Bacterial Pathogenicity Classification via Sparse-SVM
Project description
bacpacs version 0.1.1
User's guide
Overview
bacpacs is a bacterial pathogenicity classification python module, based on the following paper: "BacPaCS – Bacterial Pathogenicity Classification via Sparse-SVM", by Eran Barash, Neta Sal-Man, Sivan Sabato, and Michal Ziv-Ukelson (Submitted). It can be used for classification using a pre-trained model, or for generating a new model from labeled training data of sequenced proteomes. The training pipeline:
-
bacpacs selects the 10% longest protein out of the set of all proteins from all training samples.
-
Using CD-HIT, bacpacs clusters the selected proteins. This results in clusters, or protein families (PFs). Each PF is represented by its longest protein.
-
For each organism in the training set, bacpacs compares the organism's proteins to the representatives of the PFs, using CD-HIT-2D. This results in a binary feature vector for each organism. The vector indices represent the different PFs. If a cell with index i has a value
True, the organism has a protein similar to the representative of PF i. Otherwise, the cell's value isFalse. -
The binary feature vectors of the organisms in the training set (and their known pathogenicity labels), can then be used to train a linear SVM model (using l1 norm as penalty). Other models can be trained as well.
Installation and dependencies
Bacpacs can be installed via one of the following three alternatives:
- Run in a linux terminal:
$ pip install bacpacs(recommended) - Clone or download bacpacs Github repository and run
pip install -e path/to/bacpacs - Clone or download bacpacs Github repository and use the command line interface described below.
Dependencies:
-
Operating system: Linux. CD-HIT only runs on Linux OS, and so does bacpacs.
-
CD-HIT: bacpacs requires CD-HIT to be installed. It is also recommended to add CD-HIT to the PATH variable. CD-HIT can be downloaded from its official website.
-
Python 2.7.
-
Python packages: numpy, scipy, scikit-learn, and biopython. If you are missing a package, you can simply install the package using pip or EasyInstall.
Running bacpacs as a python module
Below are detailed running examples of the two possible bacpacs schemes:
- Predicting data using bacpacs pre-trained model: bacpacs comes with a pre-trained model, used in the bacpacs paper. The pre-trained model can be easily downloaded and used.
- Training and using a model: bacpacs can also be used to translate a training set of organisms into a feature vector that can be fed into an SVM training module, and to then translate a test set into a feature vector which uses the same features as the training set. The pathogenicity of the organisms in the test set can then be predicted using the trained model. The organisms in both the training set and the test set are fed as raw amino acid fasta files (faa files).
- Trainig a model for prediction using the command line interface.
- Using bacpacs trained model for prediction via the command line interface.
The example code below should be used in Python 2.7. Full documentation of each of the methods appears in the code. This example code can be found in examples.
1. Predicting data using bacpacs pre-trained model
Imports
In[1]
import bacpacs
from sklearn.svm import LinearSVC
The bacpacs pre-trained model can be found in https://github.com/barashe/bacpacs/tree/master/trained. It can also be downloaded and loaded directly from Python as described below.
Load the pre-trained model
In[2]
bp, clf = bacpacs.load_trained_model(output_dir='out1')
Out[2]
Retrieving files from github
Downloading full_bacpacs.pkl
Downloading linearsvc_full.pkl
Downloading protein_families
load_trained_model() returns a Bacpacs object, and a sklearn.svm.LinearSVC trained classifier. We will need bp for feature extraction, and clf for the prediction itself. Here 'bacpacs' is an empty folder, and 'out1' is created when invoking load_trained_model(). These names can be set according to preference. An existing output_dir is acceptable as well, but beware of file overrun.
Download toy data
Use your real data, or download bacpacs toy data (also available on Github):
In[3]
bacpacs.download_toy_data('.')
Out[3]
Toy data stored in ./toy
You can move genomes from toy/train to toy/validate and vice versa, or even delete some. The destination folder can be set by replacing '.' with the desired destination.
Extract features for test organisms
This step takes a while, depending on your machine:
In[4]
bp.genomes_vs_pfs('toy/validate', n_jobs=0)
Out[4]
Running genomes against protein families representatives
Genome cluster files are stored in out1/pred_clusters
genomes_vs_pfs() uses CD-HIT-2D to run all test genomes against the pre-established protein families. Assigning n_jobs=0 tells CD-HIT-2D to use all available CPUs for each genome. However, the genomes are processed sequentially. Running genomes_vs_pfs() on several machines can save plenty of time.
