diff --git a/MiScan/core.py b/MiScan/core.py
index eab28d8..cf0186a 100644
--- a/MiScan/core.py
+++ b/MiScan/core.py
@@ -1,22 +1,22 @@
# -*- coding: utf-8 -*-
import operator
-import re
-import shutil
import warnings
from collections import Counter
-from os import makedirs, system, popen, environ
-from os.path import exists, join
+from os import environ, makedirs
+from os import system, popen
+from os.path import join, exists
+import re
import keras.backend as K
import matplotlib as mpl
import numpy as np
import pandas as pd
from scipy.io import mmread
+import shutil
-from .utils import get_data, exists_or_mkdir
-from .model import build_model
from .logging import get_logger
-
+from .model import build_dense_model as build_model
+from .utils import get_data, exists_or_mkdir
mpl.use('Agg')
import matplotlib.pyplot as plt
@@ -34,8 +34,6 @@
def vcf_to_sparse(outDir, inFeaID, inFeaBed, inVcf, featureID):
"""
convert the vcf to a feature matrix, `matrix.mtx`
-
-
:param outDir: output directory
:param inFeaID: `13885fea_exon_cut_100bp_2sample.txt` in dependency_data
:param inFeaBed: `tcga_13885fea_exon_cut_100bp.bed` in dependency_data
@@ -50,12 +48,10 @@ def vcf_to_sparse(outDir, inFeaID, inFeaBed, inVcf, featureID):
vcf_list = []
with open(inVcf) as f:
for line in f:
- # TODO need to optimize the regex
if re.match('#', line):
pass
else:
data = line.strip().split('\t')
- # TODO need to optimize the regex
chrom = data[0] if re.match('chr', data[0]) else 'chr' + data[0]
start = int(data[1])
end = start + 1
@@ -63,8 +59,7 @@ def vcf_to_sparse(outDir, inFeaID, inFeaBed, inVcf, featureID):
alt = data[3] + ',' + data[4]
vcf_list.append([chrom, start, end, 1, qual, alt])
vcf_df = pd.DataFrame(vcf_list, columns=['chrom', 'start', 'end', 'sample_name', 'qual', 'alt'])
- vcf_df.sort_values(by=['chrom', 'start'], ascending=True, inplace=True) # 'snp_sampleID.bed'
- # TODO check this
+ vcf_df.sort_values(by=['chrom', 'start'], ascending=True, inplace=True)
outSnpBedFile = join(outDir, 'snp_sampleID.bed')
vcf_df.to_csv(outSnpBedFile, sep='\t', header=None, index=None)
# --------------------------------------------------
@@ -123,7 +118,8 @@ def generate_report(inDir, outDir, y_pred_pat):
"""
generate report for single sample, including Cancer Risk Prediction | Top Gene Mutation Sites
- :param inDir: for historical reason, actually, it's the path of `MISCAN.norm.trainPred.txt` and 'MISCAN.pat.trainPred.txt' in dependency_data
+ :param inDir: for historical reason, actually, it's the path of `MISCAN.norm.trainPred.txt` and
+ 'MISCAN.pat.trainPred.txt' in dependency_data
:param outDir: output directory
:param y_pred_pat: risk_to_be_patient from func `prediction`
:return:
@@ -147,14 +143,13 @@ def generate_report(inDir, outDir, y_pred_pat):
axes[2].set_position(Bbox([[0.09, 0.57], [0.95, 0.83]]))
- trainPat_file = inDir[0]
- trainNorm_file = inDir[1]
- trainPat_ay = np.loadtxt(trainPat_file)
- trainNorm_ay = np.loadtxt(trainNorm_file)
-
- g = sns.kdeplot(list(trainPat_ay), label='trainPat', ax=axes[2],
