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individual_depa.py
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#!/usr/bin/
# -*- coding: utf-8 -*-
__author__ = 'Liu Yachen'
import click
import copy
import os
import datetime
import shutil
import random
import configparser
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
from matplotlib_venn import venn2,venn2_circles
from deps_lib import utils, raw_data, stable_pairs, methods_comp, penda_pro, methods_lib, similarity_lib, type1_error_lib, robustness_lib, kegg_lib, survival_lib
import rpy2
from rpy2.robjects.vectors import FloatVector
import rpy2.robjects as ro
from rpy2.robjects.packages import importr
from rpy2.robjects import pandas2ri
from rpy2.robjects.conversion import localconverter
r_stats = importr('stats')
plt.rcParams['font.sans-serif'] = ['SimHei']
sns.set(font_scale=1.5)
plt.rcParams['axes.unicode_minus']=False
CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help'])
@click.group(context_settings=CONTEXT_SETTINGS)
def cli():
pass
@click.version_option(version='1.0.0')
@click.command(context_settings=CONTEXT_SETTINGS)
@click.option('--parameters_path', '-p', required=True, type=click.Path(), help='parameters file')
def stable(parameters_path):
""" Get reversal pairs and consensus pairs """
########################## Read parameters ##############################
params = configparser.ConfigParser()
params.read(parameters_path)
# workdir
workdir = params.get('workdir', 'workdir')
sp_workdir = params.get('workdir', 'sp_workdir')
# script
r_bpca_path = params.get('script path', 'r_bpca_path')
r_penda_fdr_path = params.get('script path', 'r_penda_fdr_path')
r_penda_path = params.get('script path', 'r_penda_path')
reoa_path = params.get('script path', 'reoa_path')
# preprocess parameters
LOG_LABEL = eval(params.get('preprocess', 'log_label'))
IMPUT_LABEL = eval(params.get('preprocess', 'imput_label'))
NA_LABEL = eval(params.get('preprocess', 'na_label'))
NA_RATE = eval(params.get('preprocess', 'na_rate'))
NORMALIZATION = eval(params.get('preprocess', 'normalization'))
INDEX_OLD_CHAR = eval(params.get('preprocess', 'index_old_char'))
INDEX_NEW_CHAR = eval(params.get('preprocess', 'index_new_char'))
COLUMNS_OLD_CHAR = eval(params.get('preprocess', 'columns_OLD_char'))
COLUMNS_NEW_CHAR = eval(params.get('preprocess', 'columns_new_char'))
# method parameters
CYCLE_RANKC = eval(params.get('method parameters', 'cycle_rankc'))
FDR = eval(params.get('method parameters', 'fdr'))
MAX_EXCEPTION_RANKC = eval(params.get('method parameters', 'max_exception_rankc'))
# stable pairs parameter
data_path = params.get('stable pairs', 'data_path')
normal_cohort_path = params.get('stable pairs', 'normal_cohort_path')
tumor_cohort_path = params.get('stable pairs', 'tumor_cohort_path')
HAS_SPECIFIC_PROTEIN = eval(params.get('stable pairs', 'label_specific_protein'))
if HAS_SPECIFIC_PROTEIN:
specific_protein_path = params.get('stable pairs', 'specific_protein_path')
else:
specific_protein_path = None
SP_THRES = eval(params.get('stable pairs', 'sp_thres'))
RANDOM_VISUAL = eval(params.get('stable pairs', 'random_visual'))
N_VISUAL = eval(params.get('stable pairs', 'n_visual'))
# read data
data = utils.read_data(data_path)
normal_cohort = utils.read_normal_cohort(normal_cohort_path)
tumor_cohort = utils.read_tumor_cohort(tumor_cohort_path)
if HAS_SPECIFIC_PROTEIN:
specific_protein = utils.read_specific_protein(specific_protein_path)
else:
specific_protein = None
rd = raw_data.InputData(data=data,
normal_cohort=normal_cohort,
tumor_cohort=tumor_cohort,
specific_protein=specific_protein,
HAS_SPECIFIC_PROTEIN=HAS_SPECIFIC_PROTEIN,
paired_data=None,
paired_samples=None,
HAS_PAIRED_DATA=None,
INDEX_OLD_CHAR=INDEX_OLD_CHAR,
INDEX_NEW_CHAR=INDEX_NEW_CHAR,
COLUMNS_OLD_CHAR=COLUMNS_OLD_CHAR,
COLUMNS_NEW_CHAR=COLUMNS_NEW_CHAR,
NORMALIZATION=NORMALIZATION,
LOG_LABEL=LOG_LABEL,
IMPUT_LABEL=IMPUT_LABEL,
NA_LABEL=NA_LABEL,
NA_RATE=NA_RATE)
preprocess_workdir = sp_workdir + '/preprocess'
# data preprocess
rd.data_preprocess(preprocess_workdir=preprocess_workdir,
r_bpca_path=r_bpca_path)
sp = stable_pairs.StablePairs(rd = rd,
sp_workdir = sp_workdir,
SP_THRES = SP_THRES,
RANDOM_VISUAL = RANDOM_VISUAL,
NUM_VISUAL = N_VISUAL,
CYCLE_RANKC = CYCLE_RANKC,
FDR = FDR,
MAX_EXCEPTION_RANKC = MAX_EXCEPTION_RANKC,
fig_save_path=sp_workdir,
reoa_path = reoa_path)
sp.run_stablePairs()
@click.command(context_settings=CONTEXT_SETTINGS)
