mixKernel
Omics Data Integration Using Kernel Methods
Kernel-based methods are powerful methods for integrating heterogeneous types of data. mixKernel aims at providing methods to combine kernel for unsupervised exploratory analysis. Different solutions are provided to compute a meta-kernel, in a consensus way or in a way that best preserves the original topology of the data. mixKernel also integrates kernel PCA to visualize similarities between samples in a non linear space and from the multiple source point of view <doi:10.1093/bioinformatics/btx682>. A method to select (as well as funtions to display) important variables is also provided <doi:10.1093/nargab/lqac014>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.9-2 |
rolling linux/jammy R-4.5 | mixKernel_0.9-2.tar.gz |
2.2 MiB |
0.9-2 |
rolling linux/noble R-4.5 | mixKernel_0.9-2.tar.gz |
2.2 MiB |
0.9-2 |
rolling source/ R- | mixKernel_0.9-2.tar.gz |
2.2 MiB |
0.9-2 |
latest linux/jammy R-4.5 | mixKernel_0.9-2.tar.gz |
2.2 MiB |
0.9-2 |
latest linux/noble R-4.5 | mixKernel_0.9-2.tar.gz |
2.2 MiB |
0.9-2 |
latest source/ R- | mixKernel_0.9-2.tar.gz |
2.2 MiB |
0.9-2 |
2026-04-26 source/ R- | mixKernel_0.9-2.tar.gz |
2.2 MiB |
0.9-2 |
2026-04-23 source/ R- | mixKernel_0.9-2.tar.gz |
2.2 MiB |
0.9-2 |
2025-04-20 source/ R- | mixKernel_0.9-2.tar.gz |
2.2 MiB |