The relevance map is cached insided of the DiffusionMap
.
gene_relevance(coords, exprs, ..., k = 20L, dims = 1:2, distance = NULL, smooth = TRUE, verbose = FALSE) # S4 method for DiffusionMap,missing gene_relevance(coords, exprs, ..., k = 20L, dims = 1:2, distance = NULL, smooth = TRUE, verbose = FALSE) # S4 method for matrix,matrix gene_relevance(coords, exprs, ..., k = 20L, dims = 1:2, distance = NULL, smooth = TRUE, verbose = FALSE)
coords | A |
---|---|
exprs | An cells \(\times\) genes |
... | If no |
k | Number of nearest neighbors to use |
dims | Index into columns of |
distance | Distance measure to use for the nearest neighbor search. |
smooth | Smoothing parameters |
verbose | If TRUE, log additional info to the console |
A GeneRelevance
object:
coords
A cells \(\times\) dims matrix
or sparseMatrix
of coordinates (e.g. diffusion components), reduced to the dimensions passed as dims
exprs
A cells \(\times\) genes matrix of expressions
partials
Array of partial derivatives wrt to considered dimensions in reduced space (genes \(\times\) cells \(\times\) dimensions)
partials_norm
Matrix with norm of aforementioned derivatives. (n\_genes \(\times\) cells)
nn_index
Matrix of k nearest neighbor indices. (cells \(\times\) k)
dims
Column index for plotted dimensions. Can character
, numeric
or logical
distance
Distance measure used in the nearest neighbor search. See find_knn
smooth_window
Smoothing window used (see smth.gaussian
)
smooth_alpha
Smoothing kernel width used (see smth.gaussian
)
Gene Relevance methods, Gene Relevance plotting: plot_differential_map
/plot_gene_relevance
data(guo_norm) dm <- DiffusionMap(guo_norm) gr <- gene_relevance(dm) m <- t(Biobase::exprs(guo_norm)) gr_pca <- gene_relevance(prcomp(m)$x, m) # now plot them!