@click.option('--parameters_path', '-p', required=True, type=click.Path(), help='parameters file')
def comparison(parameters_path):
''' Compare the precision of DEAs with the number of DEPs '''
########################## parameters input ##############################
params = configparser.ConfigParser()
params.read(parameters_path)
# workdir
workdir = params.get('workdir', 'workdir')
mc_workdir = params.get('workdir', 'mc_workdir')
# script
r_bpca_path = params.get('script path', 'r_bpca_path')
r_penda_fdr_path = params.get('script path', 'r_penda_fdr_path')
r_penda_path = params.get('script path', 'r_penda_path')
reoa_path = params.get('script path', 'reoa_path')
# preprocess parameters
LOG_LABEL = eval(params.get('preprocess', 'log_label'))
IMPUT_LABEL = eval(params.get('preprocess', 'imput_label'))
NA_LABEL = eval(params.get('preprocess', 'na_label'))
NA_RATE = eval(params.get('preprocess', 'na_rate'))
NORMALIZATION = eval(params.get('preprocess', 'normalization'))
INDEX_OLD_CHAR = eval(params.get('preprocess', 'index_old_char'))
INDEX_NEW_CHAR = eval(params.get('preprocess', 'index_new_char'))
COLUMNS_OLD_CHAR = eval(params.get('preprocess', 'columns_OLD_char'))
COLUMNS_NEW_CHAR = eval(params.get('preprocess', 'columns_new_char'))
# method parameters
CYCLE_RANKC = eval(params.get('method parameters', 'cycle_rankc'))
FDR = eval(params.get('method parameters', 'fdr'))
MAX_EXCEPTION_RANKC = eval(params.get('method parameters', 'max_exception_rankc'))
# methods comparison parameter
data_path = params.get('methods comparison', 'data_path')
normal_cohort_path = params.get('methods comparison', 'normal_cohort_path')
tumor_cohort_path = params.get('methods comparison', 'tumor_cohort_path')
paired_data_path = params.get('methods comparison', 'paired_data_path')
paired_samples_path = params.get('methods comparison', 'paired_samples_path')
METHODS_LIST = eval(params.get('methods comparison', 'methods_list'))
# read data
data = utils.read_data(data_path)
normal_cohort = utils.read_normal_cohort(normal_cohort_path)
tumor_cohort = utils.read_tumor_cohort(tumor_cohort_path)
paired_data = utils.read_paired_data(paired_data_path)
paired_samples = utils.read_paired_samples(paired_samples_path)
rd = raw_data.InputData(data=data,
normal_cohort=normal_cohort,
tumor_cohort=tumor_cohort,
specific_protein=None,
HAS_SPECIFIC_PROTEIN=False,
paired_data=paired_data,
paired_samples=paired_samples,
HAS_PAIRED_DATA=True,
INDEX_OLD_CHAR=INDEX_OLD_CHAR,
INDEX_NEW_CHAR=INDEX_NEW_CHAR,
COLUMNS_OLD_CHAR=COLUMNS_OLD_CHAR,
COLUMNS_NEW_CHAR=COLUMNS_NEW_CHAR,
NORMALIZATION=NORMALIZATION,
LOG_LABEL=LOG_LABEL,
IMPUT_LABEL=IMPUT_LABEL,
NA_LABEL=NA_LABEL,
NA_RATE=NA_RATE)
preprocess_workdir = mc_workdir + '/preprocess'
rd.data_preprocess(preprocess_workdir=preprocess_workdir,
r_bpca_path=r_bpca_path)
mc = methods_comp.methodsComp(rd = rd,
r_penda_path = r_penda_path,
r_penda_fdr_path = r_penda_fdr_path,
reoa_path = reoa_path,
CYCLE_RANKC = CYCLE_RANKC,
FDR = FDR,
MAX_EXCEPTION_RANKC = MAX_EXCEPTION_RANKC,
PLOT_METHODS_COMPARE_RESULT = False,
METHODS_LIST = METHODS_LIST)
precision_mess, positive_mess = mc.run_methodsComp(mc_workdir = mc_workdir)
@click.command(context_settings=CONTEXT_SETTINGS)
@click.option('--parameters_path', '-p', required=True, type=click.Path(), help='parameters file')
def evaluation(parameters_path):
''' Parameter evaluation '''
########################## parameters input ##############################
params = configparser.ConfigParser()
params.read(parameters_path)
# workdir
wordir = params.get('workdir', 'workdir')
pe_workdir = params.get('workdir', 'pe_workdir')
# script
r_bpca_path = params.get('script path', 'r_bpca_path')
r_penda_fdr_path = params.get('script path', 'r_penda_fdr_path')
r_penda_path = params.get('script path', 'r_penda_path')
reoa_path = params.get('script path', 'reoa_path')
# preprocess parameters
LOG_LABEL = eval(params.get('preprocess', 'log_label'))
IMPUT_LABEL = eval(params.get('preprocess', 'imput_label'))
NA_LABEL = eval(params.get('preprocess', 'na_label'))
NA_RATE = eval(params.get('preprocess', 'na_rate'))
NORMALIZATION = eval(params.get('preprocess', 'normalization'))
INDEX_OLD_CHAR = eval(params.get('preprocess', 'index_old_char'))
INDEX_NEW_CHAR = eval(params.get('preprocess', 'index_new_char'))
COLUMNS_OLD_CHAR = eval(params.get('preprocess', 'columns_OLD_char'))
COLUMNS_NEW_CHAR = eval(params.get('preprocess', 'columns_new_char'))
# method parameters
CYCLE_RANKC = eval(params.get('method parameters', 'cycle_rankc'))
