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Copy pathCausal_HR_column3.py
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Causal_HR_column3.py
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#import matplotlib.pyplot as plt
import pandas as pd
import scipy as scipy
import numpy as np
from scipy import stats
from scipy.stats import chi2
from scipy.stats import fisher_exact
from joblib import Parallel, delayed
from scipy.interpolate import UnivariateSpline
from scipy.signal import find_peaks
import heapq
from sklearn.cluster import DBSCAN
import pingouin as pg
from numba import njit
import collections
import warnings
import time
import rpy2.robjects as robjects
from scipy.stats import wasserstein_distance
def myrwaddR(a, b):
mydata = np.concatenate((a, b), axis=0)
if np.allclose(mydata, a[0], equal_nan=True):
return pd.DataFrame(np.array([[1, 0, 0, 0, 0]]), columns=['pval', 'dwass2', 'perc_loc', 'perc_size', 'perc_shape'])
rwaddR = robjects.r('''
library("waddR")
myf <- function(a,b){
spec.output<-c("pval","d.wass^2","perc.loc","perc.size","perc.shape")
wasserstein.test(a,b,method="SP",permnum=10000)[spec.output]-> re
return(re)
}
''')
waddR_re = rwaddR(robjects.FloatVector(a), robjects.FloatVector(b))
waddR_pd = pd.DataFrame(np.array([np.array(waddR_re)]), columns=[
'pval', 'dwass2', 'perc_loc', 'perc_size', 'perc_shape'])
return waddR_pd
@njit
def logrank_test(d_myfactor, d_survivalMonth, d_death_observed, d_mylength):
nall_timetotal = d_mylength
n2i_timetotal = 0
d2i_deathtotal = 0 # observed_death
T2i_sum = 0.0 # expected
patient_count_death = 0
V2i_sum = 0.0
# [HR, chisq, observed, expected]
for n in range(0, d_mylength, 1):
if (d_death_observed[n] == 1):
patient_count_death += 1
if (d_myfactor[n] == 1):
n2i_timetotal += 1
if (d_death_observed[n] == 1):
d2i_deathtotal += 1
n = 0
i = 0
while i < (d_mylength):
dall_death_censored = 0
dall_death = 0
d2i_death = 0
d2i_death_censored = 0
# T2i = 0.0
# V2i = 0.0
if ((i == (d_mylength - 1)) or (d_survivalMonth[i] != d_survivalMonth[i + 1])):
while (n <= i):
dall_death_censored += 1
if d_death_observed[n] == 1:
dall_death += 1
if (d_death_observed[n] == 1 and d_myfactor[n] == 1):
d2i_death += 1
if (d_myfactor[n] == 1):
d2i_death_censored += 1
n += 1
T2i = dall_death / nall_timetotal * n2i_timetotal
T2i_sum = T2i_sum + T2i
V2i = (n2i_timetotal / nall_timetotal) * (1 - (n2i_timetotal / nall_timetotal)) * (
(nall_timetotal - dall_death) / (nall_timetotal - 0.99999)) * dall_death
V2i_sum = V2i_sum + V2i
nall_timetotal = nall_timetotal - dall_death_censored
n2i_timetotal = n2i_timetotal - d2i_death_censored
i += 1
# chisq = (observed_death - expected) * (observed_death - expected) / (myvar + 0.00001)
chisq = (d2i_deathtotal - T2i_sum) * \
(d2i_deathtotal - T2i_sum) / (V2i_sum + 0.00001)
