Fizzy is a feature subset selection tool (publication link) that uses FEAST in the background to run feature selection on biological data formats. Fizzy processes BIOM and mapping files, which are a standard in metagenomics data analysis. fizzy will run feature selection on taxonomic abundances obtained from the biom file given a set of categorical labels obtained form the the mapping file. npfs is a subset selection tool that provides inference on the number of features that are deemed as important. Note that if you do not specify a map file than it is assumed that the labels (indicated by the --label option) is stored in the metadata of the BIOM file for each of the samples in the data. Check out the tutorial to see fizzy and npfs in action.
Do not use any version of Fizzy prior to v1.2.0.
fizzy and npfs have a couple of dependencies, which are:
Note, that you'll need to install MIToolbox and FEAST for PyFeast. After installing the dependencies open a shell and run:
sudo make installFizzy implements a suite of information theoretic subset selection objective functions. The objective function can be changed with the -f flag. Note that Fizzy requires the number of OTUs to be selected is specified in advance; however, NPFS can provide statistical inference on the number of important variables in the data.
usage: fizzy [-h] [-n SELECT] [-q BINS] -l LABEL [-f FS_METHOD] -i INPUT_FILE
[-m MAP_FILE] [-j] -o OUTPUT_FILE [-r OUTPUT_BIOM] [-v]
Fizzy implements feature subset selection for biological data formats, which
are commonly used in metagenomic data analysis.
optional arguments:
-h, --help show this help message and exit
-n SELECT, --select SELECT
number of features to select
-q BINS, --bins BINS number of bins to histogram the data
-l LABEL, --label LABEL
name of column of the mapping file that indicates the
labels
-f FS_METHOD, --fs-method FS_METHOD
Feature selection method. Available: CIFE CMIM CondMI
Condred ICAP JMI MIM MIFS mRMR
-i INPUT_FILE, --input-file INPUT_FILE
biom format file
-m MAP_FILE, --map-file MAP_FILE
map file (tsv)
-j, --json store the output as a json format file.
-o OUTPUT_FILE, --output-file OUTPUT_FILE
output file where selected OTU IDs and averages of the
OTUS for each class. are stored
-r OUTPUT_BIOM, --output-biom OUTPUT_BIOM
output a BIOM file with the relative abundances of the
sub-matrix of the selected features.
-v, --svf the input file is a separated variables fileNPFS is applied as a wrapper to any feature subset selection algorithm, regardless of the obbjective function criteria used by that algorithm, to determine whether a feature belongs in the relevant set. Perhaps more importantly, this procedure efficiently determines the number of relevant features given an initial starting point. Refer to the reference shown below for the details of NPFS.
usage: npfs [-h] [-a ALPHA] [-b BOOTSTRAPS] [-c CPUS] [-f FS_METHOD] -i
INPUT_FILE [-j] [-q BINS] -l LABEL [-m MAP_FILE] [-n SELECT] -o
OUTPUT_FILE [-r OUTPUT_BIOM] [-v]
NPFS implements feature subset selection for biological data formats, which
are commonly used in metagenomic data analysis.
optional arguments:
-h, --help show this help message and exit
-a ALPHA, --alpha ALPHA
size of the Neyman-Pearson hypothesis test
-b BOOTSTRAPS, --bootstraps BOOTSTRAPS
number of bootstraps to run
-c CPUS, --cpus CPUS the number of bootstraps to run simultaneously
-f FS_METHOD, --fs-method FS_METHOD
Feature selection method. Available: CIFE CMIM CondMI
Condred ICAP JMI MIM MIFS mRMR
-i INPUT_FILE, --input-file INPUT_FILE
biom format file
-j, --json store the output as a json format file.
-q BINS, --bins BINS number of bins to histogram the data
-l LABEL, --label LABEL
name of column of the mapping file that indicates the
labels
-m MAP_FILE, --map-file MAP_FILE
map file (tsv)
-n SELECT, --select SELECT
number of features to start with
-o OUTPUT_FILE, --output-file OUTPUT_FILE
output file where selected OTU IDsare stored
-r OUTPUT_BIOM, --output-biom OUTPUT_BIOM
output a BIOM file with the relative abundances of the
sub-matrix of the selected features.