FDR = eval(params.get('method parameters', 'fdr'))
MAX_EXCEPTION_RANKC = eval(params.get('method parameters', 'max_exception_rankc'))
# parameters evalution
data_path = params.get('parameters evalution', 'data_path')
normal_cohort_path = params.get('parameters evalution', 'normal_cohort_path')
tumor_cohort_path = params.get('parameters evalution', 'tumor_cohort_path')
samples_path = params.get('parameters evalution', 'samples_path')
SAMPLE_SIZE_LABEL = eval(params.get('parameters evalution', 'sample_size_label'))
N_PROTEIN_LABEL = eval(params.get('parameters evalution', 'n_protein_label'))
METHODS_LIST = eval(params.get('parameters evalution', 'methods_list'))
SAMPLE_SIZE_LIST = eval(params.get('parameters evalution', 'sample_size_list'))
N_PROTEIN_LIST = eval(params.get('parameters evalution', 'n_protein_list'))
# read data
data = utils.read_data(data_path)
normal_cohort = utils.read_normal_cohort(normal_cohort_path)
tumor_cohort = utils.read_tumor_cohort(tumor_cohort_path)
samples = pd.read_csv(samples_path, sep='\t', index_col=0)
samples = utils.col_adapt(samples_col = samples, characters = COLUMNS_OLD_CHAR, new_char = COLUMNS_NEW_CHAR)
rd = raw_data.InputData(data=data,
normal_cohort=normal_cohort,
tumor_cohort=tumor_cohort,
specific_protein=None,
HAS_SPECIFIC_PROTEIN=False,
paired_data=None,
paired_samples=None,
HAS_PAIRED_DATA=False,
INDEX_OLD_CHAR=INDEX_OLD_CHAR,
INDEX_NEW_CHAR=INDEX_NEW_CHAR,
COLUMNS_OLD_CHAR=COLUMNS_OLD_CHAR,
COLUMNS_NEW_CHAR=COLUMNS_NEW_CHAR,
NORMALIZATION=NORMALIZATION,
LOG_LABEL=LOG_LABEL,
IMPUT_LABEL=IMPUT_LABEL,
NA_LABEL=NA_LABEL,
NA_RATE=NA_RATE)
preprocess_workdir = pe_workdir + '/preprocess'
rd.data_preprocess(preprocess_workdir=preprocess_workdir,
r_bpca_path=r_bpca_path)
samples.to_csv(preprocess_workdir+'/samples.txt', sep='\t')
data = rd.data_imput
normal_cohort = rd.normal_cohort_adapt
tumor_cohort = rd.tumor_cohort_adapt
if SAMPLE_SIZE_LABEL:
workdir_ss = os.path.abspath(pe_workdir) + '/sample_size'
if os.path.exists(workdir_ss) == False:
os.makedirs(workdir_ss)
ss_path_list = []
ss_prec_mess = {}
ss_posi_mess = {}
i = 0
for ss in SAMPLE_SIZE_LIST:
i += 1
t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('\nSAMPLE SIZE LIST: %s/%s %s\n'%(i, len(SAMPLE_SIZE_LIST),t))
_path = workdir_ss + '/%s'%ss
ss_path_list.append(_path)
if os.path.exists(_path) == False:
os.makedirs(_path)
os.chdir(_path)
_samples_part = samples.loc[random.sample(samples.index.tolist(), ss * 2), :].reset_index(drop=True)
_paired_index = random.sample(_samples_part.index.tolist(), ss)
_paired_index.sort()
_unpaired_index = []
for _s in _samples_part.index:
if _s not in _paired_index:
_unpaired_index.append(_s)
_paired_samples = _samples_part.loc[_paired_index, :].reset_index(drop=True)
_unpaired_samples = _samples_part.loc[_unpaired_index, :].reset_index(drop=True)
_tumor_cohort = pd.DataFrame({'tumor': _paired_samples['tumor']})
_normal_cohort = pd.DataFrame({'normal': _unpaired_samples['normal']})
_paired_data = data.loc[:, _paired_samples['normal'].tolist() + _paired_samples['tumor'].tolist()]
_data = data.loc[:, _paired_samples['tumor'].tolist() + _unpaired_samples['normal'].tolist()]
_paired_samples.to_csv('./paired_samples.txt', sep='\t', index=False)
_paired_data.to_csv('./paired_data.csv')
_tumor_cohort.to_csv('./tumor.txt', sep='\t', index=False)
_normal_cohort.to_csv('./normal.txt', sep='\t', index=False)
_data.to_csv('./data.csv')
rd = raw_data.InputData(data=_data,
normal_cohort=_normal_cohort,
tumor_cohort=_tumor_cohort,
specific_protein=None,
HAS_SPECIFIC_PROTEIN=False,
paired_data=_paired_data,
paired_samples=_paired_samples,
HAS_PAIRED_DATA=True,
INDEX_OLD_CHAR=['-', ' '],
INDEX_NEW_CHAR='.',
COLUMNS_OLD_CHAR=['-', ' '],
COLUMNS_NEW_CHAR='.',
NORMALIZATION=False,
LOG_LABEL=False,
NA_LABEL='False',
NA_RATE=0)
_preprocess_workdir = _path + '/preprocess'
rd.data_preprocess(preprocess_workdir=_preprocess_workdir,
r_bpca_path=r_bpca_path)
mc = methods_comp.methodsComp(rd = rd,
r_penda_path = r_penda_path,
r_penda_fdr_path = r_penda_fdr_path,
reoa_path = reoa_path,
CYCLE_RANKC = CYCLE_RANKC,
FDR = FDR,
MAX_EXCEPTION_RANKC = MAX_EXCEPTION_RANKC,
PLOT_METHODS_COMPARE_RESULT = False,
METHODS_LIST = METHODS_LIST)
_mc_workdir = _path + '/methods_compare'
precision_mess, positive_mess = mc.run_methodsComp(mc_workdir = _mc_workdir)
ss_prec_mess[ss] = precision_mess
ss_posi_mess[ss] = positive_mess
prec_set = ss_prec_mess
num_set = ss_posi_mess