# HR = (observed_death * (patient_count_death - expected)) / (expected * (patient_count_death - observed_death) + 0.00001)
if V2i_sum == 0:
HR = 1
else:
HR = (d2i_deathtotal * (patient_count_death - T2i_sum)) / (
T2i_sum * (patient_count_death - d2i_deathtotal) + 0.00001)
# [HR, chisq, observed, expected]
d_hrresult = [HR, chisq, d2i_deathtotal, T2i_sum]
# pvalue = chisq
# pvalue = chi2.sf(chisq, 1)
# pvalue chi2.sf(d_hrresult[1], 1)
re = np.array([1.0, 1.0, 0.0, 0.0])
re[0] = HR
re[1] = chisq
re[2] = np.count_nonzero(d_myfactor == 0)
re[3] = (d_myfactor == 1).sum()
return re
class RNA_groupsurvival:
def __init__(self, mydata_survival, nstep=1000, bw_adjust=0.5, eps_score=10, my_min_samples=7, pvaluecut=0.2, HRcut=0.7, cut_pos_1=7, cut_pos_0=7, cut_neg_1=7, cut_neg_0=7):
self.mydata_survival = mydata_survival
self.nstep = nstep
self.bw_adjust = bw_adjust
self.eps_score = eps_score
self.my_min_samples = my_min_samples
self.pvaluecut = pvaluecut
self.HRcut = HRcut
self.cut_pos_1 = cut_pos_1
self.cut_pos_0 = cut_pos_0
self.cut_neg_1 = cut_neg_1
self.cut_neg_0 = cut_neg_0
def judgeconstant(self, a, b):
#np.allclose(a, 1, atol=0.001)
mydata = np.concatenate((a, b), axis=0)
return np.allclose(mydata, a[0], equal_nan=True)
def standardized_wasserstein_distance(self, a, b):
if self.judgeconstant(a, b):
return 0
numerator = wasserstein_distance(a, b)
denominator = np.std(np.concatenate([a, b]))
stand_wassd = 0
if denominator != 0:
stand_wassd = numerator / denominator
return stand_wassd
def getDBSCAN(self, X, eps_score, my_min_samples):
def do(X, eps_score, my_min_samples):
topcutoff = 0
X_sort = np.sort(X, axis=0)
my_std = np.std(X_sort)
clustering = DBSCAN(eps=0.00001+(my_std / eps_score), min_samples=my_min_samples).fit(X_sort.reshape(-1, 1))
clustering_diff = (np.diff(clustering.labels_))
clustering_diff2 = np.where(
clustering_diff != 0, 1, clustering_diff)
clustering_diff_points = np.where(clustering_diff2 == 1)[0]
if clustering_diff_points.size > 0:
topcutoff = X_sort[clustering_diff_points][-1]
return topcutoff
else:
return topcutoff
topcutoff = do(X, eps_score, my_min_samples)
if topcutoff == 0:
topcutoff = do(X, eps_score * 2, my_min_samples)
return topcutoff
def get_peakdiff(self, negative_density, positive_density, negative_numpy, x_lab):
diff_negative_positive = negative_density - positive_density
diff_negative_peaks, diff_negative_peakheights = find_peaks(
diff_negative_positive, height=0)
if len(diff_negative_peaks) == 0:
return 0, 0, 0
negative_diff_heapq = diff_negative_peaks[
heapq.nlargest(2, range(len(diff_negative_peakheights['peak_heights'])),
diff_negative_peakheights['peak_heights'].take)]
negative_difftop1 = x_lab[negative_diff_heapq[0]]
negative_difftop1_std = negative_difftop1 + 1.5 * \
np.std(negative_numpy[negative_numpy < negative_difftop1])
if len(negative_diff_heapq) > 1:
negative_difftop2 = x_lab[negative_diff_heapq[1]]
else:
negative_difftop2 = x_lab[0]
return negative_difftop1, negative_difftop1_std, negative_difftop2
def get_peaktop(self, negative_numpy, bw_adjust, x_lab):
#singular matrix
if len(np.unique(negative_numpy)) == 1:
negative_numpy = negative_numpy + (np.random.randint(low=1, high=10, size=len(negative_numpy), dtype='l') / 100.0)
negative_kde = stats.gaussian_kde(dataset=negative_numpy, bw_method='scott')
negative_kde.set_bandwidth(negative_kde.factor * bw_adjust)
negative_density = negative_kde(x_lab)
negative_peaks, negative_peakheights = find_peaks(
negative_density, height=0)
if len(negative_peaks) == 0:
return 0, 0, 0, negative_density
negative_heapq = heapq.nlargest(2, range(len(negative_peakheights['peak_heights'])),
negative_peakheights['peak_heights'].take)
negative_maxtop1_index = negative_peaks[negative_heapq[0]]
negative_maxtop1 = x_lab[negative_maxtop1_index]
# std as normal distribution
negative_maxstdtop1 = negative_maxtop1 + 1.5 * \
np.std(negative_numpy[negative_numpy < negative_maxtop1])
if len(negative_heapq) > 1:
negative_maxtop2 = x_lab[negative_peaks[negative_heapq[1]]]
else:
negative_maxtop2 = x_lab[0]
return negative_maxtop1, negative_maxstdtop1, negative_maxtop2, negative_density
def get_crosspoint(self, negative_density, positive_density, x_lab):
a = 0 - (np.abs(negative_density - positive_density))
b = np.abs(min(a)) + a
cross_peaks, cross_peakheights = find_peaks(b, height=0)
# cross peaks rank by positive_density
cross_peaks_positive_density = positive_density[cross_peaks] * 1000
cross_heapq = cross_peaks[
heapq.nlargest(2, range(len(cross_peaks_positive_density)), cross_peaks_positive_density.take)]
if len(cross_heapq) == 0:
cross_top1 = x_lab[0]
cross_top2 = x_lab[0]
elif len(cross_heapq) == 1:
cross_top1 = x_lab[cross_heapq[0]]
cross_top2 = x_lab[cross_heapq[0]]
else:
cross_top1 = x_lab[cross_heapq[0]]
cross_top2 = x_lab[cross_heapq[1]]
return cross_top1, cross_top2,
def getpeak(self, negative_group, positive_group):
if self.judgeconstant(negative_group, positive_group):
return 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
nstep = self.nstep
bw_adjust = self.bw_adjust
negative_numpy = negative_group
positive_numpy = positive_group
allscore = (np.append(negative_numpy, positive_numpy))
x_lab = np.linspace(min(allscore), max(allscore), nstep)
# max
negative_top1, negative_top1std, negative_top2, negative_density = self.get_peaktop(negative_numpy, bw_adjust,
x_lab)
positive_top1, positive_top1std, positive_top2, positive_density = self.get_peaktop(positive_numpy, bw_adjust,
x_lab)
# diff
negative_difftop1, negative_difftop1_std, negative_difftop2 = self.get_peakdiff(negative_density,
positive_density,
negative_numpy, x_lab)
positive_difftop1, positive_difftop1_std, positive_difftop2 = self.get_peakdiff(positive_density,
negative_density,
positive_numpy, x_lab)