Next, extract the features and store them in variables:
In[5]
X_pred = bp.extract_features(feats_type='pred')
Read pathogenicity labels from a csv file
bacpacs.read_labels() takes a csv file with two columns and no headers: the first column should list genome ids, and the second column should list pathogenicity labels. Genome ids should match the original genome file names. For example: for a genome file named org1.faa, the csv file should list an 'org1' genome id. Pathogenicity should be boolean: True for pathogens, False for non-pathogens. Note that we include the corresponding feature matrix (X_pred), to ensure that the order of the returned labels corresponds to the order of the genomes in the feature matrix.
In[6]
y_true = bacpacs.read_labels('toy/labels.csv', X=X_pred)
Predict pathogenicity
In[7]
y_pred = clf.predict(X_pred)
Compute accuracy:
In[8]
(y_pred == y_true).mean()
Out[8]
0.80000000000000004
Or simply:
In[9]
clf.score(X_pred, y_true)
Out[9]
0.80000000000000004
2. Training and using a model
Imports
In[1]
import bacpacs
from sklearn.svm import LinearSVC
Download toy data
Use your real data, or download the bacpacs toy data (also available on Github):
In[2]
bacpacs.download_toy_data('.')
Out[2]
Toy data stored in ./toy
Initialize a Bacpacs object
In[3]
bp = bacpacs.Bacpacs('out2')
Merge training data
Merge all training .faa files into one large .faa file.
In[4]
bp.merge_genome_files('toy/train/', output_path=None)
Specify an output path to override the default destination directory.
Out[4]
Saving merged proteins as out/merged.faa
Note that it is possible to specify a different output path.
Reduce the merged file to the 10% longest proteins
In[5]
bp.reduce(long_percent=10, merged_path=None, output_path=None)
Out[5]
Saving reduced proteins as out/reduced.faa
long_percent can be set to any value between 1 and 100. The number of selected proteins is rounded down if the requested percentage does not result in a whole number. The default merged_path is <output_directory>/merged.faa and the default output_path is <output_directory>/reduced.faa. Both can be set using the appropriate arguments of 'reduce'.
Cluster the training proteins into protein families
In[6]
bp.create_pfs(memory=800, n_jobs=0, cdhit_path=None, reduced_path=None, output_path=None)
Out[6]
Clustering genomes.
cd-hit -i out/reduced.faa -o out/protein_families -c 0.4 -n 2 -M 800 -T 0
Clustering finished successfully. Protein families dumped in out/protein_families
If CD-HIT is included in the system's path, there is no need to provide a path to 'cdhit_path'. If cd-hit is not included in the path, a valid path must be provided. Note that we are using n_jobs=0, to use all available CPUs.
Extract features
In[7]
bp.genomes_vs_pfs('toy/train/', feats_type='train', n_jobs=0)
Out[7]
Running genomes against protein families representatives
Genome cluster files are stored in out/train_clusters
In[8]
bp.genomes_vs_pfs('toy/validate/', feats_type='pred', n_jobs=0)
Out[8]
Running genomes against protein families representatives
Genome cluster files are stored in out/pred_clusters
In[9]
X_train = bp.extract_features(feats_type='train')
X_pred = bp.extract_features(feats_type='pred')
Read pathogenicity labels from a csv file
bacpacs.read_labels() takes a csv file with two columns and no headers: the first column should list genome ids, and the second column should list pathogenicity labels. Genome ids should match the original genome file names. For example: for a genome file named org1.faa, the csv file should list an 'org1' genome id. Pathogenicity should be boolean: True for pathogens, False for non-pathogens. Note that we include the corresponding feature matrices (X_train, X_pred), to ensure that the orders of the returned label sets correspond to the orders of the genomes in the feature matrices.