+ train_pat = pd.read_csv(inDir[0], header=None).values
+ train_norm = pd.read_csv(inDir[1], header=None).values
+ train_pat = np.squeeze(train_pat, axis=1)
+ train_norm = np.squeeze(train_norm, axis=1)
+ g = sns.kdeplot(train_pat, label='trainPat', ax=axes[2],
shade=True, color='#ffb7ce')
- g = sns.kdeplot(list(trainNorm_ay), label='trainNorm', ax=axes[2],
+ g = sns.kdeplot(train_norm, label='trainNorm', ax=axes[2],
shade=True, color='#95d0fc')
axes[2].set_xlabel('Cancer risk', size=15)
axes[2].set_ylabel('Density', size=15)
diff --git a/MiScan/dependency_data/MISCAN.norm.trainPred.txt b/MiScan/dependency_data/MISCAN.norm.trainPred.txt
index 4e70463..b931cdf 100644
--- a/MiScan/dependency_data/MISCAN.norm.trainPred.txt
+++ b/MiScan/dependency_data/MISCAN.norm.trainPred.txt
@@ -1,2003 +1,3232 @@
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diff --git a/MiScan/dependency_data/MISCAN.pat.trainPred.txt b/MiScan/dependency_data/MISCAN.pat.trainPred.txt
index 9d9e6e2..ccdfa50 100644
--- a/MiScan/dependency_data/MISCAN.pat.trainPred.txt
+++ b/MiScan/dependency_data/MISCAN.pat.trainPred.txt
@@ -1,1972 +1,1183 @@
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diff --git a/MiScan/layers.py b/MiScan/layers.py
new file mode 100644
index 0000000..1ec6f38
--- /dev/null
+++ b/MiScan/layers.py
@@ -0,0 +1,33 @@
+from keras.layers import Dense, ReLU, Softmax, LeakyReLU
+
+
+def dense(units,
+ activation=None,
+ use_bias=True,
+ kernel_initializer='glorot_uniform',
+ bias_initializer='zeros',
+ kernel_regularizer=None,
+ bias_regularizer=None,
+ activity_regularizer=None,
+ kernel_constraint=None,
+ bias_constraint=None,
+ **kwargs):
+ return Dense(
+ units, activation=activation, use_bias=use_bias,
+ kernel_initializer=kernel_initializer, bias_initializer=bias_initializer,
+ kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer,
+ kernel_constraint=kernel_constraint, bias_constraint=bias_constraint,
+ activity_regularizer=activity_regularizer, **kwargs
+ )
+
+
+def activation(type='relu'):
+ if type not in ['relu', 'softmax', 'leakyrelu']:
+ raise ValueError('not support for : %s' % type)
+
+ if type == 'relu':
+ return ReLU()
+ elif type == 'softmax':
+ return Softmax()
+ elif type == 'leakyrelu':
+ return LeakyReLU()
diff --git a/MiScan/model.py b/MiScan/model.py
index ddafb27..5d2a6d4 100644
--- a/MiScan/model.py
+++ b/MiScan/model.py
@@ -1,35 +1,29 @@
-from keras import Sequential
-from keras.activations import softmax
-from keras.layers import MaxoutDense, Dropout, Dense
+from keras import Model
+from keras.layers import Dropout, Input
from keras.optimizers import adam
-from .utils import nb_dense, nb_hidden_units, cut_off
-from .utils import nb_fea as nb_dim
+from .layers import dense, activation
-def build_model(dim=nb_dim):
- """
- build MiScan Maxout model
+def build_dense_model(input_dim=13885,
+ lr=1e-6,
+ dropout=0.25,
+ acti='leakyrelu'
+ ):
+ input_x = Input(shape=(input_dim,), name="input", dtype='float32')
+ x = dense(256)(input_x)
+ x = activation(type=acti)(x)
+ x = Dropout(dropout)(x)