# cross point peak in highest positive density
cross_top1, cross_top2 = self.get_crosspoint(
negative_density, positive_density, x_lab)
# DBSCAN
negative_DBSCANtop = self.getDBSCAN(
negative_numpy, self.eps_score, self.my_min_samples)
positive_DBSCANtop = self.getDBSCAN(
positive_numpy, self.eps_score, self.my_min_samples)
a = 0 - (np.abs(negative_density - positive_density))
b = np.abs(min(a)) + a
# plt.plot(x_lab, positive_density - negative_density, color='#ffd600')
# plt.plot(x_lab, b, color='#00c3ff')
# plt.plot(x_lab, negative_density, color='#61b15a')
# plt.plot(x_lab, positive_density, color='#c75643')
# plt.hlines(0, 0, 90, color="black")
return negative_top1, negative_top1std, negative_top2, positive_top1, positive_top1std, positive_top2, negative_difftop1, negative_difftop1_std, negative_difftop2, positive_difftop1, positive_difftop1_std, positive_difftop2, cross_top1, cross_top2, negative_DBSCANtop, positive_DBSCANtop
def peak_HR(self, peak_x, negative_group, positvie_group, prefix):
if peak_x == 0:
redf = collections.OrderedDict({prefix + '_cut': [0],
prefix + '_neg_HR': [1],
prefix + '_neg_pvalue': [1],
prefix + '_neg_1': [0],
prefix + '_neg_1_mscore': [0],
prefix + '_neg_0': [0],
prefix + '_neg_0_mscore': [0],
prefix + '_pos_HR': [1],
prefix + '_pos_pvalue': [1],
prefix + '_pos_1': [0],
prefix + '_pos_1_mscore': [0],
prefix + '_pos_0': [0],
prefix + '_pos_0_mscore': [0]})
return redf
# peak_x = 30
#negative_group.loc[:, 'myfpkm_level'] = pd.cut(negative_group['fpkm'], [-0.1, peak_x, 1000000], labels=[1, 0])
#positvie_group['myfpkm_level'] = pd.cut(positvie_group['fpkm'], [-0.1, peak_x, 10000000], labels=[1, 0])
# print(negative_group)
negative_group.loc[:, 'myfpkm_level'] = negative_group.fpkm.apply(
lambda a: 1 if (a < peak_x) else 0)
positvie_group.loc[:, 'myfpkm_level'] = positvie_group.fpkm.apply(
lambda a: 1 if (a < peak_x) else 0)
neg_HR, neg_chisq, neg_0, neg_1 = logrank_test(negative_group['myfpkm_level'].to_numpy(),
negative_group['survivalMonth'].to_numpy(
),
negative_group['death_observed'].to_numpy(
),
negative_group['myfpkm_level'].to_numpy().size)
pos_HR, pos_chisq, pos_0, pos_1 = logrank_test(positvie_group['myfpkm_level'].to_numpy(),
positvie_group['survivalMonth'].to_numpy(
),
positvie_group['death_observed'].to_numpy(
),
positvie_group['myfpkm_level'].to_numpy().size)
neg_pvalue = chi2.sf(neg_chisq, 1)
pos_pvalue = chi2.sf(pos_chisq, 1)
redf = collections.OrderedDict({prefix + '_cut': [peak_x],
prefix + '_neg_HR': [neg_HR],
prefix + '_neg_pvalue': [neg_pvalue],
prefix + '_neg_1': [neg_1],
prefix + '_neg_1_mscore': [
(negative_group.myscore[negative_group.myfpkm_level == 1]).median()],
prefix + '_neg_0': [neg_0],
prefix + '_neg_0_mscore': [
(negative_group.myscore[negative_group.myfpkm_level == 0]).median()],
prefix + '_pos_HR': [pos_HR],
prefix + '_pos_pvalue': [pos_pvalue],
