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Category: Workshop

Version 6.3.0 and workshop

A new CRAN version is now available. We have considerably improved the computational time for the tune and perf functions! (see example below). We also fixed some reproducibility issues when using parallel computing with a set seed.

The update of the package will require new dependencies: ‘matrixStats’, ‘rARPACK’, ‘gridExtra’

There are still some spots left for the beginner mixOmics workshop in Toulouse, 9-10 Nov. Details here.

 

Enhancements:
————-
– huge gain in computation time for the tune functions tune.splsda and tune.block.splsda. The larger the data, the bigger the gain. Requires new dependencies: ‘matrixStats’, ‘rARPACK’, ‘gridExtra’
– a plot for an object `tune.block.splsda’
– tune.multilevel function was deprecated a while ago and now removed.

Bug fixes:
———-
– fixed reproducibility problem when using parallel coding in tune.block.splsda (via the `cpus’ argument)
– network: correlation with missing values fixed, label names fixed
– fixed perf for block.splsda objects with prediction distances
– some NA issues reported in 6.2.0 fixed (hopefully)

 

The gain in computational time is reported below for our different supervised frameworks. It all depends on your operating system, but generally, the user time  =  execution of the code, the system time = system processes (e.g opening and closing files), and the elapsed time is the difference in times since we started the stopwatch.

6.2.0, 2 postdoc positions and workshops

Dear mixOmics users,

Our new update 6.2.0 is now available on CRAN as part of our new version of our manuscript.

manuscript & package update:

The mixOmics manuscript introducing the supervised and integrative frameworks (PLS-DA, DIABLO block.plsda and MINT) has be updated, along with all the R / Sweave case studies, manuscript and codes are available at this link.  The case studies are also published on our website (sPLSDA:SRBCT, Case study: TCGA and Case study: MINT).

The manuscript describes in more details the difference prediction distances (see also the supplemental material) and the interpretation of the AUROC for our supervised methods.

The constraint argument was removed from all our methods, due to a risk of overfitting.

New features:

– The constraint argument (version 6.1.0 – 6.1.3) was removed in the functions perf and tune for all supervised objects because of a risk of overfitting

Enhancements:

– AUROC aded for MINT objects mint.plsda and mint.splsda where the study name needs to be specified, e.g. auroc( .., roc.study = “study4”). See ?auroc

– choice.ncomp output added on all perf and tune functions for all supervised methods.

– mat.c output for pls and plsda objects (matrix of coefficients from the regression of X / residual matrices X on the X-variates).

Bug fixes (thank you to the users who notified us on bitbucket):

– fixed bug when using predict, perf or tune with the error msg: ‘Error in predict.spls(spls.res, X.test[, nzv]) : ‘newdata’ must include all the variables of ‘object$X”

Workshops:

We advertised two workshops at this link. The advanced workshop 23-24 Oct 2017 is fully subscribed. This is our first MAW (mixOmics advanced workshop), but there will be more planned in 2018. We still have a few spots left for the classic workshop on the 9-10 Nov 2017 in Toulouse, contact us for more information (priority will be given to students and early career researchers).

Two senior postdoc positions (2 year and 3 year) still open!

The Australian mixOmics team now based at the University of Melbourne is recruiting two senior postdocs in the fields of computational biology or statistics, 1 full time 2-year position to work with the Stemformatics team on exciting omics integrating problems (‘omics and single cell omics) to improve stem cell classification, and 1 full time 3-year position for innovative multivariate methods developments for ‘omics time course, microbiome and P-integration. Contact us for more information.

Website update:

With the invaluable help from the bioinformatics masters students Danielle Davenport and Zoe Welham we are currently revamping the website to ensure all codes are running correctly. Thank you for those who sent us some feedback!