- :param dim: feature num , 13885 in this case
- :return:
- """
- model = Sequential()
- model.add(MaxoutDense(nb_hidden_units, nb_feature=nb_dense, input_dim=dim))
- model.add(Dropout(cut_off))
- model.add(MaxoutDense(nb_hidden_units, nb_feature=nb_dense))
- model.add(Dropout(cut_off))
- model.add(MaxoutDense(nb_hidden_units, nb_feature=nb_dense))
- model.add(Dropout(cut_off))
- model.add(MaxoutDense(nb_hidden_units, nb_feature=nb_dense))
- model.add(Dropout(cut_off))
- model.add(MaxoutDense(nb_hidden_units, nb_feature=nb_dense))
- model.add(Dropout(cut_off))
- model.add(MaxoutDense(nb_hidden_units, nb_feature=nb_dense))
- model.add(Dropout(cut_off))
- model.add(MaxoutDense(nb_hidden_units, nb_feature=nb_dense))
- model.add(Dropout(cut_off))
- model.add(Dense(2, activation=softmax))
+ x = dense(128)(x)
+ x = activation(type=acti)(x)
+ x = Dropout(dropout)(x)
- model.compile(loss='categorical_crossentropy', optimizer=adam(lr=1e-3), metrics=['accuracy'])
+ x = dense(64)(x)
+ x = activation(type=acti)(x)
+ x = Dropout(dropout)(x)
+
+ last = dense(2, activation='softmax')(x)
+ model = Model(inputs=input_x, outputs=last)
+ model.compile(loss='categorical_crossentropy', optimizer=adam(lr=lr), metrics=['accuracy'])
return model
diff --git a/MiScan/utils/__init__.py b/MiScan/utils/__init__.py
index 3fc1594..b43c2a5 100644
--- a/MiScan/utils/__init__.py
+++ b/MiScan/utils/__init__.py
@@ -1,7 +1,7 @@
from .constants import *
-from .files import exists_or_mkdir, file_exists
+from .files import exists_or_mkdir, file_exists, check_file
from .util import time_it, get_data
__all__ = ['exists_or_mkdir', 'file_exists', 'time_it', 'get_data',
- 'nb_dense', 'nb_fea', 'nb_hidden_units', 'cut_off']
+ 'nb_dense', 'nb_fea', 'nb_hidden_units', 'cut_off', 'check_file']
diff --git a/MiScan/utils/files.py b/MiScan/utils/files.py
index cc00c31..4b44bc3 100644
--- a/MiScan/utils/files.py
+++ b/MiScan/utils/files.py
@@ -1,5 +1,6 @@
-from os import makedirs
-from os.path import exists, isfile
+from os import makedirs, remove, getcwd, listdir
+from os.path import exists, isfile, join
+import re
def exists_or_mkdir(path, verbose=False):
@@ -31,3 +32,48 @@ def file_exists(filepath):
"""Check whether a file exists by given file path."""
return isfile(filepath)
+
+def check_file(path, verbose=True):
+ """Check a file by given name, if exist, then delete and return 0,
+ else, return 1.
+ Parameters
+ ----------
+ path : str
+ A file path.
+ verbose : boolean
+ If True (default), prints results.
+ Returns
+ --------
+ int
+ 0 if file already exist and delete the file, otherwise, returns 1.
+ """
+ if exists(path) and isfile(path):
+ remove(path)
+ if verbose:
+ print("[*] delete original %s ..." % path)
+ return 0
+ else:
+ return 1
+
+
+def load_file_list(path=None, regx=r'\.csv$', printable=True):
+ """Return a file list in a folder by given a path and regular expression.
+ Parameters
+ ----------
+ path : a string or None
+ A folder path.
+ regx : a string
+ The regx of file name.
+ printable : boolean, whether to print the files infomation.
