Description Usage Arguments Details See Also Examples
Plotting Expression Profiles
When using NMF for clustering in particular, one looks for strong associations between the basis and a priori known groups of samples. Plotting the profiles may highlight such patterns.
1 2 3 4 5 6 7 
x 
a matrix or an NMF object from which is
extracted the mixture coefficient matrix. It is extracted
from the best fit if 
y 
a matrix or an NMF object from which is
extracted the mixture coefficient matrix. It is extracted
from the best fit if 
scale 
specifies how the data should be scaled
before plotting. If 
match.names 
a logical that indicates if the
profiles in 
legend 
a logical that specifies whether drawing
the legend or not, or coordinates specifications passed
to argument 
confint 
logical that indicates if confidence intervals for the Rsquared should be shown in legend. 
Colv 
specifies the way the columns of

labels 
a character vector containing labels for
each sample (i.e. each column of 
annotation 
a factor annotating each sample (i.e.
each column of 
... 
graphical parameters passed to

add 
logical that indicates if the plot should be added as points to a previous plot 
The function can also be used to compare the profiles from two NMF models or mixture coefficient matrices. In this case, it draws a scatter plot of the paired profiles.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  # create a random target matrix
v < rmatrix(40, 10)
# fit a single NMF model
res < nmf(v, 3)
profplot(res)
# fit a multirun NMF model
res2 < nmf(v, 3, nrun=2)
# ordering according to first profile
profplot(res2, Colv=1) # increasing
# draw a profile correlation plot: this show how the basis components are
# returned in an unpredictable order
profplot(res, res2)
# looking at all the correlations allow to order the components in a "common" order
profcor(res, res2)

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