Tag: workshop

Nov 9-10 2017, Toulouse, FR

Some feedback from our participants to the question:  ‘What did you like most about that workshop?’ (Survey Monkey results)

  • Theoretical + practical courses, course materials are really great
  • Regular oral review of the take-home messages
  • The slides and Kim-Anh presentations: very pedagogical
  • The workshop provides the exact combination of theory and practical exercises I liked to have. The examples with R scripts are so organized that you can understand the thinking process behind the analysis.
  • Open atmosphere and good pace, with enough of theory to understand the core principles
  • Didactic speakers, not much mathematics and formulas, alternance of theory and practice, well prepared R scripts and documents
  • The use of these tools is straightforward
  • Both the lecturer created a very nice exchange with the group, making everyone comfortable in making questions and express doubts.
  • Clear- Concise- Adaptable- Very complete R scripts and pdf documents

——–

We will be running a classic 2-day mixOmics workshop in November, taught by Dr Kim-Anh Lê Cao and Sébastien Déjean and other mixOmics team contributors. The event and Dr Kim-Anh Lê Cao’s visit is sponsored by the visiting scientist program INP Toulouse.

The objective of the workshop is to introduce the fundamental concepts of multivariate dimension reduction methodologies. Those methods are particularly useful for data exploration and integration of large data sets, and especially in the context of systems biology, or in research areas where statistical data integration is required. Each methodology (one ‘omics, 2 and multiple ‘omics integration) that will be presented during the course will be applied on biological “omics” studies including transcriptomics, metabolomic, proteomics and microbiome data sets using the R package mixOmics

Date: 9-10 November 2017

Venue: salle E111, Batiment E, UMR-GenPhySE, INRA Toulouse Castanet Tolosan (map access coming soon)

Prerequisites: We expect the participants to a good working knowledge in R (e.g. handling data frames and perform basic calculations). Participants are requested to bring their own laptops, having installed the software RStudio and the R package mixOmics (instructions provided prior to the training).

Practical information: The workshop is free of charge for all participants as it is fully sponsored by INP. Priority will be given to INP students, external postgraduate students and early career researcher. The workshop includes tuition, course material. The workshop excludes tea/coffee and lunch during the breaks.

More details: see this flyer.

Register: registrations have now closed. 

Want to know more? contact us at mixomics [at] math.univ-toulouse.fr

Update 6.1.3 on CRAN, postdoc position, manuscript and upcoming workshops

Dear mixOmics users,

We have been quiet for a while, but we have some good news! A CRAN update, a manuscript in bioRxiv, a 3-year postdoc position open to be part of the mixOmics core team, and three workshops planned for the French autumn!

The 6.1.3 update is now on the CRAN, we fixed a few bugs (see list below), and we also have a new plotIndiv argument ‘background‘ to visualise the prediction area for a PLS-DA and sPLS-DA model (max 2 components). This is a powerful plot to visualise the effect of the different prediction methods. Why does a prediction method matters for the performance of the discriminant analysis models? See elements of information below.

Example of prediction area plot for the SRBCT data with a PLS-DA model, see ?srbct

All you need is the background.predict function, and overlay the results with plotIndiv. For example:

data(liver.toxicity)
X = liver.toxicity$gene
Y = as.factor(liver.toxicity$treatment[, 4])
plsda.liver = plsda(X, Y, ncomp = 2)

# calculating background for the two first components, and the mahalanobis distance
background = background.predict(plsda.liver, comp.predicted = 2, dist = "mahalanobis.dist")

plotIndiv(plsda.liver, background = background, legend = TRUE)

We also added the new functions get.confusion_matrix and get.BER to calculate a confusion matrix based on class prediction of test samples and their real class, and calculate their Balanced Error Rate, see ?get.BER. Example of outputs (for a DIABLO analysis on the breast cancer TCGA multi omics study):

Example from our DIABLO pipeline available at https://mixomics.org/wp-content/uploads/2012/03/mixOmicsRscripts.zip

 

We have submitted a new version of our mixOmics manuscript to bioRxiv! The manuscript is available at this link and has been a top tweeted story in #bioinformatics. The manuscript mostly summarises the latest mixOmics frameworks for Discriminant Analysis (sPLS-DA, DIABLO and MINT) with extensive R and Sweave codes here, give it a go! The supplemental thoroughly details these methods. It almost sounds like an end of a first mixOmics era as Florian, our very talented and dedicated core developer, debugger and developer of MINT has moved on for another postdoctoral position at the University of Queensland, and Kim-Anh is starting her new group as a Senior Lecturer position at the University of Melbourne (UoM), at the Centre for Systems Genomics. Do not fear, this means there will be a new round of developments, notably in the microbiome and metagenomics field, as we are opening a new 3-year senior postdoctoral position in Computational Biostatistics at UoM (with opportunity to teach at the School of Mathematics and Statistics). More details at this link.

Seventeen multivariate methods currently implemented in mixOmics! Can you recognise your favourite?

Three workshops are coming up, between Sept – Nov 2017 in France. The first edition of MAW’17 is the advanced mixOmics workshop to introduce our new frameworks (published and in development: DIABLO, MINT, SNPOmics, timeOmics, mixMC and extension of integration) to our advanced users. The workshop is free, but you will need to cover your own travel and accommodation costs. Toulouse, 23-24 Oct 2017. Send us an email and we can send you the details. The two other workshops will be our normal beginner mixOmics workshops, in September (Lille) and in early November (Toulouse). More details on our website soon.