prefix + '_pos_1': [pos_1],
prefix + '_pos_1_mscore': [
(positvie_group.myscore[positvie_group.myfpkm_level == 1]).median()],
prefix + '_pos_0': [pos_0],
prefix + '_pos_0_mscore': [
(positvie_group.myscore[positvie_group.myfpkm_level == 0]).median()]})
return redf
def HR_corr(self, gene, mydata):
# print(mydata)
pvaluecut = self.pvaluecut
HRcut = self.HRcut
b = mydata[mydata.gene == gene]
# print(b)
mycut = (b[b.columns[b.columns.str.endswith('_cut')]]).to_numpy()[0]
mycut_neg_HR = (
b[b.columns[b.columns.str.endswith('_neg_HR')]]).to_numpy()[0]
mycut_pos_HR = (
b[b.columns[b.columns.str.endswith('_pos_HR')]]).to_numpy()[0]
neg_1_mscore = (
b[b.columns[b.columns.str.endswith('_neg_1_mscore')]]).to_numpy()[0]
pos_1_mscore = (
b[b.columns[b.columns.str.endswith('_pos_1_mscore')]]).to_numpy()[0]
mycut_neg_HR[mycut_neg_HR < 0.05] = 0.05
mycut_neg_HR[mycut_neg_HR > 20] = 20
mycut_pos_HR[mycut_pos_HR < 0.05] = 0.05
mycut_pos_HR[mycut_pos_HR > 20] = 20
log_neg_HR = np.log2(1 / mycut_neg_HR)
log_pos_HR = np.log2(1 / mycut_pos_HR)
cutHR = pd.DataFrame(
{'mycut_numpy': mycut, 'log_neg_HR': log_neg_HR, 'log_pos_HR': log_pos_HR,
'neg_1_mscore': neg_1_mscore, 'pos_1_mscore': pos_1_mscore
},
)
cutHR.dropna(axis='rows', how='any', inplace=True)
# print(cutHR)
cut_HR_pearsonr_neg = scipy.stats.pearsonr(
cutHR.mycut_numpy, cutHR.log_neg_HR)
cut_HR_spearmanr_neg = scipy.stats.spearmanr(
cutHR.mycut_numpy, cutHR.log_neg_HR)
socre_HR_pearsonr_neg = scipy.stats.pearsonr(
cutHR.neg_1_mscore, cutHR.log_neg_HR)
socre_HR_spearmanr_neg = scipy.stats.spearmanr(
cutHR.neg_1_mscore, cutHR.log_neg_HR)
cut_HR_pearsonr_pos = scipy.stats.pearsonr(
cutHR.mycut_numpy, cutHR.log_pos_HR)
cut_HR_spearmanr_pos = scipy.stats.spearmanr(
cutHR.mycut_numpy, cutHR.log_pos_HR)
socre_HR_pearsonr_pos = scipy.stats.pearsonr(
cutHR.pos_1_mscore, cutHR.log_pos_HR)
socre_HR_spearmanr_pos = scipy.stats.spearmanr(
cutHR.pos_1_mscore, cutHR.log_pos_HR)
# <0.2 and selectcutoff
cutoff_neg_HR = np.array([np.nan])
cutoff_pos_HR = np.array([np.nan])
neg_pvalue = (
b[b.columns[b.columns.str.endswith('_neg_pvalue')]]).to_numpy()[0]
pos_pvalue = (
b[b.columns[b.columns.str.endswith('_pos_pvalue')]]).to_numpy()[0]
pos_1 = (b[b.columns[b.columns.str.endswith('_pos_1')]]).to_numpy()[0]
pos_0 = (b[b.columns[b.columns.str.endswith('_pos_0')]]).to_numpy()[0]
pos_mypercent = pos_0 / (pos_1 + pos_0)
neg_1 = (b[b.columns[b.columns.str.endswith('_neg_1')]]).to_numpy()[0]
neg_0 = (b[b.columns[b.columns.str.endswith('_neg_0')]]).to_numpy()[0]
neg_mypercent = neg_0 / (neg_1 + neg_0)
count_neg_pvalue = 0
count_pos_pvalue = 0
select_negcutoff = -1
select_negHR = 1
select_negpavlue = 1
select_poscutoff = -1
select_posHR = 1
select_pospavlue = 1
for i in range(len(mycut)):
if (mycut_neg_HR[i] > (1 / HRcut)) and neg_pvalue[i] <= pvaluecut and neg_1[i] > self.cut_neg_1 and neg_0[i] > self.cut_neg_0:
