Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.
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
1 - 'constraint' parameter for sPLS-DA perf and tune functions added. 2 - plotLoading for PCA object 3 - color arugment in plot.tune and plot.perf added
In short,
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
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.
In short,
Please refer to our help files for the functions listed below.
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), breast.TCGA (for DIABLO), diverse.16S and Koren.16S (for mixMC)
1 - plotIndiv: displays explained variance for sPLS objects 2 - multilevel option is now included in PLS and PCA objects (argument multilevel = design or sample info) 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 1000 (memory issues with the package) 7 - plotContrib has been deprecated for plotLoadings 8 - tune.pca has been coded more efficiently 9 - ncomp input is now a single value in wrapper.rgcca, wrapper.sgcca, block.pls, block.spls, block.plsda, block.splsda
1 - explained variance for NIPALS/PCA fixed in print.pca, tune.pca and pca 2 - plot3d mistmatch legend color, double titles for plotIndiv ggplot2 and lattice, order of group for ggpot2 and lattice 3 - retired: data set prostate
1 - plotArrow for PLS, sPLS, rCC, rGCCA, sGCCA, sGCCDA is an improved version from our old s.match function (which is still available but will be soon deprecated) 2 - network function has been enhanced with various options to represent the nodes (e.g. lty.edge='dotted',row.names = FALSE), see our website for more examples 2 - rcc has a new argument method = c("ridge", "shrinkage") with shrinkage to estimate the shrinkage coefficients directly 3 - plotIndiv directly implements 3d plots (style='3d'), including ellipses, % of variance explained output for PCA, centroids and star plots (see example(plotIndiv)) 4 - plotVar directly implements 3d plots (style='3d'), legend can also be added with add.legend = TRUE 5 - cim and network have new arguments: save = c('jpeg','tiff','png','pdf') to save plots directly, and name.save. Argument threshold has been added/updated for both displays. Some arguments underwent name changes, see ?network
1 - network: a single function for all objects. 2 - pheatmap.multilevel has been deprecated with the new enhancements of CIM 3 - plot3dIndiv and plot3dVar have been deprecated (see new features in plotIndiv and plotVar) 4 - plotContrib also now available for sgccda plsda, splsda objects. Added arguments complete.name.var and col.ties (see ?plotContrib), changed argument name ties to show.ties 5 - imageMap has been deprecated (now included in cim directly) 6 - pca also outputs 'loadings' and 'variates' to remain in the mixOmics spirit 7 - tau.estimate help file removed as now directly called as internal function from rcc and srgcca 8 - imgCor: added argument 'main' and changed argument names x.sideColors and y.sideColors to sideColors 9 - cim: changed argument names labRow and labCol to row.sideColors and col.sideColors
1 - plotContrib now fixed (showed wrong contribution colors) 2 - cim has been fixed to show the ordered variable names after users reports (thanks!) 3 - resolved blank page in network when saving image as a pdf
1 - plotIndiv: the argument col is back! see our help file.
2 - plotVar has been dramatically improved with more efficient coding (not a S3method anymore) and availability of different plotting styles with 'ggplot2', 'lattice' or 'graphics'.
-rgcca tau parameter output enhanced.
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
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
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)
1 - The sPLS algorithm was rewritten to ensure convergence. This implies that spls results might be slightly different from version < 5.1-0!
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 helptau.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
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 French so contact us for more details if needed). plot.perf() has been updated
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
-the perf and predict functions have been updated. The prediction values are calculated based on the regression coefficients of Y onto the latent variables associated to X.
-scaling issues in perf/old-valid have been fixed
-one warning on the plotIndiv.rcc has been fixed.
-transition from valid() to perf() announced.
-pls and spls function have been modified and harmonised w.r.t to scaling. Loading vectors a and b are now scaled to 1. Latent variables t and u are not scaled (following Table 21 of the Tenenhaus book - which is in French, sorry!).
-the argument abline.line has been set to FALSE by default in all plotIndiv functions.
