Name: polyAcode
Owner: Computational Biology and Computational Learning @ UCI
Description: null
Created: 2015-08-16 20:50:04.0
Updated: 2015-11-25 01:59:35.0
Pushed: 2015-11-25 03:08:08.0
Homepage:
Size: 6959
Language: Python
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README
README for polyAcode
This code base provides all the necessary pieces to reproduce the main results of the polyAcode manuscript. If you have any
questions, please email yil8@uci.edu
PREREQUISITES
Mandatory
Optional
- Shogun(>=3.2.0). Required for wdsvm.py
DATA
features_annotation.xls contains detailed annotation about the 658 features.
MayrLab
- polyAsites_specific.txt contains the 2276 tissue-specific PASs described in the manuscript.
- polyAsites_unspecific.txt contains the 3903 tissue-unspecific(constitutive) PASs described in the manuscript.
- features_specific.txt contains the 658 RNA features for the 2276 tissue-specific PASs.
- features_unspecific.txt contains the 658 RNA features for the 3903 tissue-unspecific PASs.
- seq_specific.txt contains the +/- 100nt sequence for the 2276 tissue-specific PASs.
- seq_unspecific.txt contains the +/- 100nt sequence for the 3903 tissue-unspecific PASs.
- data_features_train.npy is the numpy format for training lr.py, lsvm.py adaboost.py. It is a 4000x659 matrix with 4000 corresponding to 2000 tissue-specific PASs and 2000 tissue-unspecific PASs, and 659 corresponding 658 features and the last column representing the label(+1 for tissue-specific and -1 for tissue-unspecific).
- data_features_test.npy is the numpy format for testing lr.py lsvm.py adaboost.py. It is a 552x659 matrix with 276 tissue-specific PASs and 276 tissue-unspecific PASs.
- data_features_Bcell_train.npy, data_features_Bcell_test.npy are similarly defined as above but only for Bcell-specific PASs.
- data_features_Testis_train.npy, data_features_Testis_test.npy are similarly defined as above but only for Testis-specific PASs.
- data_seq_train.txt is the +/- 100nt sequence with the label for training wdsvm.py. It contains 2000 tissue-specific PASs and 2000 tissue-unspecific PASs.
- data_seq_test.txt is the +/- 100nt sequence with the label for testing wdsvm.py. It contains 276 tissue-specific PASs and 276 tissue-unspecific PASs.
JohnLab
- features_specific.txt contains the 658 RNA features for the 706 tissue-specific PASs.
- features_unspecific.txt contains the 658 RNA features for the 1114 tissue-unspecific PASs.
- data_features_train.npy is the numpy format for training lr.py, lsvm.py adaboost.py. It is a 1000x659 matrix with 1000 corresponding to 500 tissue-specific PASs and 500 tissue-unspecific PASs, and 659 corresponding 658 features and the last column representing the label(+1 for tissue-specific and -1 for tissue-unspecific).
- data_features_test.npy is the numpy format for testing lr.py lsvm.py adaboost.py. It is a 412x659 matrix with 206 tissue-specific PASs and 206 tissue-unspecific PASs.
TRAINING
AdaBoost
Training AdaBoost model is done by adaboost.py. For example:
adaboost.py ./MayrLab/data_features_train.npy ./MayrLab/data_features_test.npy 0 10 5
In this case, 0 is the feature index; 10 means using 10-fold cross-validation; 5 means using 5 process for training. The complete feature index is given below:
| Index | Definition |
| :—–| :———————– |
| 0 | All |
| 1 | Conservation level |
| 2 | Nucleosome positioning |
| 3 | Secondary structure |
| 4 | Transcript structure |
| 5 | Short 3mer motif |
| 6 | PAS signal & variants |
| 7 | Known regulators |
| 8 | Potential unknown motifs |
Logistic Regression
Training logistic regression model is done by lr.py. For example:
lr.py ./MayrLab/data_features_train.npy ./MayrLab/data_features_test.npy 0 10 5
which is similarly defined as adaboost.py.
Linear SVM
Training linear SVM model is done by lsvm.py. For example:
lsvm.py ./MayrLab/data_features_train.npy ./MayrLab/data_features_test.npy 0 10 5
which is similarly defined as adaboost.py.
WD-SVM
Training WD-SVM model is done by wdsvm.py. For example:
wdsvm.py ./MayrLab/data_seq_train.txt ./MayrLab/data_seq_test.txt 10
In this case, 10 means using 10-fold cross-validation;
