Compute cosine from Frobenius norm between kernels and display the corresponding correlation plot.

cim.kernel(..., scale = TRUE, 
           method = c("circle", "square", "number", "shade", "color", "pie"))



list of kernels (called 'blocks') computed on different datasets and measured on the same samples.


boleean. If scale = TRUE, each block is standardized to zero mean and unit variance and cosine normalization is performed on the kernel. Default: TRUE.


character. The visualization method to be used. Currently, seven methods are supported (see Details).


The displayed similarities are the kernel generalization of the RV-coefficient described in Lavit et al., 1994.

The plot is displayed using the corrplot package. Seven visualization methods are implemented: "circle" (default), "square", "number", "pie", "shade" and "color". Circle and square areas are proportional to the absolute value of corresponding similarities coefficients.


cim.kernel returns a matrix containing the cosine from Frobenius norm between kernels.


Lavit C., Escoufier Y., Sabatier R. and Traissac P. (1994). The ACT (STATIS method). Computational Statistics and Data Analysis, 18(1), 97-119.

Mariette J. and Villa-Vialaneix N. (2018). Unsupervised multiple kernel learning for heterogeneous data integration. Bioinformatics, 34(6), 1009-1015.


Jerome Mariette <>

Nathalie Vialaneix <>

See also



# compute one kernel per dataset
phychem.kernel <- compute.kernel(TARAoceans$phychem, kernel.func = "linear")
pro.phylo.kernel <- compute.kernel(TARAoceans$pro.phylo, kernel.func = "abundance")
pro.NOGs.kernel <- compute.kernel(TARAoceans$pro.NOGs, kernel.func = "abundance")

# display similarities between kernels
cim.kernel(phychem = phychem.kernel,
           pro.phylo = pro.phylo.kernel,
           pro.NOGs = pro.NOGs.kernel, 
           method = "square")