GeneRelevance-class {destiny} | R Documentation

## Gene relevances for entire data set

### Description

The relevance map is cached insided of the `DiffusionMap`.

### Usage

    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)

### Arguments

`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`.

### Value

A `GeneRelevance` object:

### Slots

`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`)

### See Also

Gene Relevance methods, Gene Relevance plotting: `plot_differential_map`/`plot_gene_relevance`

### Examples

    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!

[Package destiny version 3.0.1 Index]