 

Other enhancements and bug fixes:

Enhancements:
————-
1 – perf.sgccda (for DIABLO) now implements a constraint model (see details in ?perf)
2 – legend = TRUE option in circosPlot and plotDiablo
Bug fixes:
———-
– tune.splsda had a bug when assessing the ‘choice.ncomp’ based on ones-sided t-test of the error rate when the error rate was constant.
– sparse PCA deflation algorithm fixed
– added add mixOmics:: for pls functions to avoid clash with other packages

 

Why does a prediction distance matter? (full story in our manuscript)

The supervised multivariate methods in mixOmics can be applied on an external test set to predict the outcome of new samples with the predict function (predict), or to assess the performance of the statistical model (perf). The predict function calculates prediction scores for each new sample, or predicted coordinates, which are equivalent to the latent component scores in the training set.

Prediction distances. Our supervised models work with dummy indicator matrices Y to indicate the class membership of each sample, and result in a prediction score for each outcome category k, k = 1, . . . , K. Therefore, the scores across all classes K need to be combined to obtain the final prediction of a given test sample using a prediction distance. We propose distances such as ‘maximum distance’, ‘Mahalanobis distance’ and ‘Centroids distance’, as detailed our supplemental information and in ?predict. Those distance can give different predictions, which will be assessed in the performance of the model.

 

Software requirements for mixOmics workshops

We list below some installation requirements to ensure the mixOmics workshop will run smoothly for everyone.

Important reminders. We expect the trainees to have a good working knowledge in R programming (e.g. handling data frame, perform simple calculations and display simple graphical outputs) to be able to fully enjoy the workshop. Attendees are requested to bring their own laptop as this is a hands-on workshop (we will alternate theory and practice).

Software installation and updates. To run the R scripts in this workshop, you will need to install or update the latest versions of R available from the CRAN (currently > 3.4, see also Installation guide for R and RStudio), followed by the update or installation of the following R packages:

  • mixOmics version 6.3.1 (the version number is important)
  • mvtnorm
  • corrplot
  • igraph

The mixOmics package should directly import the following packages: igraph, rgl, ellipse, corpcor, RColorBrewer, plyr, parallel, dplyr, tidyr, reshape2, methods , matrixStats , rARPACK, gridExtra .

Check after install that the following does not throw any error*:

library(mixOmics)

We also advise to use the software RStudio

*For apple mac users, if you are unable to install the mixOmics imported library rgl, you will need to install the XQuartz software first .

Wifi will be available on site, but it is preferable that you make those installations before the workshop to avoid delays for the analyses.

Any question regarding the requirements and software installation: email us at mixomics[at]math.univ-toulouse.fr

Sept 12-14 Sept 2016, Toulouse, FR

Dear mixOmics users,

It is our pleasure to announce the following mixOmics 3-day workshop sponsored by EU COST Action “The quest for tolerant varieties: phenotyping at plant and cellular level (FA1306). More details in the pdf here. We do offer some travel grants for PhD students and early career researchers.

Organized by

 Dates:  Monday 12 September until Wednesday 14 September 2016 (3 full days)

Practical information: The course fee is 300 € for the academia and 600 € for the industry. It covers tuition, course material, coffee/tea, lunches and 1 diner downtown on Monday 12 September. Please note that no fee will be counted for 12 PhD students and/or early postdocs (up to 8 years after Ph.D) selected by COST Action FA1306, and their travel and living expenses will be take in charge up to 650 €.

Location: Toulouse, France

Maximum number of participants: 30

Deadline for application: 15 June 2016

Decision for attendee selection and grant allowance: 30 June 2016

Registration: 4 to 15 July 2016

Apply for this Summer School at: https://www.surveymonkey.com/r/FLSYJZP

In addition, send a CV to E Jamet (jamet [at] lrsv.ups-tlse.fr) and do not forget to mention if you want to apply for a grant.

Contact in Toulouse:  E Jamet, LRSV, jamet [at] lrsv.ups-tlse.fr

Good luck!

March 24-25 2016, Nantes, FR

This was our first workshop of the year!

‘many thanks for that great tutorial’, ‘excellent training, excellent teachers, excellent visual aids’

When : 24-25 March 2015

Where : Nantes

Institution : L’Ecole Nationale Vétérinaire, Agroalimentaire et de l’Alimentation Nantes-Atlantique (http://www.oniris-nantes.fr/)

Lab : Laboratoire d’Etude des Résidus et des Contaminants dans les aliments (http://www.oniris-nantes.fr/services/laberca/)

Teachers : Sébastien Déjean, François Bartolo (Toulouse-based team)

Sept 24-25 2015, Jouy-en-Josas, FR

Date: 24-25 September 2015, 9.30am – 5.30pm

Venue: INRA Jouy-en-Josas, Allée de Vilvert, 78352 Jouy-en-Josas, France

‘paper, pdf and slide great, excellent pedagogy‘

‘A lot of informations were brought and must be adapted on our complex data. Workshop was well organized and the speech was really clear even if some things were not easy to understand when we don’t know all the statistical terms. But Kim-Ahn was very available for more explanations. Thank you very much. A lot of new ideas for data treatments… 🙂‘

2015-09-27 21.11.05
Sorry no pictures from the workshop this time. All characters appearing in this Totoro puzzle are fictitious. Any resemblance to real workshop participants is purely coincidental.

 

coverMigale2

Sept 14-15 2015, Toulouse, FR

Date: 14-15 September 2015, 9am – 5pm

Venue: Salle 131 Bâtiment administratif, INRA Toulouse Auzeville, Chemin de Borde Rouge, 31326 Castanet Tolosan cedex

‘I discovered a lot of new methods and it gives me ideas for future analyses. The practical part of the course is particularly interesting (make it more concret).’

‘intervenants très disponibles pour discuter, et nous aider’

‘super bonne préparation’ ‘bonne articulation theorique et pratique’

20150915_161209
Sébastien (bottom left) explaining some mixOmics concepts

mixomics_Biostat