In mixOmicsTeam/mixOmics: Omics Data Integration Project

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What's New

March 2022

bug fix implemented for Issue #196. perf() can now handle features with a (s)pls which have near zero variance.
bug fix implemented for Issue #192. predict() can now handle when the testing and training data have their columns in different orders.
bug fix implemented for Issue #178. If the indY parameter is used in block.spls(), circosPlot() can now properly identify the $Y$ dataframe.
bug fix implemented for Issue #172. perf() now returns values for the choice.ncomp component when nrepeat $< 3$ whereas before it would just return NAs.
bug fix implemented for Issue #171. cim() now can take pca objects as input.
bug fix implemented for Issue #161. tune.spca() can now handle NA values appropriately.
bug fix implemented for Issue #150. Provided users with a specific error message for when plotArrow() is run on a (mint).(s)plsda object.
bug fix implemented for Issue #122. Provided users with a specific error message for when a splsda object that has only one sample associated with a given class is passed to perf().
bug fix implemented for Issue #120. plotLoadings() now returns the loading values for features from all dataframes rather than just the last one when operating on a (mint).(block).(s)plsda object.
bug fix implemented for Issue #43. Homogenised the way in which tune.mint.splsda() and perf.mint.splsda() calculate balanced error rate (BER) as there was disparity between them. Also made the global BER a weighted average of BERs across each study.
enhancement implemented for Issue #30/#34. The parameter verbose.call was added to most of the methods. This parameter allows users to access the specific values input into the call of a function from its output.
bug fix implemented for Issue #24. background.predict() can now operate on mint.splsda objects and can be used as part of plotIndiv().

July 2021

new function plotMarkers to visualise the selected features in block analyses (see https://github.com/mixOmicsTeam/mixOmics/issues/134)
tune.spls now able to tune the selected variables on both X and Y. See ?tune.spls
new function impute.nipals to impute missing values using the nipals algorithm
new function tune.spca to tune the number of selected variables for pca components
circosPlot now has methods for block.spls objects. It can now handle similar feature names across blocks. It is also much more customisable. See advanced arguments in ?circosPlot
new biplot function for pca and pls objects. See ?mixOmics::biplot
plotDiablo now takes col.per.group (see #119)

April 2020

weighted consensus plots for DIABLO objects now consider per-component weights

March 2020

plotIndiv now supports (weighted) consensus plots for block analyses. See the example in this issue
plotIndiv(..., ind.names=FALSE) warning issue now fixed