squares Regression (MBPLSR). CPCA and MBPLR are two large families of MB methods. The developed methods can still be transferred to other data analysis methods. CPCA and MBPLSR which are extensions of PCA and PLSR to multi-block data sets can be employed for such integration of systems biology data Consensus PCA (CPCA) and MultiblockPLSR (MBPLSR) are two exploratory chemometrics approaches that are capable of modeling multi-block data sets. These methods, which are based on latent variables, aim at detecting a common underlying pattern between different data matrices and revealing the contribution of every individual block to the detected pattern. CPCA and MBPLSR can therefore be adapted for the integration of multi-block –omics data sets.


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Created: 20th Feb 2019 at 12:59

Last updated: 20th Aug 2021 at 12:22

Last used: 12th Aug 2022 at 20:12

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Version 1 Created 20th Feb 2019 at 12:59 by Sahar Hassani

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