Commit a32031e6 authored by Samuel BERTRAND's avatar Samuel BERTRAND 🐉

Update README.md

parent 8de02d66
......@@ -140,3 +140,46 @@ Again two result tables a provided. One corresponding to the selected features c
The provided data corresponds to the VIP of both models to assess statistical significance (for more information please read https://bioconductor.org/packages/release/bioc/html/ropls.html). In addition, the p parameters for each variables as provided by ropls package is also reported as it allows to knows the change corresponds to an increase or decrease. Thus the p1+p2 values is important to select features induced in both OPLS-DA models.
The variables represented in pink in the SUS plot are those listed in the “result$featuresSelection” table. The parameters used for features selection can be modified within the function argument “lim.VIP”.
Multivariate data analysis using POChEMon
The data analysis strategy corresponds to the comparison of mixed metabolome samples with a model mixing both pure metabolomes. For more information, please read the following publication: Jansen, J.J., Blanchet, L., Buydens, L.M.C., Bertrand, S., Wolfender, J.-L., 2015. Projected Orthogonalized CHemical Encounter MONitoring (POCHEMON) for microbial interactions in co-culture. Metabolomics 11(4), 908-919, http://doi.org/10.1007/s11306-014-0748-5.
This data analysis is achieved as follow:
` result<-Coculture.analysis(data=data, SampleNames = rowheader, monocultureSamples = c("Fungi1","Fungi2"), cocultureSamples = "Fungi1VSFungi2", scaleC='standard', log10L=TRUE, Method = "Pochemon")`
The function provide various results as follow. First a graphical representations of the mixing model, with projection of the co-culture samples. Then, a second graph corresponds to the competition model with individual representation of each mixed metabolome sample.
<img src='https://gitlab.univ-nantes.fr/bertrand-s-1/pochermon/raw/master/Pictures/mixingmodel.png' />
<img src='https://gitlab.univ-nantes.fr/bertrand-s-1/pochermon/raw/master/Pictures/competition model.png' />
Again two result tables a provided. One corresponding to the selected features called “result$featuresSelection” and the results for all variables called “result$Pochemon”. Those tables are structures as follow:
<table>
<tr>
<td></td>
<th>var<sub>1</sub></th>
<th>var<sub>2</sub></th>
<th>...</th>
<th>var<sub>i</sub></th>
<th>...</th>
</tr>
<tr>
<th>SSRankProduct</th>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td></tr>
</table>
The provided data corresponds to the “SSRankProduct” value, which corresponds to the normalized SSRankProduct. Thus the SSRankProduct values is used to select features of interest and can be modified within the function argument “lim.Pochemon”.
<h2>Further use</h2>
Such Pochemon approach is also compatible with variance decomposition through ANOVA, as reported in Geurts, B.P., Neerincx, A.H., Bertrand, S., Leemans, M.A.A.P., Postma, G.J., Wolfender, J.L., Cristescu, S.M., Buydens, L.M.C., Jansen, J.J., 2017. Combining ANOVA-PCA with POCHEMON to analyse micro-organism development in a polymicrobial environment. Anal. Chim. Acta 963, 1-16, http://doi.org/ 10.1016/j.aca.2017.01.064
This package is compatible with other packages used for variance decomposition such as lmdme (http://bioconductor.org/packages/release/bioc/html/lmdme.html) with some adjustments.
<h2>Contact</h2>
For any comments and additions do not hesitate to contact me at Samuel.bertrand@univ-nantes.fr
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