Compute biMAP basedon CA results
ca_biMAP.RdThe function takes either a `caclust` and a `cacomp` object as input and computes the biMAP embedings for cells and genes on the basis of the singular vectors of CA.
Usage
ca_biMAP(
obj,
caobj,
k = 30,
rand_seed = 2358,
use_SNN = TRUE,
features = NULL,
...
)
# S4 method for caclust,cacomp
ca_biMAP(
obj,
caobj,
k = 30,
rand_seed = 2358,
use_SNN = TRUE,
features = NULL,
...
)
# S4 method for SingleCellExperiment,ANY
ca_biMAP(
obj,
caobj = NULL,
k = 30,
rand_seed = 2358,
use_SNN = TRUE,
features = NULL,
...,
caclust_meta_name = "caclust",
cacomp_meta_name = "CA"
)Arguments
- obj
A `caclust` object or `SingleCellExperiment` object with `caclust` and `cacomp` objects stored in the metadata.
- caobj
A `cacomp` object.
- k
integer. Number of nearest neighbours to use to compute UMAP.
- rand_seed
integer. Random seed for UMAP.
- use_SNN
TRUE/FALSE. This parameter only works when method == 'ca'. If TRUE, it will calculate bimap embedding of genes and cells contained in the SNN graph together with the given 'fearures' if any. If FALSE, only calculate bimap embedding of cells contained in the SNN graph together with the given 'fearures' if any.
- features
character/vector of gene names. This parameter only works when method == 'ca'. 'ca' method allows visualization of genes which are absent from SNN graph, so the 'features' could be genes detected by our biclustering algorithm, and genes which goes through CA analysis. Setting use_SNN = FALSE allows users to have a visualization of the feature genes user defined/interested.
- ...
Further arguments
- caclust_meta_name
the name of caclust object stored in metadata(obj)
- cacomp_meta_name
the name of cacomp object stored in metadata(obj)