x_x = range(len(prec_set.keys()))
x = list(prec_set.keys())
y_p, y_n = {}, {}
for _m in list(prec_set.values())[0].keys():
_m_p = []
_m_n = []
for _x in x:
_m_p.append(np.mean(prec_set[_x][_m]))
_m_n.append(np.mean(num_set[_x][_m]))
y_p[_m] = _m_p
y_n[_m] = _m_n
bar_width = 0.1
tick_label = list(prec_set.keys())
fig = plt.figure(figsize=(12,8))
ax1 = fig.add_subplot(111)
if 'RankComp' in METHODS_LIST:
ax1.bar(np.array(x_x)+(0 * bar_width), y_p['rankc_v1'], bar_width, align="center", label='prec_rankc_v1', alpha=1, color='b')
ax1.bar(np.array(x_x)+(1 * bar_width), y_p['rankc_v2'], bar_width, align="center", label='prec_rankc_v2', alpha=1, color='c')
if 'Penda' in METHODS_LIST:
ax1.bar(np.array(x_x)+(2 * bar_width), y_p['penda'], bar_width, align="center", label='prec_penda', alpha=1, color='g')
if 'T-test' in METHODS_LIST:
ax1.bar(np.array(x_x)+(3 * bar_width), y_p['ttest'], bar_width, align="center", label='Prec_ttest', alpha=1, color='m')
if 'Wilcoxon' in METHODS_LIST:
ax1.bar(np.array(x_x)+(4 * bar_width), y_p['wilcox'], bar_width, align="center", label='prec_wilcox', alpha=1, color='r')
if 'Quantile' in METHODS_LIST:
ax1.bar(np.array(x_x)+(5 * bar_width), y_p['quantile'], bar_width, align="center", label='prec_quantile', alpha=1, color='y')
if 'Peng_methods' in METHODS_LIST:
ax1.bar(np.array(x_x)+(6 * bar_width), y_p['peng_method'], bar_width, align="center", label='prec_peng', alpha=1, color='k')
ax1.set_xlabel("Sample Size", fontsize=20)
ax1.set_ylabel('Precision', fontsize=20)
ax1.legend(loc=[1.2, 0.5])
ax2 = ax1.twinx()
if 'RankComp' in METHODS_LIST:
ax2.plot(np.array(x_x)+(3 * bar_width), y_n['rankc_v1'], label='num_rankc_v1', alpha=1, ms=10, lw=3, marker='o', c='b')
ax2.plot(np.array(x_x)+(3 * bar_width), y_n['rankc_v2'], label='num_rankc_v2', alpha=1, ms=10, lw=3, marker='o', c='c')
if 'Penda' in METHODS_LIST:
ax2.plot(np.array(x_x)+(3 * bar_width), y_n['penda'], label='num_penda', alpha=1, ms=10, lw=3, marker='o', c='g')
if 'T-test' in METHODS_LIST:
ax2.plot(np.array(x_x)+(3 * bar_width), y_n['ttest'], label='num_ttest', alpha=1, ms=10, lw=3, marker='o', c='m')
if 'Wilcoxon' in METHODS_LIST:
ax2.plot(np.array(x_x)+(3 * bar_width), y_n['wilcox'], label='num_wilcox', alpha=1, ms=10, lw=3, marker='o', c='r')
if 'Quantile' in METHODS_LIST:
ax2.plot(np.array(x_x)+(3 * bar_width), y_n['quantile'], label='num_quantile', alpha=1, ms=10, lw=3, marker='o', c='y')
if 'Peng_methods' in METHODS_LIST:
ax2.plot(np.array(x_x)+(3 * bar_width), y_n['peng_method'], label='num_peng', alpha=1, ms=10, lw=3, marker='o', c='k')
ax2.set_ylabel('Number of DE protein', fontsize='20')
sns.despine(left=True, bottom=True)
ax2.tick_params(labelsize=15)
ax2.legend(loc=[1.2, 0])
plt.xticks(np.array(x_x)+ 3*bar_width/2, tick_label)
plt.savefig(pe_workdir + '/pe_sample_size.pdf', dpi=800, bbox_inches='tight')
if N_PROTEIN_LABEL:
workdir_np = os.path.abspath(pe_workdir) + '/n_protein'
if os.path.exists(workdir_np) == False:
os.makedirs(workdir_np)
np_path_list = []
np_prec_mess = {}
np_posi_mess = {}
i = 0
for pn in N_PROTEIN_LIST:
i += 1
t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('\nNUM PROTEIN LIST: %s/%s %s\n'%(i, len(N_PROTEIN_LIST),t))
_path = workdir_np + '/%s'%pn
np_path_list.append(_path)
if os.path.exists(_path) == False:
os.makedirs(_path)
os.chdir(_path)
_data_part = data.loc[random.sample(data.index.tolist(), pn), :]
_paired_index = random.sample(samples.index.tolist(), int(samples.shape[0] / 2))
_paired_index.sort()
_unpaired_index = []
for _s in samples.index:
if _s not in _paired_index:
_unpaired_index.append(_s)
_paired_samples = samples.loc[_paired_index, :].reset_index(drop=True)
_unpaired_samples = samples.loc[_unpaired_index, :].reset_index(drop=True)
_tumor_cohort = pd.DataFrame({'tumor': _paired_samples['tumor']})
_normal_cohort = pd.DataFrame({'normal': _unpaired_samples['normal']})
_paired_data = _data_part.loc[:, _paired_samples['normal'].tolist() + _paired_samples['tumor'].tolist()]
_data = _data_part.loc[:, _paired_samples['tumor'].tolist() + _unpaired_samples['normal'].tolist()]
_paired_samples.to_csv('./paired_samples.txt', sep='\t', index=False)
_paired_data.to_csv('./paired_data.csv')
_tumor_cohort.to_csv('./tumor.txt', sep='\t', index=False)
_normal_cohort.to_csv('./normal.txt', sep='\t', index=False)
_data.to_csv('./data.csv')
rd = raw_data.InputData(data=_data,
normal_cohort=_normal_cohort,
tumor_cohort=_tumor_cohort,
specific_protein=None,
HAS_SPECIFIC_PROTEIN=False,
