heatmap-NMF {NMF} | R Documentation

## Heatmaps of NMF Factors

### Description

The NMF package ships an advanced heatmap engine implemented by the function `aheatmap`. Some convenience heatmap functions have been implemented for NMF models, which redefine default values for some of the arguments of `aheatmap`, hence tuning the output specifically for NMF models.

### Usage

      basismap(object, ...)

      ## S4 method for signature 'NMF'
    basismap(object, color = "YlOrRd:50",
        scale = "r1", Rowv = TRUE, Colv = NA,
        subsetRow = FALSE, annRow = NA, annCol = NA,
        tracks = "basis", main = "Basis components",
        info = FALSE, ...)

      coefmap(object, ...)

      ## S4 method for signature 'NMF'
    coefmap(object, color = "YlOrRd:50",
        scale = "c1", Rowv = NA, Colv = TRUE, annRow = NA,
        annCol = NA, tracks = "basis",
        main = "Mixture coefficients", info = FALSE, ...)

      consensusmap(object, ...)

      ## S4 method for signature 'NMFfitX'
    consensusmap(object, annRow = NA,
        annCol = NA,
        tracks = c("basis:", "consensus:", "silhouette:"),
        main = "Consensus matrix", info = FALSE, ...)

      ## S4 method for signature 'matrix'
    consensusmap(object,
        color = "-RdYlBu",
        distfun = function(x) as.dist(1 - x),
        hclustfun = "average", Rowv = TRUE, Colv = "Rowv",
        main = if (is.null(nr) || nr > 1) "Consensus matrix" else "Connectiviy matrix",
        info = FALSE, ...)

      ## S4 method for signature 'NMFfitX'
    coefmap(object, Colv = TRUE,
        annRow = NA, annCol = NA,
        tracks = c("basis", "consensus:"), ...)