+ """
+ if not path:
+ path = getcwd()
+ file_list = listdir(path)
+ return_list = []
+ for f in file_list:
+ if re.search(regx, f):
+ return_list.append(join(path, f))
+ if printable:
+ print('Match file example = %s' % (str(return_list[0])))
+ print('Number of files = %d in path: %s' % (len(return_list), path))
+ return return_list
diff --git a/README.rst b/README.rst
index 5b6e245..e93d8b7 100644
--- a/README.rst
+++ b/README.rst
@@ -24,4 +24,5 @@ the website_ version(website introduction_)
.. _documentation: https://miscan-cli.readthedocs.io
.. _website: http://qulab.ustc.edu.cn/miscan
+.. _QunKunLab: https://github.com/QuKunLab/MiScan
.. _introduction: https://miscan-cli.readthedocs.io/en/latest/website.html
diff --git a/docs/source/API/index.rst b/docs/source/API/index.rst
index c9390bb..4c5b46e 100644
--- a/docs/source/API/index.rst
+++ b/docs/source/API/index.rst
@@ -14,27 +14,26 @@ MiScan utils
~~~~~~~~~~~~~~~~
.. autosummary::
+ :toctree: .
utils.exists_or_mkdir
utils.file_exists
utils.get_data
-MiScan Core
-~~~~~~~~~~~~~~~~
-
-.. autosummary::
- logging.get_logger
-
MiScan model
~~~~~~~~~~~~~~~~
.. autosummary::
+ :toctree: .
+
model.build_model
MiScan Core
~~~~~~~~~~~~~~~~
.. autosummary::
+ :toctree: .
+
core.vcf_to_sparse
core.prediction
core.generate_report
diff --git a/docs/source/conf.py b/docs/source/conf.py
index ee3b532..5e6b47e 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -22,9 +22,9 @@
project = 'MiScan'
copyright = '2019, Qulab USTC'
author = 'Qulab USTC'
-version = '1.0.1'
+version = '1.0.2'
# The full version, including alpha/beta/rc tags
-release = '1.0.1'
+release = '1.0.2'
# -- General configuration ---------------------------------------------------
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 32e7c26..307f992 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -32,7 +32,7 @@
.. |Travis| image:: https://img.shields.io/travis/com/jefferyustc/MiScan_cli
:alt: Travis (.com)
-MiScan: Maxout-inferred SNV-based cancer prediction model
+MiScan: mutation-inferred screening model of breast cancer
==============================================================
|
@@ -41,9 +41,9 @@ MiScan: Maxout-inferred SNV-based cancer prediction model
|
-Here we present a Maxout-inferred SNV-based can cer prediction model (MiScan) for screening of breast cancer patients
+Here we present MiScan: mutation-inferred screening model of cancer for screening of breast cancer patients
using genotype information from blood samples. We coarsened 119,318 variants into 13,885 genomic windows, and trained
-MiScan with coarse-grained mutation information from 986 breast cancer patients and 2,003 healthy people. To promote
+MiScan with coarse-grained mutation information from 1183 breast cancer patients and 3232 healthy people. To promote
the application of MiScan for breast cancer prediction and prevention, we established an interactive website_, which
automatically provides a breast cancer risk assessment feedback report from genotype information as input.
diff --git a/docs/source/references.rst b/docs/source/references.rst
index 5aa9797..b399e47 100644
--- a/docs/source/references.rst
+++ b/docs/source/references.rst
@@ -1,7 +1,7 @@
References
================
-[Tensorflow]_, [Keras]_, [Maxout]_ provides great inspiration to our project.
+[Tensorflow]_, [Keras]_, provides great inspiration to our project.
.. [Tensorflow] Abadi, Martín, et al.
"Tensorflow: A system for large-scale machine learning." 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16). 2016.
@@ -10,7 +10,4 @@ References
.. [Keras] Chollet, François. "Keras." (2015).
`Keras `__
-.. [Maxout] Goodfellow, Ian J., et al.
- "Maxout networks." arXiv preprint arXiv:1302.4389 (2013).
