Gene-Relevance: Gene relevances for entire data set
In destiny: Creates diffusion maps

Description Usage Arguments Value Slots See Also Examples

The relevance map is cached insided of the DiffusionMap.

gene_relevance(
  coords,
  exprs,
  ...,
  k = 20L,
  dims = 1:2,
  distance = NULL,
  smooth = TRUE,
  remove_outliers = FALSE,
  verbose = FALSE
)

## S4 method for signature 'DiffusionMap,missing'
gene_relevance(
  coords,
  exprs,
  ...,
  k = 20L,
  dims = 1:2,
  distance = NULL,
  smooth = TRUE,
  remove_outliers = FALSE,
  verbose = FALSE
)

## S4 method for signature 'matrix,dMatrixOrMatrix'
gene_relevance(
  coords,
  exprs,
  ...,
  pcs = NULL,
  knn_params = list(),
  weights = 1,
  k,
  dims,
  distance,
  smooth,
  remove_outliers,
  verbose
)

`coords`	A `DiffusionMap` object or a cells \times dims `matrix`.
`exprs`	An cells \times genes `matrix`. Only provide if `coords` is no `DiffusionMap`.
`...`	Unused. All parameters to the right of the `...` have to be specified by name.
`k`	Number of nearest neighbors to use
`dims`	Index into columns of `coord`
`distance`	Distance measure to use for the nearest neighbor search.
`smooth`	Smoothing parameters `c(window, alpha)` (see `smth.gaussian`). Alternatively `TRUE` to use the smoother defaults or `FALSE` to skip smoothing,
`remove_outliers`	Remove cells that are only within one other cell's nearest neighbor, as they tend to get large norms.
`verbose`	If TRUE, log additional info to the console
`pcs`	A cell \times `n_pcs` matrix of principal components to use for the distances.
`knn_params`	A `list` of parameters for `find_knn`.
`weights`	Weights for the partial derivatives. A vector of the same length as `dims`.

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!