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-class and
NMFfitXn-class, 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.
proc_time that contains CPU
times required to perform all the runs.
signature(object = "NMFfitX"):
Plots a heatmap of the basis matrix of the best fit in
object.
signature(object = "NMFfitX"):
Plots a heatmap of the coefficient matrix of the best fit
in object.
This method adds:
'consensus:', that shows the consensus cluster
associated to each sample. '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. signature(object = "NMFfitX"):
Pure virtual method defined to ensure consensus is
defined for sub-classes of NMFfitX. It throws an
error if called.
signature(object = "NMFfitX"):
Compute the hierarchical clustering on the consensus
matrix of object, or on the connectivity matrix of
the best fit in object.
signature(object = "NMFfitX"):
Plots a heatmap of the consensus matrix obtained when
fitting an NMF model with multiple runs.
signature(object = "NMFfitX"):
Computes the cophenetic correlation coefficient on the
consensus matrix of object. All arguments in
... are passed to the method
cophcor,matrix.
signature(object = "NMFfitX"):
Returns the deviance achieved by the best fit object,
i.e. the lowest deviance achieved across all NMF runs.
signature(object = "NMFfitX"):
Computes the dispersion on the consensus matrix obtained
from multiple NMF runs.
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.
signature(object = "NMFfitX"):
Returns the RNG settings used for the first NMF run of
multiple NMF runs.
signature(object = "NMFfitX"):
Method for multiple NMF fit objects, which returns the
indexes of fixed basis terms from the best fitted model.
signature(object = "NMFfitX"):
Deprecated method subsituted by
consensusmap.
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.
signature(x = "NMFfitX", y =
"NMF"): Compares two NMF models when at least one comes
from multiple NMF runs.
signature(object = "NMFfitX"):
Provides a way to aggregate NMFfitXn objects into
an NMFfitX1 object.
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.
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.
signature(object = "NMFfitX"):
Returns the residuals achieved by the best fit object,
i.e. the lowest residual approximation error achieved
across all NMF runs.
signature(object = "NMFfitX"):
Returns the CPU time required to compute all the NMF
runs. It returns NULL if no CPU data is available.
signature(object = "NMFfitX"): Show
method for objects of class NMFfitX
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-class 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).
# 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
## <Object of class: NMFfitX1 >
## Method: brunet
## Runs: 3
## RNG:
## 407L, 1640873474L, -2107031141L, 137979936L, -396230239L, 305232622L, -1882487145L
## Total timing:
## user system elapsed
## 1.816 0.308 1.683
# 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
## <Object of class: NMFfitXn >
## Method: brunet
## Runs: 3
## RNG:
## 407L, -2007342784L, 680534081L, -313646834L, 541863479L, -376386292L, 812240541L
## Total timing:
## user system elapsed
## 1.628 0.292 1.334
## Sequential timing:
## user system elapsed
## 0.616 0.004 0.618