New dependency to RGCCA package to enable integration of multiple matching data sets
Multilevel analysis
RCC -the function estim.regul has been renamed tune.rcc -the function pcatune has been renamed tune.pca
-in plotIndiv: horizontal and vertical abline set as a default argument -a new argument in splsda() function added: near.zero.var = TRUE or FALSE to speed up computations (near.zero.var = FALSE to gain speed) -the valid() function has been updated to speed up the computations. There is no 'criterion' argument to choose anymore (by default, all are included in the computation) -in plotVar: matching arguments user-function to avoid additions of unused arguments -in plotIndiv, arguments 'x.label' and 'y.label' were replaced by 'X.label' and 'Y.label' -in pca, argument 'scale.' was changed to 'scale'
-pls and spls have been modified to better handle NA values
The new methodology IPCA and sIPCA have been added as well as the associated S3 methods for the graphical outputs
GeneBank IDs and gene titles were added in the liver toxicity study
Modifying the valid function: the Q2 criterion has been implemented
var.label argument is used in plotVar.plsda, plotVar.splsda, plot3dVar.plsda, plot3dVar.splsda instead of X.label
New S3 method network for pls
New code for valid function to PLS-DA and sPLS-DA models validation
New code for plot.valid to display the results of the valid function for PLS-DA and sPLS-DA models
cim and network were modified to obtain the simMat matrix as value
plotVar was modified to obtain the coordinates for X and Y variables as value
In predict function, several or all prediction methods are available simultaneously to predict the classes of test data with plsda and splsda
The argument 'mode' has been removed of plsda and splsda functions
PCA has been modified to run either SVD (no missing values) or NIPALS (missing values)
print.pca has been added to display the results of PCA
pcatune has been added to guide the choice of the number of principal components
New S3 methods plotIndiv and plotVar for PCA
New S3 method plot.valid to display the results of the valid function
New code for imgCor function for a nicer representation of the correlation matrix
In predict.plsda and predict.splsda functions the argument 'method' were replaced by method = c("max.dist", "class.dist", "centroids.dist", "mahalanobis.dist")
New arguments for the cim function:
Modifying the valid function:
Functions pls, plsda, spls and splsda were modified to identify zero- or near-zero variance predictors
Functions plotVar.plsda, plotVar.splsda, plot3dVar.plsda, plot3dVar.splsda were modified to represent only the X variables
New function: 'nearZeroVar' for identification of zero- or near-zero variance predictors
New arguments ("axis.labelX", "axis.labelY") in the function imgCor, to indicate if the labels of axis have to be shown or not
New classes splsda and plsda for predict, print, plotIndiv, plot3dIndiv, plotVar, plot3dVar
Several prediction functions are avaiable to predict the classes of test data with plsda and splsda see predict (argument 'method' ("class.dist", "centroids.dist", "Sr.dist", "max.dist"))
New functions map & unmap borrowed from the mclust package
New functions pca, plsda and splsda, as well as extensions of plot3dVar and plot3dIndiv for pca
New network.default function which is called by network.rcc and network.spls
bin.color function added in network.default to color edges w.r.t. the values in the simMat matrix
nipals has been improved to be computationally more efficient
Missing values are treated as in Tenenhaus in pls, spls and valid functions
New argument 'ncomp' in rcc function, argument 'ncomp' has been removed from 'summary' and 'rcc'
New option ("XY-variate") for the argument 'rep.space' in the 'plot3dVar'
'tick marks' values have been corrected for color key in cim
Computation of the simMat matrix for pls and spls - canonical mode, and correction in plotVar, plot3dVar, cim and network
Correction of the default argument 'rep.space = "XY-variate"' in plotIndiv and plot3dIndiv
Correction of the manual
Former R package integrOmics has been renamed mixOmics
In functions plotIndiv, plotVar, cim, network the arguments 'dim1', 'dim2', 'ncomp' were replaced by 'comp', a vector of length 2 (by default 'comp = 1:2')
Network has a new argument 'alpha'