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Description:
This script trains a supervised classifier using OTUs (or other continuous input sample x observation data) as predictors, and a mapping file column containing discrete values as the class labels.
It is recommended that you remove low-depth samples and rare OTUs before running this script. This can drastically reduce the run-time, and in many circumstances will not hurt performance. It is also recommended to perform rarefaction to control for sampling effort before running this script. For example, to rarefy at depth 200, then remove OTUs present in < 10 samples run:
single_rarefaction.py -i otu_table_filtered.txt -d 200 -o otu_table_rarefied200.txt filter_otus_from_otu_table.py -i otu_table_rarefied200.txt -s 10
For an overview of the application of supervised classification to microbiota, see PubMed ID 21039646.
This script requires that R be installed and in the search path. To install R visit: http://www.r-project.org/. Once R is installed, run R and excecute the command “install.packages(“randomForest”)”, then type q() to exit.
Usage: supervised_learning.py [options]
Input Arguments:
Note
[REQUIRED]
[OPTIONAL]
Output:
Outputs a ranking of features (e.g. OTUs) by importance, an estimation of the generalization error of the classifier, and the predicted class labels and posterior class probabilities according to the classifier.
Simple example of random forests classifier:
supervised_learning.py -i otu_table.biom -m Fasting_Map.txt -c BarcodeSequence -o ml
Running with 10-fold cross-validation for improved estimates of generalization error and feature importances:
supervised_learning.py -i otu_table.biom -m Fasting_Map.txt -c BarcodeSequence -o ml_cv10 -e cv10
Running with 1,000 trees for improved generalization error:
supervised_learning.py -i otu_table.biom -m Fasting_Map.txt -c BarcodeSequence -o ml_ntree1000 --ntree 1000