cim.kernel.Rd
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.
data(TARAoceans)
# 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")