profplot | R Documentation |
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.
profplot(x, ...)
## Default S3 method:
profplot(x, y,
scale = c("none", "max", "c1"), match.names = TRUE,
legend = TRUE, confint = TRUE, Colv, labels,
annotation, ..., add = FALSE)
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 R-squared 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.
profcor
# create a random target matrix
v <- rmatrix(30, 10)
# fit a single NMF model
res <- nmf(v, 3)
profplot(res)
# fit a multi-run 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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