Author: klecao

Patch 6.1.1

Dear mixOmics users,

We have a new patch version 6.1.1 available from the CRAN to fix a few bugs by our team or mixOmics users (thank you!) and few enhancements and updates to follow ggplot2 updates.

For those using DIABLO, please note points 8 & 9 as we changed the default parameters for a scheme = ‘horst’ instead of ‘centroid’ and  init = ‘svd.single’ instead of ‘svd’ in the methods, as we feel it was more appropriate. That may change your results compared to last version and you may want to use the old parameters instead.

New features:
1 – mint.pca function to perform unsupervised integration of independent data sets
2 – new weighted prediction for block approaches for both unsupervised and supervised analyses, see ?predict.spls and ?predict.splsda.
3 – ‘cpus’ parameter for sPLS-DA perf/tune and block.splsda perf/tune added to run the code in parallel

Enhancements:
4 – ‘constraint’ parameter for sPLS-DA perf and tune functions added.
5 – plotLoading for PCA object
6 – color argument in plot.tune and plot.perf added

Bug fixes:
7- predict with logratio (the logratio transform is now performed inside the predict function)
8- in block methods, scheme = ‘horst’ set by default instead of centroid
9- in block methods, initialisation set to svd.single by default

Thank you again for using mixOmics.

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.

Patch 6.0.1

We are preparing a patch to fix some small bugs we (and other users) noticed since we released version 6.0.0. The .zip (windows) and .tar.gz (linux / mac) can be downloaded from this page. We plan to push a completed patch on the CRAN end of august 2016.

Latest patch update: 18 August

Package to download: mixOmics_6.0.1.zip (windows) or mixOmics_6.0.1.tar.gz (linux, mac)

For the .tar.gz you can install it via RStudio (mac environment) alternatively, type in a terminal (linux environment):

R CMD INSTALL mixOmicsPatch_6.0.1.tar.gz

Then load the patch version in R:

#Load the patch 
library(mixOmics)

Bugs fixed:

Date: 18/08/2016

  • Offset value of 1 added for CLR log transform for mixMC
  • circosPlot variable name fixed for mixDIABLO, new argument size.variables
  • cimDiablo and circosPlot match name to legend color for mixDIABLO, new arguments transpose, row.names and col.names

Date: 03/08/2016

  • Call to plsda.vip() from the RVAideMemoire package
  • Speed up computations for PCA with logratio transformation
  • perf / tune for sPLS-DA with log ratio transformation (that will improve the performance of the model)
  • network function for block.spls models (still in development!)

Version 6.0.0

Dear mixOmics users,

It is with a huge relief and pride (and maybe some slight anticipatory anxiety of that very moment) that we announce the release of mixOmics_6_0_0 on CRAN. We are introducing three novel frameworks, mixMC, mixMINT and mixDIABLO, which are described (as best as we can, given the free remaining time we have on our hands not debugging) on the website. All manuscripts are in submission / revision so feel free to ask.

A special thanks to those who made that update possible, in particular Florian Rohart and Benoit Gautier, and the whole Lê Cao lab troop for the numerous layers of testing. We tested as much as we could but of course all data are different. Do no hesitate to report bugs or comments at mixomics[at]math.univ-toulouse.fr or on our bitbuket issue list.

Members of the mixOmics team will be present at the following summer conferences in the nothern hemisphere, feel free to say hello!
Rencontres R 2016: Toulouse, France, June 22-24, presentation on mixMINT
JOBIM 2016: Lyon, France, June 28-30, presentation on mixDIABLO
ISMB / ISCB 2016: Orlando, Florida, July 8 – July 12, attendance and presentation to the SBV crowd verification challenge, using our cousin package bootPLS
JSM 2016: Chicago, Illinois, July 30 – Aug 4, presentation on mixMC
INPPO 2016: Bratislava, Slovaquia, Sept 4-8, keynote

Below is the list of changes in the package. Please note the few argument names changes for some of the plots.

Changes in 6.0.0 (major, implementation improvements and new methods)

In short,

– argument names which changed in all plots for homogeneous call are: ‘main’ changed to ‘title’, ‘add.legend’ -> ‘legend’, ‘cex.xxx’ -> ‘size.xxx’, ‘plot.ellipse’ -> ‘ellipse’

– ncomp is now a single value in all wrapper. and block. functions (multiple integration)

Please refer to our help files for the functions listed below.

New features:

1- log.ratio transformation (log.ratio = c(‘CLR’, ‘ILR’)) in PCA and PLS-like methods to deal with compositional microbiome data (see website www.mixOmics.org/mixMC for details)

2 – plotLoadings is a novel graphical way of showing the regression coefficients of the selected variables (deprecated plotContrib)

3 – mixMINT module to analyse independent data sets on the same type of variable. See www.mixOmics.org/mixMINT for details.

      Added methods: mint.pls, mint.plsda, mint.spls, mint.splsda;

      S3 visualisations: plotIndiv, plotLoadings, plotVar;

      Performance evaluation: perf (new, uses leave one out group), tune (new, uses leave one out group)

4 – mixDIABLO module to integrate different omics data sets performed on the same samples. See www.mixOmics.org/mixDIABLO for details.

      Added methods: block.pls, block.plsda, block.spls, block.splsda;

      S3 visualisations: circosPlot (new), cimDiablo (new), plotDiablo (new), plotIndiv, plotLoadings, plotVar;   

      Performance evaluation: perf, tune, predict (new with majority vote for DIABLO, $vote)

5 – new data sets: stemcell (for MINT), TCGA.breast.cancer (for DIABLO)

Enhancements:

1 – plotIndiv: displays explained variance for sPLS objects

2 – multilevel option is now included in PLS and PCA objects (argument multilevel = design or sample information)

3 – WARNING: in all plots, homogeneous arguments call: ‘main’ changed to ‘title’, ‘add.legend’ -> ‘legend’, ‘cex.xxx’ -> ‘size.xxx’, ‘plot.ellipse’ -> ‘ellipse’

4 – print.method functions updated to show the range of graphics / other functions to use with the object

5 – predict function now outputs class names in $class

6 – data set vac18 reduced number of genes is now 100 genes

7 – plotContrib has been depraceted for plotLoadings

8 – ncomp input is now a single value in wrapper.rgcca, wrapper.sgcca, block.pls, block.spls, block.plsda, block.splsda

Bug fixes:

1 – explained variance for NIPALS/PCA fixed

2 – plot3d mistmatch legend color, double titles for plotIndiv ggplot2 and lattice, order of group for ggpot2 and lattice

3 – retired: data set prostate

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)