-v, --svf the input file is a separated variables fileusage: lasso [-h] -l LABEL -i INPUT_FILE [-m MAP_FILE] [-j] -o OUTPUT_FILE
[-r OUTPUT_BIOM] [-q WEIGHTS] [-v] [-c CONFIG]
lasso: error: argument -l/--label is required
Gregorys-MacBook-Pro:Fizzy gditzler$ lasso -h
usage: lasso [-h] -l LABEL -i INPUT_FILE [-m MAP_FILE] [-j] -o OUTPUT_FILE
[-r OUTPUT_BIOM] [-q WEIGHTS] [-v] [-c CONFIG]
Fizzy implements feature subset selection for biological data formats, which
are commonly used in metagenomic data analysis.
optional arguments:
-h, --help show this help message and exit
-l LABEL, --label LABEL
name of column of the mapping file that indicates the
labels
-i INPUT_FILE, --input-file INPUT_FILE
biom format file
-m MAP_FILE, --map-file MAP_FILE
map file (tsv)
-j, --json store the output as a json format file.
-o OUTPUT_FILE, --output-file OUTPUT_FILE
output file where selected OTU IDs and averages of the
OTUS for each class. are stored
-r OUTPUT_BIOM, --output-biom OUTPUT_BIOM
output a BIOM file with the relative abundances of the
sub-matrix of the selected features.
-q WEIGHTS, --weights WEIGHTS
output a feature file with the lasso weights
-v, --svf the input file is a separated variables file
-c CONFIG, --config CONFIG
file path to lasso config file. see sklearn's
documentation on the options available to the user.A general useage example for Fizzy would be something along the lines of
fizzy -i my-data.biom -o results.txt -m mapping-file.txt -f JMI -l BODY-SITE -n 100where BODY-SITE is a column in the mapping file. The output file is save in JSON format with the fields that are the classes and a key called Features. The Features key contains the OTUs that were selected as relevant. Each of the class keys contains the average relative abundance of the OTUs that were selected. The user has contol over the output format (either TSV or JSON) by setting, or not setting the json flag.
Also, check out the IPython notebook for a general Fizzy and NPFS usage example.
You can also with with CSV files as long as the file conforms to a strict standard. The csv file containing the abundances should look something like:
otu,sample1,sample2,sample3,sample4
otu1,0,0,1,1
otu2,2,2,1,1
otu3,1,2,2,1
otu4,1,1,1,1
otu5,2,2,2,2where there first row contains the feature identifier followed by the sample IDs. Each subsequent row contains the feature name followed by the feature values for each sample. Not sticking to this format could cause errors in the interpretation of the results. You'll need a corresponding map file to identify the class labels.
#SampleID Class
sample1 0
sample2 0
sample3 1
sample4 1Calling fizzy or npfs is pretty much the same, just set the -v flag.
Gregorys-MacBook-Pro-2:Fizzy gditzler$ fizzy -n 2 -l Class -f MIM -m test.map -i test.csv -s -o test.ouput -r test.biom -vThe output selects otu1 and otu2, which makes sense because they are the only variables that carry information about the class label.
Gregorys-MacBook-Pro-2:src gditzler$ cat test.ouput
Features Feature IDs 1 (std) 0 (mean) 1 (mean) 0 (std)
otu1 Feature1 0.0119047619048 0.0 0.154761904762 0.0
otu2 Feature2 0.0119047619048 0.309523809524 0.154761904762 0.0238095238095Note that the data in the csv file MUST be discretized into bins as per the FEAST documentation.
A config file might look something like the one below. This is an exhaustive list. A config file is not required if the system defaults are acceptable.
fit_intercept:True:bool
normalize:True:bool
precompute:auto:string
copy_X:True:bool
max_iter:5000:int
tol:0.000001:float
cut_off:0.00001:floatalpha,fit_intercept,normalize,precompute,copy_X,max_iter,tol,warm_start,positive: Scikit-learn's Lasso parameters. Refer the Scikit-learn's documentation.cut_off: threshold for determining which weights are zero.
- Gregory Ditzler, J. Calvin Morrison, Yemin Lan and Gail L. Rosen. "Fizzy: feature subset selection for metagenomics," BMC Bioinformatics, vol. 16, no. 358, 2015. (link)
- Gregory Ditzler, Robi Polikar, and Gail Rosen, "A bootstrap based Neyman-Pearson test for identifying variable importance," IEEE Transactions on Neural Networks and Learning Systems, 2014, In Press. (link)
- Gavin Brown, Adam Pocock, Ming-Jie Zhao, and Mikel Lujan, “Conditional likelihood maximisation: A unifying framework ´ for information theoretic feature selection,” Journal of Machine Learning Research, vol. 13, pp. 27–66, 2012. (link)