### Arguments

`object` | an object from which is extracted NMF factors or a consensus matrix
`...` | extra arguments passed to `aheatmap`.
`subsetRow` | Argument that specifies how to filter the rows that will appear in the heatmap. When `FALSE` (default), all rows are used. Besides the values supported by argument `subsetRow` of `aheatmap`, other possible values are: `TRUE`: only the rows that are basis-specific are used. The default selection method is from KimH2007. This is equivalent to `subsetRow='kim'`. a single `character` string or numeric value that specifies the method to use to select the basis-specific rows, that should appear in the heatmap (cf. argument `method` for function `extractFeatures`). Note `extractFeatures` is called with argument `nodups=TRUE`, so that features that are selected for multiple components only appear once.
`tracks` | Special additional annotation tracks to highlight associations between basis components and sample clusters: basis matches each row (resp. column) to the most contributing basis component in `basismap` (resp. `coefmap`). In `basismap` (resp. `coefmap`), adding a track `':basis'` to `annCol` (resp. `annRow`) makes the column (resp. row) corresponding to the component being also highlited using the mathcing colours.
`info` | if `TRUE` then the name of the algorithm that fitted the NMF model is displayed at the bottom of the plot, if available. Other wise it is passed as is to `aheatmap`.
`color` | colour specification for the heatmap. Default to palette '-RdYlBu2:100', i.e. reversed palette 'RdYlBu2' (a slight modification of RColorBrewer's palette 'RdYlBu') with 100 colors. Possible values are: a character/integer vector of length greater than 1 that is directly used and assumed to contain valid R color specifications. a single color/integer (between 0 and 8)/other numeric value that gives the dominant colors. Numeric values are converted into a pallete by `rev(sequential_hcl(2, h = x, l = c(50, 95)))`. Other values are concatenated with the grey colour '#F1F1F1'. one of RColorBrewer's palette name (see `display.brewer.all`) , or one of 'RdYlBu2', 'rainbow', 'heat', 'topo', 'terrain', 'cm'. When the coluor palette is specified with a single value, and is negative or preceded a minus ('-'), the reversed palette is used. The number of breaks can also be specified after a colon (':'). For example, the default colour palette is specified as '-RdYlBu2:100'.
`scale` | character indicating how the values should scaled in either the row direction or the column direction. Note that the scaling is performed after row/column clustering, so that it has no effect on the row/column ordering. Possible values are: `"row"`: center and standardize each row separately to row Z-scores `"column"`: center and standardize each column separately to column Z-scores `"r1"`: scale each row to sum up to one `"c1"`: scale each column to sum up to one `"none"`: no scaling
`Rowv` | clustering specification(s) for the rows. It allows to specify the distance/clustering/ordering/display parameters to be used for the rows only. Possible values are: `TRUE` or `NULL` (to be consistent with `heatmap`): compute a dendrogram from hierarchical clustering using the distance and clustering methods `distfun` and `hclustfun`. `NA`: disable any ordering. In this case, and if not otherwise specified with argument `revC=FALSE`, the heatmap shows the input matrix with the rows in their original order, with the first row on top to the last row at the bottom. Note that this differ from the behaviour or `heatmap`, but seemed to be a more sensible choice when vizualizing a matrix without reordering. an integer vector of length the number of rows of the input matrix (`nrow(x)`), that specifies the row order. As in the case `Rowv=NA`, the ordered matrix is shown first row on top, last row at the bottom. a character vector or a list specifying values to use instead of arguments `distfun`, `hclustfun` and `reorderfun` when clustering the rows (see the respective argument descriptions for a list of accepted values). If `Rowv` has no names, then the first element is used for `distfun`, the second (if present) is used for `hclustfun`, and the third (if present) is used for `reorderfun`. a numeric vector of weights, of length the number of rows of the input matrix, used to reorder the internally computed dendrogram `d` by `reorderfun(d, Rowv)`. `FALSE`: the dendrogram is computed using methods `distfun`, `hclustfun`, and `reorderfun` but is not shown. a single integer that specifies how many subtrees (i.e. clusters) from the computed dendrogram should have their root faded out. This can be used to better highlight the different clusters. a single double that specifies how much space is used by the computed dendrogram. That is that this value is used in place of `treeheight`.
`Colv` | clustering specification(s) for the columns. It accepts the same values as argument `Rowv` (modulo the expected length for vector specifications), and allow specifying the distance/clustering/ordering/display parameters to be used for the columns only. `Colv` may also be set to `"Rowv"`, in which case the dendrogram or ordering specifications applied to the rows are also applied to the columns. Note that this is allowed only for square input matrices, and that the row ordering is in this case by default reversed (`revC=TRUE`) to obtain the diagonal in the standard way (from top-left to bottom-right). See argument `Rowv` for other possible values.
`annRow` | specifications of row annotation tracks displayed as coloured columns on the left of the heatmaps. The annotation tracks are drawn from left to right. The same conversion, renaming and colouring rules as for argument `annCol` apply.
`annCol` | specifications of column annotation tracks displayed as coloured rows on top of the heatmaps. The annotation tracks are drawn from bottom to top. A single annotation track can be specified as a single vector; multiple tracks are specified as a list, a data frame, or an `ExpressionSet` object, in which case the phenotypic data is used (`pData(eset)`). Character or integer vectors are converted and displayed as factors. Unnamed tracks are internally renamed into `Xi`, with i being incremented for each unamed track, across both column and row annotation tracks. For each track, if no corresponding colour is specified in argument `annColors`, a palette or a ramp is automatically computed and named after the track's name.
`main` | Main title as a character string or a grob.
`distfun` | default distance measure used in clustering rows and columns. Possible values are: all the distance methods supported by `dist` (e.g. "euclidean" or "maximum"). all correlation methods supported by `cor`, such as `"pearson"` or `"spearman"`. The pairwise distances between rows/columns are then computed as `d <- dist(1 - cor(..., method = distfun))`. One may as well use the string "correlation" which is an alias for "pearson". an object of class `dist` such as returned by `dist` or `as.dist`.
`hclustfun` | default clustering method used to cluster rows and columns. Possible values are: a method name (a character string) supported by `hclust` (e.g. `'average'`). an object of class `hclust` such as returned by `hclust` a dendrogram

### Details

IMPORTANT: although they essentially have the same set of arguments, their order sometimes differ between them, as well as from `aheatmap`. We therefore strongly recommend to use fully named arguments when calling these functions.

