| Gene Relevance {destiny} | R Documentation |
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 signature 'DiffusionMap,missing' gene_relevance(coords, exprs, ..., k = 20L, dims = 1:2, distance = NULL, smooth = TRUE, verbose = FALSE) ## S4 method for signature '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:
coordsA cells \times dims matrix or sparseMatrix
of coordinates (e.g. diffusion components), reduced to the dimensions passed as dims
exprsA cells \times genes matrix of expressions
partialsArray of partial derivatives wrt to considered dimensions in reduced space (genes \times cells \times dimensions)
partials_normMatrix with norm of aforementioned derivatives. (n\_genes \times cells)
nn_indexMatrix of k nearest neighbor indices. (cells \times k)
dimsColumn index for plotted dimensions. Can character, numeric or logical
distanceDistance measure used in the nearest neighbor search. See find_knn
smooth_windowSmoothing window used (see smth.gaussian)
smooth_alphaSmoothing 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!