In[10]
y_train = bacpacs.read_labels('toy/labels.csv', X_train)
y_pred = bacpacs.read_labels('toy/labels.csv', X_pred)
Load a linear SVM object:
In[11]
clf = LinearSVC(penalty='l1', dual=False)
Fit it with the created features and labels:
In[12]
clf.fit(X_train, y_train)
Out[12]
LinearSVC(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, loss='squared_hinge', max_iter=1000,
multi_class='ovr', penalty='l1', random_state=None, tol=0.0001,
verbose=0)
Compute the accuracy pf the model's prediction:
In[13]
clf.score(X_pred, y_pred)
Out[13]
0.80000000000000004
3. Training a model for prediction using the command line interface
The flow of bacpacs using the command line, is very similar to using it as python module as described above. Typing
In[1]
python <path_to_bacpacs> bacpacs/pacpacs.py --h
produces:
Out[1]
usage: bacpacs.py [-h] -m
{merge,init,train,create_pfs,extract_feats,predict,reduce,genomes_vs_pfs}
-w WORKING_DIRECTORY [-i INPUT]
[--genome_input_dir GENOME_INPUT_DIR] [--pf_path PF_PATH]
[-o OUTPUT] [-t {pred,train}] [-r LONG_RATIO]
[-c CLUSTERS_DIR] [-f FEATS_PATH] [-l LABELS_PATH]
[--clf CLF] [--cdhit CDHIT] [--n_jobs N_JOBS]
[--memory MEMORY]
optional arguments:
-h, --help show this help message and exit
required arguments:
-m {merge,init,train,create_pfs,extract_feats,predict,reduce,genomes_vs_pfs},
--mode {merge,init,train,create_pfs,extract_feats,predict,reduce,genomes_vs_pfs}
bacpacs operating mode.
init: Initiates a bacpacs working directory. Will create
a "bp.json" file, which
stores previous operations history.
merge: Merges the raw training faa files.
Reduce: Selects the longest 10 precent proteins from the
merged fasta file.
create_pfs: Runs CD-HIT to cluster the merged and
reduced fasta file to protein families. genomes_vs_pf:
Creates feature vectors for training/predicting
genomes. Runs CD-HIT-2D for every genome, against the
previously created protein families.
extract_feats: Get features matrix X (pandas.DataFrame) for
training/prediction.
train: Trains a sklearn.svm.LinearSVC model on the extracted feats.
predict: Using either the trained classifier trained
in "train", or a classifier from a JSON file (created
by bacpacs.util.clf_to_json) a prediction is made and
stored in a csv file.
-w WORKING_DIRECTORY, --working_directory WORKING_DIRECTORY
Working directory in which bacpacs will cache files,
and store resulting features.
optional arguments:
-i INPUT, --input INPUT
Input file path
--genome_input_dir GENOME_INPUT_DIR
Working directory in which bacpacs will cache files,
and store resulting features.
--pf_path PF_PATH Path to protein families file, created in
"create_pfs". Applies to "pf_vs_genomes". If not
specified, the last created pf_file is used.
-o OUTPUT, --output OUTPUT
Output file path. Applies to all modes except "init".
If not specified, default paths in the working
directory are used and printed to the screen.
-t {pred,train}, --feats_type {pred,train}
Indication whether genomes are used for training, or
for prediction. Applies to "genomes_vs_pfs" and
"extract_feats"
-r LONG_RATIO, --long_ratio LONG_RATIO
Ratio of long proteins to use in "reduce".
-c CLUSTERS_DIR, --clusters_dir CLUSTERS_DIR
Path to training/prediction (defined in --feats_type)
clusters. Applies to "extract_feats". If not
specified, the directory used by "genomes_vs_pfs" to
store clusters is used.
-f FEATS_PATH, --feats_path FEATS_PATH
Path to training/prediction csv file. Applies to
"train" and "predict". If not specified, the path used
to store features in "extract_feats" is used.
-l LABELS_PATH, --labels_path LABELS_PATH
Path to labels csv file. Applies to "train".
--clf CLF Path to scikit-learn classifier, stored in JSON
format, using bacpacs.util.clf_to_json. If not
supplied, a new sklearn.svm.LinearSVC is used.
--cdhit CDHIT Path to CD-HIT. Only required if CD-HIT not in
environmental path. Applies to "create_pfs" and
"genomes_vf_pfs".
--n_jobs N_JOBS Number of threads for CD-HIT-2D. 0 to use all CPUs.
Applies to "create_pfs" and "genomes_vf_pfs".
--memory MEMORY Memory limit (in MB) for CD-HIT, 0 for unlimited.
Applies to "create_pfs" and "genomes_vf_pfs".
Initialize a working directory
In[1]
$ python <path_to_bacpacs>/bacpacs.py -w my_bp_dir
Out[1]
bacpacs initialized in my_bp_dir
-w is the working directory of this bacpacs run. bp.json is stored in the working directory, and it holds information from all running steps. The working directory is a required argument, which needs to be passed on each operation.
Merge training data
Merge all training .faa files into one large .faa file.
The bacpacs project includes a toy.tar.gz file. Untar it using tar -xzvf toy.tar.gz <destination>. You could replace <destination>
with my_bp_dir/toy. We'll use the toy set for the rest of the running example.