`basimap` default values for the following arguments of `aheatmap`:

- the color palette;

- the scaling specification, which by default scales each row separately so that they sum up to one (`scale='r1'`);

- the column ordering which is disabled;

- allowing for passing feature extraction methods in argument `subsetRow`, that are passed to `extractFeatures`. See argument description here and therein.

- the addition of a default named annotation track, that shows the dominant basis component for each row (i.e. each feature).

  This track is specified in argument `tracks` (see its argument description). By default, a matching column annotation track is also displayed, but may be disabled using `tracks=':basis'`.

- a suitable title and extra information like the fitting algorithm, when `object` is a fitted NMF model.

`coefmap` redefines default values for the following arguments of `aheatmap`:

- the color palette;

- the scaling specification, which by default scales each column separately so that they sum up to one (`scale='c1'`);

- the row ordering which is disabled;

- the addition of a default annotation track, that shows the most contributing basis component for each column (i.e. each sample).

  This track is specified in argument `tracks` (see its argument description). By default, a matching row annotation track is also displayed, but can be disabled using `tracks='basis:'`.

- a suitable title and extra information like the fitting algorithm, when `object` is a fitted NMF model.

`consensusmap` redefines default values for the following arguments of `aheatmap`:

- the colour palette;

- the column ordering which is set equal to the row ordering, since a consensus matrix is symmetric;

- the distance and linkage methods used to order the rows (and columns). The default is to use 1 minus the consensus matrix itself as distance, and average linkage.

- the addition of two special named annotation tracks, `'basis:'` and `'consensus:'`, that show, for each column (i.e. each sample), the dominant basis component in the best fit and the hierarchical clustering of the consensus matrix respectively (using 1-consensus as distance and average linkage).

  These tracks are specified in argument `tracks`, which behaves as in `basismap`.

- a suitable title and extra information like the type of NMF model or the fitting algorithm, when `object` is a fitted NMF model.

### Methods

basismap

  `signature(object = "NMF")`: Plots a heatmap of the basis matrix of the NMF model `object`. This method also works for fitted NMF models (i.e. `NMFfit` objects).

basismap

  `signature(object = "NMFfitX")`: Plots a heatmap of the basis matrix of the best fit in `object`.

coefmap

  `signature(object = "NMF")`: The default method for NMF objects has special default values for some arguments of `aheatmap` (see argument description).

coefmap

  `signature(object = "NMFfitX")`: Plots a heatmap of the coefficient matrix of the best fit in `object`.

  This method adds:

  - an extra special column annotation track for multi-run NMF fits, `'consensus:'`, that shows the consensus cluster associated to each sample.

  - a column sorting schema `'consensus'` that can be passed to argument `Colv` and orders the columns using the hierarchical clustering of the consensus matrix with average linkage, as returned by `consensushc(object)`. This is also the ordering that is used by default for the heatmap of the consensus matrix as ploted by `consensusmap`.

consensusmap

  `signature(object = "NMFfitX")`: Plots a heatmap of the consensus matrix obtained when fitting an NMF model with multiple runs.

consensusmap

  `signature(object = "NMF")`: Plots a heatmap of the connectivity matrix of an NMF model.

consensusmap

  `signature(object = "matrix")`: Main method that redefines default values for arguments of `aheatmap`.

### Examples

    #----------
    # heatmap-NMF
    #----------
    ## More examples are provided in demo `heatmaps`
    ## Not run:
    demo(heatmaps)

    ## End(Not run)
    ##

    # random data with underlying NMF model
    v <- syntheticNMF(20, 3, 10)
    # estimate a model
    x <- nmf(v, 3)

    #----------
    # basismap
    #----------
    # show basis matrix
    basismap(x)
    ## Not run:
    # without the default annotation tracks
    basismap(x, tracks=NA)

    ## End(Not run)

    #----------
    # coefmap
    #----------
    # coefficient matrix
    coefmap(x)
    ## Not run:
    # without the default annotation tracks
    coefmap(x, tracks=NA)

    ## End(Not run)

    #----------
    # consensusmap
    #----------
    ## Not run:
    res <- nmf(x, 3, nrun=3)
    consensusmap(res)

    ## End(Not run)

[Package NMF version 0.20.6 Index]