Commit 223215ea authored by Jakob Wirbel's avatar Jakob Wirbel

fix build error: reroxygenize.

parent c54b2bbf
......@@ -13,7 +13,7 @@ filter.features(siamcat, filter.method = "abundance",
\item{filter.method}{string, method used for filtering the features, can be
one of these: \code{c('abundance', 'cum.abundance', 'prevalence',
'variance')}, defaults to \code{'abundance'}}
'variance', 'pass')}, defaults to \code{'abundance'}}
\item{cutoff}{float, abundace, prevalence, or variance cutoff, defaults
to \code{0.001} (see Details below)}
......@@ -55,6 +55,8 @@ This function filters the features in a \link{siamcat-class}
in more than \code{1 - cutoff} percent of samples.
\item \code{'variance'} - remove features with low variance across
samples, i.e. those that have a variance lower than \code{cutoff}
\item \code{'pass'} - pass-through filtering will not change the
features
}
Features can also be filtered repeatedly with different methods, e.g.
......
......@@ -6,7 +6,7 @@
\usage{
normalize.features(siamcat,
norm.method = c("rank.unit", "rank.std",
"log.std", "log.unit", "log.clr", "pass"),
"log.std", "log.unit", "log.clr", "std", "pass"),
norm.param = list(log.n0 = 1e-06, sd.min.q = 0.1,
n.p = 2, norm.margin = 1),
feature.type='filtered',
......@@ -17,7 +17,7 @@ normalize.features(siamcat,
\item{norm.method}{string, normalization method, can be one of these:
'\code{c('rank.unit', 'rank.std', 'log.std', 'log.unit', 'log.clr',
'pass')}}
'std', 'pass')}}
\item{norm.param}{list, specifying the parameters of the different
normalization methods, see details for more information}
......@@ -40,7 +40,7 @@ This function performs feature normalization according to user-
specified parameters.
}
\details{
There are six different normalization methods available:
There are seven different normalization methods available:
\itemize{
\item \code{'rank.unit'} - converts features to ranks and normalizes
each column (=sample) by the square root of the sum of ranks
......@@ -52,16 +52,18 @@ There are six different normalization methods available:
pseudocounts) and applies z-score standardization
\item \code{'log.unit'} - log-transforms features (after addition of
pseudocounts) and normalizes by features or samples with different norms
\item \code{'std'} - z-score standardization without any other
transformation
\item \code{'pass'} - pass-through normalization will not change the
features}
The list entries in \code{'norm.param'} specify the normalzation parameters,
which are dependant on the normalization method of choice:
\itemize{
\item \code{'rank.unit'} does not require any other parameters
\item \code{'rank.std'} requires \code{sd.min.q}, quantile of the
distribution of standard deviations of all features that will be added
to the denominator during standardization in order to avoid
\item \code{'rank.unit' or 'pass'} does not require any other parameters
\item \code{'rank.std' and 'std'} requires \code{sd.min.q}, quantile
of the distribution of standard deviations of all features that will
be added to the denominator during standardization in order to avoid
underestimation of the standard deviation, defaults to 0.1
\item \code{'log.clr'} requires \code{log.n0}, which is the pseudocount
to be added before log-transformation, defaults to \code{NULL} leading
......
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