filter.features {SIAMCAT} | R Documentation |
This function performs unsupervised feature filtering. Features can be filtered based on abundance or prevalence. Additionally, unmapped reads may be removed.
filter.features(siamcat, filter.method = "abundance", cutoff = 0.001, rm.unmapped = TRUE, feature.type='original', verbose = 1)
siamcat |
an object of class siamcat-class |
filter.method |
method used for filtering the features, can be one of
these: |
cutoff |
float, abundace or prevalence cutoff, default to |
rm.unmapped |
boolean, should unmapped reads be discarded?, defaults to
|
feature.type |
On which type of features should the function work? Can be either "original", "filtered", or "normalized". Please only change this paramter if you know what you are doing! |
verbose |
control output: |
This function filters the features in a siamcat-class object in a unsupervised manner.
The different filter methods work in the following way:
'abundace'
remove features whose maximum abundance is never
above the threshold value in any of the samples
'cum.abundance'
remove features with very low abundance
in all samples i.e. ones that are never among the most abundant
entities that collectively make up (1-cutoff) of the reads in
any sample
'prevalence'
remove features with low prevalence across
samples i.e. ones that are 0 (undetected) in more than (1-cutoff)
proportion of samples.
Features can also be filtered repeatedly with different methods, e.g. first using the maximum abundance filtering and then using prevalence filtering. However, if a filtering method has already been applied to the dataset, SIAMCAT will default back on the original features for filtering.
siamcat an object of class siamcat-class
# Example dataset data(siamcat_example) # Simple examples siamcat_filtered <- filter.features(siamcat_example, filter.method='abundance', cutoff=1e-03)