paired_data=_paired_data,
paired_samples=_paired_samples,
HAS_PAIRED_DATA=True,
INDEX_OLD_CHAR=['-', ' '],
INDEX_NEW_CHAR='.',
COLUMNS_OLD_CHAR=['-', ' '],
COLUMNS_NEW_CHAR='.',
NORMALIZATION=False,
LOG_LABEL=False,
NA_LABEL='False',
NA_RATE=0)
_preprocess_workdir = _path + '/preprocess'
rd.data_preprocess(preprocess_workdir=_preprocess_workdir,
r_bpca_path=r_bpca_path)
mc = methods_comp.methodsComp(rd = rd,
r_penda_path = r_penda_path,
r_penda_fdr_path = r_penda_fdr_path,
reoa_path = reoa_path,
CYCLE_RANKC = CYCLE_RANKC,
FDR = FDR,
MAX_EXCEPTION_RANKC = MAX_EXCEPTION_RANKC,
PLOT_METHODS_COMPARE_RESULT = False,
METHODS_LIST = METHODS_LIST)
_mc_workdir = _path + '/methods_compare'
precision_mess, positive_mess = mc.run_methodsComp(mc_workdir = _mc_workdir)
np_prec_mess[pn] = precision_mess
np_posi_mess[pn] = positive_mess
prec_set = np_prec_mess
num_set = np_posi_mess
x_x = range(len(prec_set.keys()))
x = list(prec_set.keys())
y_p, y_n = {}, {}
for _m in list(prec_set.values())[0].keys():
_m_p = []
_m_n = []
for _x in x:
_m_p.append(np.mean(prec_set[_x][_m]))
_m_n.append(np.mean(num_set[_x][_m]))
y_p[_m] = _m_p
y_n[_m] = _m_n
bar_width = 0.1
tick_label = list(prec_set.keys())
fig = plt.figure(figsize=(12,8))
ax1 = fig.add_subplot(111)
if 'RankComp' in METHODS_LIST:
ax1.bar(np.array(x_x)+(0 * bar_width), y_p['rankc_v1'], bar_width, align="center", label='prec_rankc_v1', alpha=1, color='b')
ax1.bar(np.array(x_x)+(1 * bar_width), y_p['rankc_v2'], bar_width, align="center", label='prec_rankc_v2', alpha=1, color='c')
if 'Penda' in METHODS_LIST:
ax1.bar(np.array(x_x)+(2 * bar_width), y_p['penda'], bar_width, align="center", label='prec_penda', alpha=1, color='g')
if 'T-test' in METHODS_LIST:
ax1.bar(np.array(x_x)+(3 * bar_width), y_p['ttest'], bar_width, align="center", label='Prec_ttest', alpha=1, color='m')
if 'Wilcoxon' in METHODS_LIST:
ax1.bar(np.array(x_x)+(4 * bar_width), y_p['wilcox'], bar_width, align="center", label='prec_wilcox', alpha=1, color='r')
if 'Quantile' in METHODS_LIST:
ax1.bar(np.array(x_x)+(5 * bar_width), y_p['quantile'], bar_width, align="center", label='prec_quantile', alpha=1, color='y')
if 'Peng_methods' in METHODS_LIST:
ax1.bar(np.array(x_x)+(6 * bar_width), y_p['peng_method'], bar_width, align="center", label='prec_peng', alpha=1, color='k')
ax1.set_xlabel("The number of protein", fontsize=20)
ax1.set_ylabel('Precision', fontsize=20)
ax1.legend(loc=[1.2, 0.5])
ax2 = ax1.twinx()
if 'RankComp' in METHODS_LIST:
ax2.plot(np.array(x_x)+(3 * bar_width), y_n['rankc_v1'], label='num_rankc_v1', alpha=1, ms=10, lw=3, marker='o', c='b')
ax2.plot(np.array(x_x)+(3 * bar_width), y_n['rankc_v2'], label='num_rankc_v2', alpha=1, ms=10, lw=3, marker='o', c='c')
if 'Penda' in METHODS_LIST:
ax2.plot(np.array(x_x)+(3 * bar_width), y_n['penda'], label='num_penda', alpha=1, ms=10, lw=3, marker='o', c='g')
if 'T-test' in METHODS_LIST:
ax2.plot(np.array(x_x)+(3 * bar_width), y_n['ttest'], label='num_ttest', alpha=1, ms=10, lw=3, marker='o', c='m')
if 'Wilcoxon' in METHODS_LIST:
ax2.plot(np.array(x_x)+(3 * bar_width), y_n['wilcox'], label='num_wilcox', alpha=1, ms=10, lw=3, marker='o', c='r')
if 'Quantile' in METHODS_LIST:
ax2.plot(np.array(x_x)+(3 * bar_width), y_n['quantile'], label='num_quantile', alpha=1, ms=10, lw=3, marker='o', c='y')
if 'Peng_methods' in METHODS_LIST:
ax2.plot(np.array(x_x)+(3 * bar_width), y_n['peng_method'], label='num_peng', alpha=1, ms=10, lw=3, marker='o', c='k')
ax2.set_ylabel('Number of DE protein', fontsize='20')
sns.despine(left=True, bottom=True)
ax2.tick_params(labelsize=15)
ax2.legend(loc=[1.2, 0])
plt.xticks(np.array(x_x)+ 3*bar_width/2, tick_label)
plt.savefig(pe_workdir + '/pe_num_protein.pdf', dpi=800, bbox_inches='tight')
@click.command(context_settings=CONTEXT_SETTINGS)
@click.option('--parameters_path', '-p', required=True, type=click.Path(), help='parameters file')
def similarity(parameters_path):
''' Compare the similarity of DEAs '''
########################## parameters input ##############################
params = configparser.ConfigParser()
params.read(parameters_path)
# workdir
similarity_workdir = params.get('workdir', 'similarity_workdir')
# script
r_bpca_path = params.get('script path', 'r_bpca_path')
r_penda_fdr_path = params.get('script path', 'r_penda_fdr_path')
r_penda_path = params.get('script path', 'r_penda_path')
reoa_path = params.get('script path', 'reoa_path')
# preprocess parameters
LOG_LABEL = eval(params.get('preprocess', 'log_label'))
