README.md
In f-puig/R.ComDim: ComDim analysis under R environment

ComDim

ComDim (also known as CCSWA) is an unsupervised multi-block method that aims to simultaneously consider multiple data tables to find the latent components that are common to all the tables as well as those that are specific to each data table, along with the contribution of each of the tables to each of these components. ComDim determines a common space describing the dispersion of the samples in all the blocks, each block having a specific weight (salience) associated with each dimension in this common space. Significant differences in the saliences for a given dimension reflect the fact that the dimension contains different amounts of information coming from each block. In addition to the saliences, Local loadings for each analyzed block and two different sets of scores are obtained. The first set corresponds to the Local scores for each analyzed block while the second set is composed of the Global scores, common to all the blocks.

To analyze different types of data (ex. multi-omics) and see how they are untangled.
To extract the common profiles of related variables (ex. metabolites detected in the same pathway).
To deal with unbalanced multi-block datasets (ex. different number of sample replicates in the blocks). However, ComDim can also deal with balanced multi-block datasets.
Within the data from the same analytical platform, to evaluate inter-sample variability and batch effects related to the analytical platform.
To investigate cross-platform variability, which is useful to detect errors in the sample preparation.

To successfully extract all the potential of the ComDim method, several functions coded in R are proposed. Some of them are listed below: MultiBlock(): To initialize a MultiBlock object with the first data-block(s). BuildMultiBlock(): To combine several single data-blocks into a MultiBlock object, containing some of them metadata information. ExpandMultiBlock(): To combine several single data-blocks into a MultiBlock object, containing some of them metadata information. NormalizeMultiBlock(): To normalize (some or all) the data-blocks of the MultiBlock object. ProcessMultiBlock(): To apply customized data transformation to (some or all) the data-blocks of the MultiBlock object. ComDim_PCA_MB(): This function applies the ComDim-PCA algorithm on the MultiBlock object resulting from BuildMultiBlock() or ExpandMultiBlock().

For more information on the usage of these functions, please consult the tutorial from the docs folder.

```{r install, echo = TRUE} if (!require("devtools")) install.packages("devtools") library("devtools") install_github("f-puig/R.ComDim")

# Load R.ComDim library("R.ComDim") ```

Puig-Castellví, F.; Jouan-Rimbaud Bouveresse, D.; Mazéas, L.; Chapleur, O.; Rutledge, D. N. Rearrangement of incomplete multi-omics datasets combined with ComDim for evaluating replicate cross-platform variability and batch influence. Chemom. Intell. Lab. Syst. 2021, 18 (104422). https://doi.org/10.1016/j.chemolab.2021.104422
Qannari, E. M.; Courcoux, P.; Vigneau, E. Common Components and Specific Weights Analysis Performed on Preference Data. Food Qual. Prefer. 2001, 12 (5–7), 365–368. https://doi.org/10.1016/S0950-3293(01)00026-X.
Mazerolles, G.; Hanafi, M.; Dufour, E.; Bertrand, D.; Qannari, E. M. Common Components and Specific Weights Analysis: A Chemometric Method for Dealing with Complexity of Food Products. Chemom. Intell. Lab. Syst. 2006, 81 (1), 41–49. https://doi.org/10.1016/J.CHEMOLAB.2005.09.004.
Claeys-Bruno, M.; Béal, A.; Rutledge, D. N.; Sergent, M. Use of the Common Components and Specific Weights Analysis to Interpret Supersaturated Designs. Chemom. Intell. Lab. Syst. 2016, 152, 97–106. https://doi.org/10.1016/j.chemolab.2016.01.014.