In[2]
$ python <path_to_bacpacs>/bacpacs.py -w my_bp_dir -m merge --genome_input_dir my_bp_dir/toy/train/
Specify an output path to override the default destination directory.
Out[2]
Saving merged proteins as my_bp_dir/merged.faa
Note that it is possible to specify a different output path.
Reduce the merged file to the 10% longest proteins
In[3]
$ python <path_to_bacpacs>/bacpacs.py -w my_bp_dir -m reduce -r 10
Out[3]
Saving reduced proteins as my_bp_dir/reduced.faa
-r can be set to any value between 1 and 100. The number of selected proteins is rounded down if the requested percentage does not result in a whole number.
The default merged_path is <working_directory>/merged.faa and the default output_path is <working_directory>/reduced.faa. Both can be set using --input and --output.
Cluster the training proteins into protein families
In[4]
$ python <path_to_bacpacs>/bacpacs.py -w my_bp_dir -m create_pfs --memory 800 --n_jobs 0
Out[4]
Clustering genomes.
cd-hit -i out/reduced.faa -o out/protein_families -c 0.4 -n 2 -M 800 -T 0
Clustering finished successfully. Protein families dumped in my_bp_dir/protein_families
If CD-HIT is included in the system's path, there is no need to provide a path to CD-HIT. If cd-hit is not included in the path, a valid path must be provided using --cdhit.
Note that we are using n_jobs=0, to use all available CPUs.
Create and extract features
In[5]
$ python <path_to_bacpacs>/bacpacs.py -w my_bp_dir -m genomes_vs_pfs --genome_input_dir my_bp_dir/toy/train -t train
Out[5]
Running genomes against protein families representatives
Genome cluster files are stored in my_bp_dir/train_clusters
In[6]
$ python <path_to_bacpacs>/bacpacs.py -w my_bp_dir -m genomes_vs_pfs --genome_input_dir my_bp_dir/toy/validate -t pred
Out[6]
Running genomes against protein families representatives
Genome cluster files are stored in my_bp_dir/pred_clusters
In[7]
$ python <path_to_bacpacs>/bacpacs.py -w my_bp_dir -m extract_feats -t train
Out[7]
Training feats stored in my_bp_dir/train_feats.csv
In[8]
$ python <path_to_bacpacs>/bacpacs.py -w my_bp_dir -m extract_feats -t pred
Out[8]
Prediction feats stored in my_bp_dir/pred_feats.csv
Train a Linear SVM classifier
In[9]
$ python <path_to_bacpacs>/bacpacs.py -w my_bp_dir -m train --labels_path my_bp_dir/toy/labels.csv
Note the argument --labels_path (or -l in short). The provided path contains a csv file with two columns and no headers: the first column should list genome ids, and the second column should list pathogenicity labels. Genome ids should match the original genome file names. For example: for a genome file named org1.faa, the csv file should list an 'org1' genome id. Pathogenicity should be boolean: True for pathogens, False for non-pathogens.
Out[9]
Trained classifier is stored at my_bp_dir/trained_clf.json
Predict the validation set pathogenicity labels
In[10]
$ python <path_to_bacpacs>/bacpacs.py -w my_bp_dir -m predict
Out[10]
Predictions stored at my_bp_dir/predictions.clf
4. Using bacpacs trained model for prediction via the command line interface
Initialize working directory
In[1]
$ python <path_to_bacpacs>/bacpacs.py -w my_bp_dir2 -m init --pre_trained
Out[1]
bacpacs initialized in my_bp_dir2
Now you can simply continue as before, without the clustering and training:
Create and extract features
The bacpacs project includes a toy.tar.gz file. Untar it using tar -xzvf toy.tar.gz <destination>. You could replace <destination>
with my_bp_dir2/toy. We'll use the toy set for the rest of the running example.
In[2]
$ python <path_to_bacpacs>/bacpacs.py -w my_bp_dir2 -m genomes_vs_pfs --genome_input_dir my_bp_dir2/toy/validate -t pred
Out[2]
Running genomes against protein families representatives
Genome cluster files are stored in my_bp_dir2/pred_clusters
In[3]
$ python <path_to_bacpacs>/bacpacs.py -w my_bp_dir2 -m extract_feats -t pred
Out[3]
Prediction feats stored in my_bp_dir2/pred_feats.csv
Predict the validation set pathogenicity labels
In[4]
$ python <path_to_bacpacs>/bacpacs.py -w my_bp_dir2 -m predict
Out[4]
Predictions stored at my_bp_dir2/predictions.csv
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