IMPUT_LABEL = eval(params.get('preprocess', 'imput_label'))
NA_LABEL = eval(params.get('preprocess', 'na_label'))
NA_RATE = eval(params.get('preprocess', 'na_rate'))
NORMALIZATION = eval(params.get('preprocess', 'normalization'))
INDEX_OLD_CHAR = eval(params.get('preprocess', 'index_old_char'))
INDEX_NEW_CHAR = eval(params.get('preprocess', 'index_new_char'))
COLUMNS_OLD_CHAR = eval(params.get('preprocess', 'columns_OLD_char'))
COLUMNS_NEW_CHAR = eval(params.get('preprocess', 'columns_new_char'))
# method parameters
CYCLE_RANKC = eval(params.get('method parameters', 'cycle_rankc'))
FDR = eval(params.get('method parameters', 'fdr'))
MAX_EXCEPTION_RANKC = eval(params.get('method parameters', 'max_exception_rankc'))
# methods comparison parameter
data_path = params.get('similarity', 'data_path')
normal_cohort_path = params.get('similarity', 'normal_cohort_path')
tumor_cohort_path = params.get('similarity', 'tumor_cohort_path')
METHODS_LIST = eval(params.get('similarity', 'methods_list'))
N_CC = eval(params.get('similarity', 'n_cc'))
# read data
data = utils.read_data(data_path)
normal_cohort = utils.read_normal_cohort(normal_cohort_path)
tumor_cohort = utils.read_tumor_cohort(tumor_cohort_path)
rd = raw_data.InputData(data=data,
normal_cohort=normal_cohort,
tumor_cohort=tumor_cohort,
specific_protein=None,
HAS_SPECIFIC_PROTEIN=False,
paired_data=None,
paired_samples=None,
HAS_PAIRED_DATA=False,
INDEX_OLD_CHAR=INDEX_OLD_CHAR,
INDEX_NEW_CHAR=INDEX_NEW_CHAR,
COLUMNS_OLD_CHAR=COLUMNS_OLD_CHAR,
COLUMNS_NEW_CHAR=COLUMNS_NEW_CHAR,
NORMALIZATION=NORMALIZATION,
LOG_LABEL=LOG_LABEL,
IMPUT_LABEL=IMPUT_LABEL,
NA_LABEL=NA_LABEL,
NA_RATE=NA_RATE)
preprocess_workdir = similarity_workdir + '/preprocess'
rd.data_preprocess(preprocess_workdir=preprocess_workdir,
r_bpca_path=r_bpca_path)
mc = methods_comp.methodsComp(rd = rd,
r_penda_path = r_penda_path,
r_penda_fdr_path = r_penda_fdr_path,
reoa_path = reoa_path,
CYCLE_RANKC = CYCLE_RANKC,
FDR = FDR,
MAX_EXCEPTION_RANKC = MAX_EXCEPTION_RANKC,
PLOT_METHODS_COMPARE_RESULT = False,
METHODS_LIST = METHODS_LIST)
mc.run_methodsComp(mc_workdir = similarity_workdir+'/methods', method_comp_label = False)
result_dir = similarity_workdir+'/methods'
similarity_lib.get_algorithm_similarity(result_dir = result_dir,
workdir = similarity_workdir,
N = N_CC)
@click.command(context_settings=CONTEXT_SETTINGS)
@click.option('--parameters_path', '-p', required=True, type=click.Path(), help='parameters file')
def type1error(parameters_path):
''' Type one error '''
########################## parameters input ##############################
params = configparser.ConfigParser()
params.read(parameters_path)
# workdir
# workdir = params.get('workdir', 'workdir')
type1_error_workdir = params.get('workdir', 'type1_error_workdir')
# script
r_bpca_path = params.get('script path', 'r_bpca_path')
r_penda_fdr_path = params.get('script path', 'r_penda_fdr_path')
r_penda_path = params.get('script path', 'r_penda_path')
reoa_path = params.get('script path', 'reoa_path')
# preprocess parameters
LOG_LABEL = eval(params.get('preprocess', 'log_label'))
IMPUT_LABEL = eval(params.get('preprocess', 'imput_label'))
NA_LABEL = eval(params.get('preprocess', 'na_label'))
NA_RATE = eval(params.get('preprocess', 'na_rate'))
NORMALIZATION = eval(params.get('preprocess', 'normalization'))
INDEX_OLD_CHAR = eval(params.get('preprocess', 'index_old_char'))
INDEX_NEW_CHAR = eval(params.get('preprocess', 'index_new_char'))
COLUMNS_OLD_CHAR = eval(params.get('preprocess', 'columns_OLD_char'))
COLUMNS_NEW_CHAR = eval(params.get('preprocess', 'columns_new_char'))
# method parameters
CYCLE_RANKC = eval(params.get('method parameters', 'cycle_rankc'))
FDR = eval(params.get('method parameters', 'fdr'))
MAX_EXCEPTION_RANKC = eval(params.get('method parameters', 'max_exception_rankc'))
# methods comparison parameter
data_path_list = eval(params.get('type 1 error', 'data_path_list'))
normal_col_path_list = eval(params.get('type 1 error', 'normal_col_path_list'))
tumor_col_path_list = eval(params.get('type 1 error', 'tumor_col_path_list'))
log_label_list = eval(params.get('type 1 error', 'log_label_list'))
na_label_list = eval(params.get('type 1 error', 'na_label_list'))
data_label_list = eval(params.get('type 1 error', 'data_label_list'))
nd_sample_size = eval(params.get('type 1 error', 'nd_sample_size'))
METHODS_LIST = eval(params.get('type 1 error', 'methods_list'))
workdir = os.path.abspath(type1_error_workdir)
