Month: September 2016

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

 

Version 6.1.0 and latest publications

We are proud to announce our new update 6.1.0 available on CRAN. It was supposed to be a small patch but we got slightly ahead of ourselves. Special thanks to the mixOmics French’Oz developers, Dr Florian Rohart (University of Queensland, Brisbane) and Mr François Bartolo (Université de Toulouse, France), as well as several users who have been using our latest methods and reported bugs or suggested improvements on our bitbucket issue website.

Manuscripts and publication update

  • Rohart F.,  Matigian N., Eslami A., Bougeard S and Lê Cao, K. A..MINT: A multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms. Now available on bioRxiv!

  • Singh A, Gautier B, Shannon C, Vacher M, Rohart F, Tebbutt S, K-A. Lê Cao. DIABLO – multi-omics data integration for biomarker discovery. Manuscript available in bioRxiv.

  • K-A. Lê Cao*, ME Costello*,  VA Lakis, F Bartolo, XY Chua, R Brazeilles, P Rondeau. (2016) MixMC: Multivariate insights into Microbial Communities.PLoS ONE 11(8): e0160169 [link]

List of changes in mixOmics 6.1.0 (in NEWS file)

In short,
– cimDIABLO argument ‘corThreshold’ replaced by ‘cutoff’
– new plots of tune and perf results now available
– tune function for block.splsda/DIABLO method
– auroc for supervised methods

New features:

1- auroc function applicable for (mint).(block).(s)plsda objects. AUc values also included in perf and tune functions (except mixDIABLO module)
2- tune.block.splsda function to chose the keepX parameters of block.splsda (a.k.a mixDIABLO)
3- plot for perf objects displays the classification error rate w.r.t components
4- plot for tune objects displays the classification error rate w.r.t keepX values (not implemented for tune.block.splsda)
5- multilevel function has been removed (as planned) as it is now included as an argument in other functions (see pca, pls, splsda, etc)

Enhancements:
1 – All tune functions (except for mixDIABLO/block.splsda module) include a ‘constraint’ argument to either build the model based on user input specific parameters (object$keepX.constraint) or based on the optimal parameter keepX determined by the tune function, see examples in help files.
2 – All perf functions (except for mixDIABLO/block.splsda module) have now a ‘constraint’ argument that allows the performances to be calculate either based on the number of parameters (object$keepX) defined in object or based on the variables selected on each component, see examples in help files.
3 – max.iter has been set to 100 to speed up computational time for all multivariate methods except pca/spca.
4 – cimDiablo: new arguments include transpose, row.names and col.names
5 – circosPlot: new arguments include var.names and comp. Argument ‘corThreshold’ has been replaced by ‘cutoff’.
6 – plotIndiv: new argument legend.title
7 – network function for block.spls(da) models and allows to plot for more than 2 blocks
8 – PCA: new argument ilr.offset to be used only for ILR log transform in PCA (mixMC module)
9 – Legend added in plotDiablo, new argument legend.ncol

Bug fixes:
1 – plotIndiv and ellipse: plot ellipse for all groups with more than 1 sample
2 – predict function: argument multilevel added, log transform included
3 – Call to plsda.vip() from the RVAideMemoire package
4 – other small bugs as listed in out bitbucket issues, matching rgl package changes.