klecao – Page 7

Update on CRAN 5.1.1 Major changes

In the last few months we have been busy with our major update. This is quite a major release with additional new features.

One major change that will impact all of us is the function plotIndiv. While we have new (sexy) functionalities, the argument ‘col‘ was swapped to ‘group‘. We will see if we can patch it back in the next release (in a month). In the meantime, give it a try, because it is worth the trouble!

We also fixed a convergence issue in the main sparse PLS algorithm. This may slightly affect your end feature selections as the algorithm is now converging properly.

We list the changes below, enjoy!

New features:
1 – plotContrib for objects of class PLSDA and sPLSDA has been added and is of particular interest for those analysing microbial communities / metagenomics data.

2 – wrapper.sgccda was added to enable multiple data sets integration with one or several factor outcomes. Note: the prediction function for this new add-on has not been fully tested yet and is not available.

3 – wrapper.sgcca and wrapper.sgccda now have an argument called ‘keep‘ that you can use as an alternative to the ‘penalty’ old argument. Keep is the equivalent of the keepX in the PLS method to specify the number of variables to select on each component and each block. Refer to the help file, as keep should be input as a list of length the number of blocks, and each element of the list (corresponding to a block) indicates the number of variables to select on each component (yes, it becomes, indeed, complicated).

4 – All wrapper methods for the multiblock module, i.e. wrapper.rgcca, wrapper.sgcca and wrapper.sgccda take the input argument ‘blocks‘ (instead of previously ‘data‘) – this is to enable a smoother transition to the next update!

5 – plotIndiv has been improved dramatically. A single function can now be used for the objects PLS, sPLS, PLS-DA, SPLS-DA, rCC, PCA, sPCA, IPCA, sIPCA, rGCCA, sGCCA, sGCCDA (not an S3 function anymore). In addition, we now provide the new arguments (and more to come!):
– ellipse plots are now available, a group argument is requested for the unsupervised methods (PCA, IPCA, PLS)
– three types of graphical plot: graphics (version < 5.1-0), ggplot2 and lattice
– legend and title can be added
– NOTE: if you want to color each sample with respect to a factor (i.e. a factor of length n), then the argument to use is ‘group’. If you use a supervised approach then col.per.group is a vector of length the number of groups. These arguments may change in the coming up updates.

6 – cim has been implemented for PLS, sPLS, PLS-DA, SPLS-DA, rCC, PCA, sPCA, IPCA, sIPCA and includes a wide range of options to plot a single data set in the form of a heatmap (new!), or the cross correlation between two matching data sets via the methods rCC or (s)PLS using the cross product between latent variables and loading vectors (improved with legends and color bars). We will give more examples on our website.

7 – added package dependencies: ggplot2 and ellipse

Enhancements:
1 – All wrappers for multiple data integration have been improved and re-implemented. Consequently, the dependency to RGCCA has been removed, and three wrapper functions are now available: wrapper.sgcca, wrapper.rgcca and wrapper.sgccda (see New Feature #2 above).

2 – selectVar has been extended for the non sparse versions PCA, PLS and PLS-DA and output the features with decreasing absolute weights in the loading vectors. It is used in particular for plotContrib (see New feature #1 above)

Bug fixes:
1 – The sPLS algorithm was rewritten to ensure convergence. This implies that spls results might be slightly different from version < 5.1-0!

Aug 13-14 2015, Brisbane, AUS

Our first Brisbane workshop entitled ‘mixOmics: exploration and integration of ‘omics data‘ is taking place at the Translational Research Institute in sunny Brisbane, where the Lê Cao team is based. The workshop is sponsored by AGTA small grant scheme, QIAGEN and our institutes the University of Queensland Diamantina Institute (UQDI) and the Translational Research Institute (TRI).

The workshop will be mostly targeting postgraduate students, postdocs and researchers working in biology, bioinformatics, computational biology, with a basic knowledge in statistics and good R programming skills.

Dates: 13-14 August 2015

Time: 9am – 5pm

Venue: SparQ-eD Room 2011, level 2, Translational Research Institute, 37 Kent st, Woolloonggabba, Brisbane.

‘The case studies were very helpful in understanding the type of data each test can be used with.’

‘Loved it. Very well presented, course material very clear and will be a useful resource in the future. Thanks Kim-Anh!!‘

‘Very nicely and clearly explained. Particularly appreciated that we were given the R scripts, so that we could concentrate on concepts rather than scripting.‘

20150813_161837 — ‘Everyone very helpful. Thanks Benoit!! You are a legend!’

20150813_155407 — ‘Very well designed such that the concepts were presented and explained while the trickiness of the R code was helped enormously by being able to follow the downloaded scripts. The tutors were very helpful in interpreting what the code was doing.’

20150813_155455 — ‘I really enjoyed the workshop – I found it very informative and the analogies used to help explain some of the more difficult concepts were very helpful.’

mixOmics 5.0-4 on CRAN

Dear mixOmics users,

We have submitted an updated version to the CRAN. The changes are listed below. Few points in particular to keep in mind:

select.var() was renamed selectVar() (clash with our dependency to the package MASS)
we borrowed the function tau.estim() to the RGCCA package in order to estimate the regularisation parameters from the rCCA – a way to bypass tune.rcc() with large matrices
the multilevel module has been updated, with some changes in the call of the function and a new function called withinVariation() (see details on the website https://mixomics.org/methods/multilevel/)

We thank you all for your interest in the package. There are important upcoming developments so please keep in touch via the website.

Changes in 5.0-4
================

New features:
————-
1- new set of palettes have been added: color.jet, color.spectral, color.GreenRed and color.mixo
2- the multilevel module has been updated. A new function called withinVariation() calculates the within matrix. Our new website www.mixOmics.org will be updated shortly
3- the function tau.estim was borrowed from the RGCCA package and included in mixOmics in order to estimate the regularisation parameters from rcc more efficiently than tune.rcc(). We noted differences in those parameters estimates between tune.rcc() and tau.estim() as the methods use either cross-validation or the formula from Shaefer and Strimmer (2005). When using tau.estim() we also advise to center and scale the input data in rcc(). See help tau.estim().
4- because of a S3 method clash with the MASS package with the current R version we had to rename select.var to selectVar

Bug fixes:
———-
1- select.var.sgcca has been fixed (the outputs were messy)
2- minor bug in plotVar.sgcca and plotVar.rgcca fixed
3- the algorithm in perf.pls and perf.spls has been almost entirely changed. We are now using a different algorithm to estimate the Q2, as presented in the help Rd file (unfortunately the reference is in French so contact us for more details if needed). plot.perf() has been updated

Enhancements:
———-
1- network default color set to color.GreenRed
2- output feature.final in perf S3 function has been removed. Better to use select.var() to obtain the list of selected variables
3- the multilevel module has been updated. The argument names were changed to ‘design’ instead of ‘cond’. The pheatmap.multilevel() function has been improved.
4- the nearZeroVar function that was borrowed from the caret package has been enhanced to improve computational time as this is costly in the pls/spls functions

April 9-10 2015, Auckland, NZ

We ran our third workshop at the University of Auckland, New Zealand. The event was organised by the Department of Statistics’ Statistical Consulting Centre. For more details go to the Statistical Consulting Centre’s Workshops web page.

There were 34 live and 6 online participants.

Coming up workshops: Brisbane (End of June 2015) and Paris (24-25 Sept 2015).