Description Details Slots Methods See Also Examples

This class defines a common interface to handle the
results from multiple runs of a single NMF algorithm,
performed with the `nmf`

method.

Currently, this interface is implemented by two classes,
`NMFfitX1`

and
`NMFfitXn`

, which respectively handle
the case where only the best fit is kept, and the case
where the list of all the fits is returned.

See `nmf`

for more details on the method
arguments.

- runtime.all
Object of class

`proc_time`

that contains CPU times required to perform all the runs.

- basismap
`signature(object = "NMFfitX")`

: Plots a heatmap of the basis matrix of the best fit in`object`

.- 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`

.

- consensus
`signature(object = "NMFfitX")`

: Pure virtual method defined to ensure`consensus`

is defined for sub-classes of`NMFfitX`

. It throws an error if called.- consensushc
`signature(object = "NMFfitX")`

: Compute the hierarchical clustering on the consensus matrix of`object`

, or on the connectivity matrix of the best fit in`object`

.- consensusmap
`signature(object = "NMFfitX")`

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

: Computes the cophenetic correlation coefficient on the consensus matrix of`object`

. All arguments in`...`

are passed to the method`cophcor,matrix`

.- deviance
`signature(object = "NMFfitX")`

: Returns the deviance achieved by the best fit object, i.e. the lowest deviance achieved across all NMF runs.- dispersion
`signature(object = "NMFfitX")`

: Computes the dispersion on the consensus matrix obtained from multiple NMF runs.- fit
`signature(object = "NMFfitX")`

: Returns the model object that achieves the lowest residual approximation error across all the runs.It is a pure virtual method defined to ensure

`fit`

is defined for sub-classes of`NMFfitX`

, which throws an error if called.- getRNG1
`signature(object = "NMFfitX")`

: Returns the RNG settings used for the first NMF run of multiple NMF runs.- ibterms
`signature(object = "NMFfitX")`

: Method for multiple NMF fit objects, which returns the indexes of fixed basis terms from the best fitted model.- metaHeatmap
`signature(object = "NMFfitX")`

: Deprecated method subsituted by`consensusmap`

.- minfit
`signature(object = "NMFfitX")`

: Returns the fit object that achieves the lowest residual approximation error across all the runs.It is a pure virtual method defined to ensure

`minfit`

is defined for sub-classes of`NMFfitX`

, which throws an error if called.- nmf.equal
`signature(x = "NMFfitX", y = "NMF")`

: Compares two NMF models when at least one comes from multiple NMF runs.- NMFfitX
`signature(object = "NMFfitX")`

: Provides a way to aggregate`NMFfitXn`

objects into an`NMFfitX1`

object.- nrun
`signature(object = "NMFfitX")`

: Returns the number of NMF runs performed to create`object`

.It is a pure virtual method defined to ensure

`nrun`

is defined for sub-classes of`NMFfitX`

, which throws an error if called.See

`nrun,NMFfitX-method`

for more details.- predict
`signature(object = "NMFfitX")`

: Returns the cluster membership index from an NMF model fitted with multiple runs.Besides the type of clustering available for any NMF models (

`'columns', 'rows', 'samples', 'features'`

), this method can return the cluster membership index based on the consensus matrix, computed from the multiple NMF runs.See

`predict,NMFfitX-method`

for more details.- residuals
`signature(object = "NMFfitX")`

: Returns the residuals achieved by the best fit object, i.e. the lowest residual approximation error achieved across all NMF runs.- runtime.all
`signature(object = "NMFfitX")`

: Returns the CPU time required to compute all the NMF runs. It returns`NULL`

if no CPU data is available.- show
`signature(object = "NMFfitX")`

: Show method for objects of class`NMFfitX`

- summary
`signature(object = "NMFfitX")`

: Computes a set of measures to help evaluate the quality of the*best fit*of the set. The result is similar to the result from the`summary`

method of`NMFfit`

objects. See`NMF`

for details on the computed measures. In addition, the cophenetic correlation (`cophcor`

) and`dispersion`

coefficients of the consensus matrix are returned, as well as the total CPU time (`runtime.all`

).

Other multipleNMF: `NMFfitX1-class`

,
`NMFfitXn-class`

1 2 3 4 5 6 7 8 9 10 | ```
# generate a synthetic dataset with known classes
n <- 20; counts <- c(5, 2, 3);
V <- syntheticNMF(n, counts)
# perform multiple runs of one algorithm (default is to keep only best fit)
res <- nmf(V, 3, nrun=3)
res
# plot a heatmap of the consensus matrix
## Not run: consensusmap(res)
``` |

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