NMFfitX-class {NMF} | R Documentation

## Virtual Class to Handle Results from Multiple Runs of NMF Algorithms

### Description

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

### Details

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.

### Slots

runtime.all

  Object of class `proc_time` that contains CPU times required to perform all the runs.

### Methods

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`).

### See Also

Other multipleNMF: `NMFfitX1-class`, `NMFfitXn-class`

### Examples

    # 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)

    # perform multiple runs of one algorithm (keep all the fits)
    res <- nmf(V, 3, nrun=3, .options='k')
    res

[Package NMF version 0.20.6 Index]