User: etienne.thevenot

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Location:
France
Website:
http://etiennethevenot...
Last seen:
5 months, 3 weeks ago
Joined:
3 years, 5 months ago
Email:
e***************@cea.fr

I have a background of mathematics (Master) and molecular and cellular neurobiology (PhD). I have been working in the field of computational metabolomics since 2010 at CEA (French government-funded technological research organisation) within the MetaboHUB infrastructure. My research focuses on statistical approaches for biomarker discovery (Thevenot et al, 2015). To enable multivariate modeling and feature selection with OPLS, I wrote the ropls bioconductor package. I have integrated several tools for normalization and quality control of LC-HRMS data, as well as univariate and multivariate statistical analysis, into the Workflow4Metabolomics infrastructure for computational metabolomics.

Posts by etienne.thevenot

<prev • 7 results • page 1 of 1 • next >
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Answer: A: predict.opls() doesn't work when Y is a matrix
... Thanks for reporting this bug which has been fixed in the 1.13.2 version of ropls (dev). Etienne. ...
written 5 months ago by etienne.thevenot0
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Comment: C: HOW to define the "parLabVc" indices in ropls package.
... You cannot use symbols (but special characters work). A better option would be to use colors according to your classes (use a factor in the parColFcVn argument): > library(ropls) > data(sacurine) > pcaModel <- opls(sacurine[["dataMatrix"]]) > plot(pcaModel, typeVc = "x-score", parLab ...
written 7 months ago by etienne.thevenot0
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Answer: A: HOW to define the "parLabVc" indices in ropls package.
... Hi, Below is an example of labels ("s1", "s2", ..., "sN"), where N is the total number of samples. You can use any character vector of length N. Does it answer your question? Etienne. > library(ropls) > data(sacurine) > pcaModel <- opls(sacurine[["dataMatrix"]]) > plot(pcaModel, p ...
written 7 months ago by etienne.thevenot0
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Answer: A: Error in ROPLS with LPS-DA
... Hi Abigail, I ran the script below on your data (PCA and PLS-DA) without any error. The modeling with PLS-DA does not appear significant (pQ2 = 0.1). In addition, since you have 1e3 times more variables than samples, the risk of overfitting is very high. Best wishes, Etienne. sunDF <- read.t ...
written 9 months ago by etienne.thevenot0
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Answer: A: Error in ROPLS with LPS-DA
... Hi Abigail, Did you check that there is any variable with constant value for all samples in your dataset? Otherwise, could you share your "sunoesy" data with me (etienne.thevenot@cea.fr)? Best wishes, Etienne. ...
written 9 months ago by etienne.thevenot0
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Answer: A: Data arrangement for PLSDA using the ropls R-package
... Hi Mike, In your dataset, the algorithm finds that the optimal number of component is 1. Hence, a score plot in two-dimensions is not returned. By setting the number of predictive components to 2 (with 'predI = 2'), you will force the algorithm to compute the second component and thus the score-pl ...
written 14 months ago by etienne.thevenot0
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Answer: A: problem with ropls library (OPLS-DA)
... Dear Dominik, By default, ropls automatically selects the optimal number of predictive (PLS) or orthogonal (OPLS) components. To do this, the algorithm checks if the addition of an additional component improves the predictions. Here the message indicates that even the first predictive component was ...
written 2.4 years ago by etienne.thevenot0

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