- `Maxout networks `__
diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst
index c28bd02..56896c6 100644
--- a/docs/source/tutorial.rst
+++ b/docs/source/tutorial.rst
@@ -52,5 +52,5 @@ users can also execute this from script::
-.. _FTP: http://galaxy.ustc.edu.cn:30803/liunianping/miscan/miscan_test_data/
-.. _here: http://galaxy.ustc.edu.cn:30803/liunianping/miscan/miscan_model/
\ No newline at end of file
+.. _FTP: http://galaxy.ustc.edu.cn:30803/MiScan/miscan_test_data/
+.. _here: http://galaxy.ustc.edu.cn:30803/MiScan/miscan_model/MiScan_weight.h5
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index b077c70..bf08968 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,30 +1,58 @@
absl-py==0.7.1
adjustText==0.7.3
-astor==0.8.0
+astor==0.7.1
+bleach==3.1.0
+certifi==2019.9.11
+chardet==3.0.4
cycler==0.10.0
+docutils==0.15.2
+entrypoints==0.3
gast==0.2.2
-google-pasta==0.1.7
-grpcio==1.22.0
+grpcio==1.19.0
h5py==2.9.0
+idna==2.8
+importlib-metadata==0.23
+joblib==0.13.2
Keras==2.2.4
-Keras-Applications==1.0.8
-Keras-Preprocessing==1.1.0
-kiwisolver==1.1.0
-Markdown==3.1.1
+Keras-Applications==1.0.7
+Keras-Preprocessing==1.0.9
+keyring==19.2.0
+kiwisolver==1.0.1
+llvmlite==0.29.0
+Markdown==3.0.1
matplotlib==3.1.1
-numpy==1.16.4
-pandas==0.24.2
-protobuf==3.9.0
-pyparsing==2.4.0
+mock==2.0.0
+more-itertools==7.2.0
+numba==0.44.0
+numpy==1.17.0
+opencv-python==4.0.0.21
+pandas==0.25.0
+patsy==0.5.1
+pbr==5.1.3
+Pillow==6.0.0
+pkginfo==1.5.0.1
+protobuf==3.7.0
+Pygments==2.4.2
+pyparsing==2.3.1
python-dateutil==2.8.0
-pytz==2019.1
-PyYAML==5.1.1
-scipy==1.3.0
+pytz==2018.9
+PyYAML==5.1
+readme-renderer==24.0
+requests==2.22.0
+requests-toolbelt==0.9.1
+scikit-learn==0.20.3
+scipy==1.2.1
seaborn==0.9.0
six==1.12.0
-tensorboard==1.14.0
-tensorflow==1.14.0
-tensorflow-estimator==1.14.0
+statsmodels==0.10.2
+tensorboard==1.13.1
+tensorflow==1.13.1
+tensorflow-estimator==1.13.0
termcolor==1.1.0
-Werkzeug==0.15.5
-wrapt==1.11.2
+tqdm==4.38.0
+twine==3.0.0
+umap-learn==0.3.9
+urllib3==1.25.7
+webencodings==0.5.1
+Werkzeug==0.15.0
+zipp==0.6.0
diff --git a/setup.py b/setup.py
index dbc77ea..b9c91da 100644
--- a/setup.py
+++ b/setup.py
@@ -7,15 +7,15 @@
setuptools.setup(
name="MiScan",
- version="1.0.1.post1",
+ version="1.1.1",
author='Qulab USTC',
author_email="jeffery_cpu@163.com",
- description="Maxout-inferred SNV-based cancer prediction model",
+ description="MiScan: mutation-inferred screening model of cancer",
long_description=long_description,
- long_description_content_type="text/markdown",
+ long_description_content_type="text/x-rst",
url="https://github.com/QuKunLab/MiScan",
project_urls={
- 'MiScan website': 'http://galaxy.ustc.edu.cn',
+ 'MiScan website': 'http://qulab.ustc.edu.cn/miscan',
},
zip_safe=False,
keywords="deep learning SNV breast cancer prediction",
diff --git a/tests/test_model.py b/tests/test_model.py
index 84030ef..0666441 100644
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -1,4 +1,4 @@
-from MiScan.model import build_model
+from MiScan.model import build_dense_model as build_model
import unittest