imputation_workspace = type1_error_workdir + '/imputation'
data_imput_set = {}
data_normal_imput_set = {}
data_tumor_imput_set = {}
normal_col_adapt_set = {}
tumor_col_adapt_set = {}
print('Preprocess')
for i in range(len(data_label_list)):
t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('# %s: %s'%(data_label_list[i], t))
data_imput, data_normal_imput, data_tumor_imput, normal_col_adapt, tumor_col_adapt = type1_error_lib.data_preprocess(data_path = data_path_list[i],
normal_col_path = normal_col_path_list[i],
tumor_col_path = tumor_col_path_list[i],
r_bpca_path = r_bpca_path,
workdir = imputation_workspace,
data_label = data_label_list[i],
LOG_LABEL = log_label_list[i],
NA_LABEL = na_label_list[i],
NA_RATE = NA_RATE,
INDEX_OLD_CHAR = INDEX_OLD_CHAR,
INDEX_NEW_CHAR = INDEX_NEW_CHAR,
COLUMNS_OLD_CHAR = COLUMNS_OLD_CHAR,
COLUMNS_NEW_CHAR = COLUMNS_NEW_CHAR)
data_imput_set[data_label_list[i]] = data_imput
data_normal_imput_set[data_label_list[i]] = data_normal_imput
data_tumor_imput_set[data_label_list[i]] = data_tumor_imput
normal_col_adapt_set[data_label_list[i]] = normal_col_adapt
tumor_col_adapt_set[data_label_list[i]] = tumor_col_adapt
nd_data_list = []
nd_cohort_1_list = []
nd_cohort_2_list = []
for k in data_normal_imput_set.keys():
_nd_data_list, _nd_cohort_1_list, _nd_cohort_2_list = type1_error_lib.create_nulldata(data_normal_imput = data_normal_imput_set[k],
normal_col_adapt = normal_col_adapt_set[k],
ND_SAMPLE_SIZE = nd_sample_size)
nd_data_list = nd_data_list + _nd_data_list
nd_cohort_1_list = nd_cohort_1_list + _nd_cohort_1_list
nd_cohort_2_list = nd_cohort_2_list + _nd_cohort_2_list
null_data_workspace = workdir + '/null_data'
if os.path.exists(null_data_workspace) == False:
os.makedirs(null_data_workspace)
os.chdir(null_data_workspace)
# null data
tmp_workspace_list = []
for i in range(len(nd_data_list)):
tmp_workspace = null_data_workspace + '/' + '%s'%i
if os.path.exists(tmp_workspace) == False:
os.makedirs(tmp_workspace)
os.chdir(tmp_workspace)
nd_cohort_1_list[i].to_csv('./cohort_1.dat', sep='\t', index=0, header=0)
nd_cohort_2_list[i].to_csv('./cohort_2.dat', sep='\t', index=0, header=0)
nd_cohort_1_list[i].to_csv('./cohort_1.csv')
nd_cohort_2_list[i].to_csv('./cohort_2.csv')
tmp_workspace_list.append(tmp_workspace)
nd_rankc = []
nd_rankc_v1 = []
nd_rankc_v2 = []
nd_penda_fdr_result = []
nd_penda_result = []
nd_ttest_result = []
nd_wilcox_result = []
nd_penda_pro_result = []
nd_quantile_result = []
nd_peng_result = []
print('Analysis')
for i in range(len(nd_data_list)):
t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('\nData index: %s (%s)'%(i, t))
_path = tmp_workspace_list[i]
os.chdir(_path)
if 'RankComp' in METHODS_LIST:
# rankc
t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('# Rankc %s'%(t))
type1_error_lib.rankc_j2(reoa_path = reoa_path, normal_data = nd_cohort_1_list[i], tumor_data = nd_cohort_2_list[i],
workdir=_path, cycle_rankc=CYCLE_RANKC, fdr=FDR, max_exception_rankc=MAX_EXCEPTION_RANKC)
_dep = type1_error_lib.get_rankc_j2_results(_path)
_dep = _dep.iloc[:, :-1]
_dep.index = nd_cohort_1_list[i].index
_dep.columns = nd_cohort_1_list[i].columns
nd_rankc.append(_dep)
nd_rankc_v1.append(_dep // 4)
nd_rankc_v2.append(_dep % 4)
if 'Penda' in METHODS_LIST:
# Penda
t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('# Penda %s'%(t))
# _penda_fdr_up, _penda_fdr_down, _penda_fdr_result = run_penda_fdr(rscript_path = r_penda_fdr_path, workdir = _path, fdr = fdr)
_penda_up, _penda_down, _penda_result = type1_error_lib.run_penda(rscript_path = r_penda_path, workdir = _path)
# nd_penda_fdr_result.append(_penda_fdr_result)
nd_penda_result.append(_penda_result)
if 'Peng method' in METHODS_LIST:
#Peng
t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('# Peng %s'%(t))
_peng_up, _peng_down = methods_lib.run_peng_method(normal_data = nd_cohort_1_list[i],
tumor_data = nd_cohort_2_list[i])
_peng_up.to_csv('./peng_up.csv')
_peng_down.to_csv('./peng_down.csv')
_peng_result = _peng_up | _peng_down
nd_peng_result.append(_peng_result)
if 'T-test' in METHODS_LIST:
# T-test
t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('# T-test %s'%(t))
_ttest_up, _ttest_down = type1_error_lib.run_ttest(normal_run_path='./cohort_1.csv', tumor_test_path='./cohort_2.csv', workdir=_path,
Q_THRES = FDR, SAVE_OUT = True)
_ttest_result = _ttest_up + _ttest_down
nd_ttest_result.append(_ttest_result)
if 'Wilcoxon' in METHODS_LIST:
