heatmaps: Heatmaps of NMF Factors
In renozao/NMF: Algorithms and Framework for Nonnegative Matrix Factorization (NMF)

Description Usage Arguments Details Methods (by generic) References Examples

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.

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 'NMF'
consensusmap(object, ...)

## 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'
basismap(object, ...)

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

`object`	an object from which is extracted NMF factors or a consensus matrix
`...`	extra arguments passed 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'. RColorBrewer palette names (see `display.brewer.all`) `viridis` palette names: 'viridis', 'inferno', 'plasma', 'magma'; one of 'RdYlBu2', 'rainbow', 'heat', 'topo', 'terrain', 'cm'. When the colour 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 `"stdrow"`: center and standardize each row separately to row Z-scores, and force values onto `[0,1]` inteval. `"column"`: center and standardize each column separately to column Z-scores `"stdcolumn"`: center and standardize each column separately to column Z-scores, and force values onto `[0,1]` inteval. `"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. See section Row/column ordering and display for details on all supported values.
`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 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 section Row/column ordering and display for details on all supported values.
`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 Kim and Park (2007). 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.
`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.
`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.
`main`	Main title as a character string or a grob.
`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`.
`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

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.

basismap:

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

coefmap:

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

consensusmap(object = NMF.rank): Draw a single plot with a heatmap of the consensus matrix obtained for each value of the rank, in the range tested with nmfEstimateRank.
consensusmap(object = list): Draw a single plot with a heatmap of the consensus matrix of each element in the list object.
consensusmap(object = NMFfitX): Plots a heatmap of the consensus matrix obtained when fitting an NMF model with multiple runs.
consensusmap(object = NMF): Plots a heatmap of the connectivity matrix of an NMF model.
consensusmap(object = matrix): Main method that redefines default values for arguments of aheatmap.

Kim H, Park H (2007). “Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis.” _Bioinformatics (Oxford, England)_, *23*(12), 1495-502. ISSN 1460-2059, doi: 10.1093/bioinformatics/btm134 (URL: https://doi.org/10.1093/bioinformatics/btm134).

## 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)


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

## End(Not run)


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

## End(Not run)


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

## End(Not run)