The feature selection step in timeOmics depends on the methods used in mixOmics (lasso). What changes here is the optimization process to determine the best keepX. In timeOmics, we use the variation of the silhouette coefficient with respect to the grid of values to be tested, rather than the cross-validation in mixOmics.
Cluster assignment is similar to that proposed by the selectVar function, based on the absolute maximum loading value per feature.
With sparse methods, some loadings are set to 0 by lasso. This is performed in mixOmics.
As you have noticed, there may be a difference between the number of features requested and those returned.
This behavior has already been noticed in mixOmics and could be related to this post:
However, concerning the plot, the 34 rna are indeed present but are hidden by others as they feature similar scaled profiles. To be convinced, you may run all the code by yourself and dig into the object returned by getCluster(final.block, user.block = "RNA") to find the 34 RNA.