Nov 22-24 2017, Toulouse, FR

[Update:  5 spots left, contact us] ]Following last year’s success of our COST workshop, the second edition will be run by Dr Sébastien Déjean and his crew in Toulouse. The event is organised by the local committee at UGSF (Drs Estelle Goulas, Anne-Sophie Blervacq, Anne Creach, Brigitte Huss and Prof Simon Hawkins)

Dates: 12-14 September (3 days)

Venue: Toulouse, France, TBA

Fees: 300 EUR (academics) and 600 (private) that include tuition, course material, coffee breaks, lunches and one dinner in town. Bursary for 12 PhD students and early career researchers are funded by COST ACTION FA1306, apply!

Application:  see details here.

Send your CV to: Estelle.goulas [at] univ-lille1.fr and mention whether you are applying for a travel bursary.

Deadline for application: 15 October 2017 

More details: at this link.

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

Oct 23-24 2017, Toulouse, FR, Advanced Workshop

[Update: the workshop is full subscribed and registrations have closed!] This is the first edition of our advanced workshop, run by Dr Kim-Anh Lê Cao and Sébastien Déjean. The event and Dr Kim-Anh Lê Cao’s visit is sponsored by the visiting scientist program INP Toulouse and by the company Methodomics.

The mixOmics package has undergone substantial improvements and methodological developments in the last 18 months to address the strong demand from the computational and biological community to integrate multiple (>2) `omics data sets, including microbiome, genotype and longitudinal data. The aim of this advanced workshop is to introduce our new frameworks and encourage discussions, collaborations and suggested improvements on the themes including:

  1. N-integration with DIABLO
  2. P-integration with MINT
  3. Longitudinal `omics analysis with timeOmics (not yet in mixOmics!)
  4. Exploratory multivariate analysis with SNPOmics (not yet in mixOmics!)
  5. mixMC: mixOmics for Microbial communities, with N-integration extensions

Date: 23-24 October 2017

Venue: Ground level, building ‘SDAR’, INRA Toulouse, Castanet Tolosan (map access coming soon)

Prerequisites: Since this is an advanced course, we expect the participants to be expert in R programming language and familiar with multivariate projection based methods and mixOmics.

More details: at this link

Interested? contact us at mixomics [at] math.univ-toulouse.fr

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

12-14 Sept 2016, Toulouse, FR (COST)

Our workshop in Toulouse (3-day) was sponsored by EU COST Action “The quest for tolerant varieties: phenotyping at plant and cellular level (FA1306). and organised by GenoToul Biostat platform, Laboratory of Plant-microbe Interactions (LIPM) and Plant Science Research Laboratory (LRSV). We trained and coached 26 participants and had a great time during the third day (‘byo’ data) and the ice breaking gala dinner!

 

mixomics-summer-school_09-2016-crop
Participants, organisers and tutors
20160913_143319
Looking very studious! We were hosted by the LIPM lab, INRA Auzeville Toulouse

Some feedback from our participants:

Overall I did enjoy the workshop, it was one of the most interesting and well put together that I have attended. Thank you very much.

The tutorials on the website are excellent for training.

It was a very good mixture of theory and practice to directly try out the methods. Also there were many experts who where available for questions. The presentations were quite clear to me as well as the course material and the provided scripts.

‘[Day 3] was useful, because it allows to check if we have well understood the use of each analysis, and bring our own data allows to make these analysis more concrete.’

[…] I could discuss with some other participants with similar experimental design and see how they think [they can] apply mixOmics

 

Data for Day 3 available:

Draught response in sunflower data with Get_started script (knitr format, open the .Rmd file with RStudio), with slides from David.

Some useful references discussed during the workshop:

Liu et al 2015: we used Principal Component Curves (a variant of PCA, but where you fit a curve, and where you need a ‘reference’ group) to quantify pathway regulation of Homologous Recombination in breast cancer.

Singh et al. 2016 (bioRxiv): the asthma study (#2) summarised some of the omics data sets into gene modules to quantify pathways before the integration step. This is the DIABLO paper.

Straube et al 2015: the linear mixed model framework to reduce the dimension of time course data from (n x p x T) to (T x p), lmms is available on CRAN.

Straube et al 2016: Dynomics to detect delay between time course data. Submitted.

Rengel et al. 2012 paper fr the drought response in sunflower.

Wickham 2014: tidy data