# wilcoxon
t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('# wilcox %s'%(t))
_wilcox_up, _wilcox_down = type1_error_lib.run_wilcox(normal_run_path='./cohort_1.csv', tumor_test_path='./cohort_2.csv', workdir=_path,
Q_THRES = FDR, SAVE_OUT = True)
_wilcox_result = _wilcox_up + _wilcox_down
nd_wilcox_result.append(_wilcox_result)
if 'Penda pro' in METHODS_LIST:
# Penda pro
t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('# Penda pro %s'%(t))
_penda_pro_up_qv, _penda_pro_down_qv = penda_pro.run_penda_pro(normal_path = './cohort_1.csv', tumor_path = './cohort_2.csv',
FDR = fdr, CONVERGENCE_THRESHOLD = 0.99,
MAX_CYCLE = cycle_rankc, K = 30, THRESHOLD_LH = 0.99)
_penda_pro_up = _penda_pro_up_qv < fdr
_penda_pro_down = _penda_pro_down_qv < fdr
nd_penda_pro_result = _penda_pro_up | _penda_pro_down
if 'Quantile' in METHODS_LIST:
# quantile
t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('# Quantile %s'%(t))
_quantile_up, _quantile_down = methods_lib.quantile_dep(normal_data_path = './cohort_1.csv', tumor_data_path = './cohort_2.csv',
workdir = _path, quantile = 0.05, factor = 1.2, SAVE_OUT = True)
_quantile_result = _quantile_up + _quantile_down
nd_quantile_result.append(_quantile_result)
nd_samples_fdp = []
nd_method = []
if 'RankComp' in METHODS_LIST:
for i in range(len(nd_rankc_v1)):
nd_samples_fdp.extend(((nd_rankc_v1[i] != 0).sum(axis=0) / nd_rankc_v1[i].shape[0]).values)
for col in nd_rankc_v1[i].columns:
nd_method.append('rankc_v1')
for i in range(len(nd_rankc_v2)):
nd_samples_fdp.extend(((nd_rankc_v2[i] != 0).sum(axis=0) / nd_rankc_v2[i].shape[0]).values)
for col in nd_rankc_v2[i].columns:
nd_method.append('rankc_v2')
if 'Penda' in METHODS_LIST:
for i in range(len(nd_penda_result)):
nd_samples_fdp.extend(((nd_penda_result[i] != 0).sum(axis=0) / nd_penda_result[i].shape[0]).values)
for col in nd_penda_result[i].columns:
nd_method.append('penda')
if 'Peng method' in METHODS_LIST:
for i in range(len(nd_peng_result)):
nd_samples_fdp.extend(((nd_peng_result[i] != 0).sum(axis=0) / nd_peng_result[i].shape[0]).values)
for col in nd_peng_result[i].columns:
nd_method.append('peng_method')
if 'Penda pro' in METHODS_LIST:
for i in range(len(nd_penda_pro_result)):
nd_samples_fdp.extend(((nd_penda_pro_result[i] != 0).sum(axis=0) / nd_penda_pro_result[i].shape[0]).values)
for col in nd_penda_pro_result[i].columns:
nd_method.append('penda_pro')
if 'T-test' in METHODS_LIST:
for i in range(len(nd_ttest_result)):
nd_samples_fdp.extend(((nd_ttest_result[i] != 0).sum(axis=0) / nd_ttest_result[i].shape[0]).values)
for col in nd_ttest_result[i].columns:
nd_method.append('T-test')
if 'Wilcoxon' in METHODS_LIST:
for i in range(len(nd_wilcox_result)):
nd_samples_fdp.extend(((nd_wilcox_result[i] != 0).sum(axis=0) / nd_wilcox_result[i].shape[0]).values)
for col in nd_wilcox_result[i].columns:
nd_method.append('wilcoxon')
if 'Quantile' in METHODS_LIST:
for i in range(len(nd_quantile_result)):
nd_samples_fdp.extend(((nd_quantile_result[i] != 0).sum(axis=0) / nd_quantile_result[i].shape[0]).values)
for col in nd_quantile_result[i].columns:
nd_method.append('quantile')
nd_fpr_result = pd.DataFrame({'FDP': nd_samples_fdp, 'method': nd_method})
plt.rcParams['font.sans-serif'] = ['SimHei']
sns.set(font_scale=1.5)
# plt.rcParams['savefig.dpi'] = 600
# plt.rcParams['figure.dpi'] = 300
plt.rcParams['axes.unicode_minus']=False
plt.figure(figsize=(12, 8))
sns.boxplot(x = 'method', y='FDP', data=nd_fpr_result)
sns.swarmplot(x = 'method', y="FDP", data=nd_fpr_result, color ='k',size = 3,alpha = 0.8)
plt.hlines(0.05, -9, 9,color="red")
plt.xlabel('methods', fontsize=20)
plt.ylabel('FPR', fontsize=20)
plt.title("Null data with FPR", fontsize=25)
# plt.title("The precisions of DE protein by RankComp(v1-v2)-Penda-Wilcox: %s"%title, fontsize=25)
# plt.legend(fontsize=20)
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.savefig(workdir +'/type_one_error.pdf', dpi=800, format='pdf')
plt.show()
merge_list = []
if 'RankComp' in METHODS_LIST:
nd_param_sn_rankc_v1 = type1_error_lib.get_nd_param_sn(nd_result = nd_rankc_v1,
nd_set = nd_data_list,
method = 'rankc_v1')
nd_param_sn_rankc_v2 = type1_error_lib.get_nd_param_sn(nd_result = nd_rankc_v2,
nd_set = nd_data_list,
method = 'rankc_v2')
merge_list.append(nd_param_sn_rankc_v1)
merge_list.append(nd_param_sn_rankc_v2)
if 'Penda' in METHODS_LIST:
nd_param_sn_penda = type1_error_lib.get_nd_param_sn(nd_result = nd_penda_result,
nd_set = nd_data_list,
method = 'penda')
merge_list.append(nd_param_sn_penda)