count_neg_pvalue = count_neg_pvalue + 1
cutoff_neg_HR = np.append(cutoff_neg_HR, mycut[i])
cutoff_neg_HR = cutoff_neg_HR[~np.isnan(cutoff_neg_HR)]
if mycut_neg_HR[i] > select_negHR:
select_negHR = mycut_neg_HR[i]
select_negpavlue = neg_pvalue[i]
select_negcutoff = mycut[i]
if mycut_pos_HR[i] < HRcut and pos_pvalue[i] <= pvaluecut and pos_1[i] > self.cut_pos_1 and pos_0[i] > self.cut_pos_0:
count_pos_pvalue = count_pos_pvalue + 1
cutoff_pos_HR = np.append(cutoff_pos_HR, mycut[i])
cutoff_pos_HR = cutoff_pos_HR[~np.isnan(cutoff_pos_HR)]
if mycut_pos_HR[i] < select_posHR:
select_posHR = mycut_pos_HR[i]
select_pospavlue = pos_pvalue[i]
select_poscutoff = mycut[i]
if not np.isnan(min(cutoff_pos_HR)):
pos_cut_percent = min(
pos_mypercent[np.where(mycut == min(cutoff_pos_HR))])
neg_cut_percent = min(
neg_mypercent[np.where(mycut == min(cutoff_pos_HR))])
else:
pos_cut_percent = np.nan
neg_cut_percent = np.nan
try:
cut_mscore_HR_neg = pg.linear_regression(
cutHR[['mycut_numpy', 'neg_1_mscore']], cutHR['log_neg_HR'], remove_na=True)
cut_mscore_HR_neg_r2 = cut_mscore_HR_neg.adj_r2[0]
except:
cut_mscore_HR_neg_r2 = 0
try:
cut_mscore_HR_pos = pg.linear_regression(
cutHR[['mycut_numpy', 'pos_1_mscore']], cutHR['log_pos_HR'], remove_na=True)
cut_mscore_HR_pos_r2 = cut_mscore_HR_pos.adj_r2[0],
except:
cut_mscore_HR_pos_r2 = 0
HRcorr_df = pd.DataFrame(
{'gene': gene,
'cut_HR_pearsonr_neg_r': cut_HR_pearsonr_neg[0],
'cut_HR_pearsonr_neg_pvalue': cut_HR_pearsonr_neg[1],
'cut_HR_spearmanr_neg_r': cut_HR_spearmanr_neg[0],
'cut_HR_spearmanr_neg_pvalue': cut_HR_spearmanr_neg[1],
'cut_HR_pearsonr_pos_r': cut_HR_pearsonr_pos[0],
'cut_HR_pearsonr_pos_pvalue': cut_HR_pearsonr_pos[1],
'cut_HR_spearmanr_pos_r': cut_HR_spearmanr_pos[0],
'cut_HR_spearmanr_pos_pvalue': cut_HR_spearmanr_pos[1],
'socre_HR_pearsonr_neg_r': socre_HR_pearsonr_neg[0],
'socre_HR_pearsonr_neg_pvalue': socre_HR_pearsonr_neg[1],
'socre_HR_spearmanr_neg_r': socre_HR_spearmanr_neg[0],
'socre_HR_spearmanr_neg_pvalue': socre_HR_spearmanr_neg[1],
'socre_HR_pearsonr_pos_r': socre_HR_pearsonr_pos[0],
'socre_HR_pearsonr_pos_pvalue': socre_HR_pearsonr_pos[1],
'socre_HR_spearmanr_pos_r': socre_HR_spearmanr_pos[0],
'socre_HR_spearmanr_pos_pvalue': socre_HR_spearmanr_pos[1],
'cut_mscore_HR_neg_r2': cut_mscore_HR_neg_r2,
'cut_mscore_HR_pos_r2': cut_mscore_HR_pos_r2,
'count_neg_pvalue': count_neg_pvalue,
'count_pos_pvalue': count_pos_pvalue,
'select_negcutoff': select_negcutoff,
'select_negHR': select_negHR,
'select_negpavlue': select_negpavlue,
'select_poscutoff': select_poscutoff,
'select_posHR': select_posHR,
'select_pospavlue': select_pospavlue,
'neg_cut_percent': neg_cut_percent,
'pos_cut_percent': pos_cut_percent,
'pos_neg_cut_ratio': pos_cut_percent / (neg_cut_percent + 0.0000001),
},
index=[0])
#allresult = mydata.merge(HRcorr_df, left_on='gene', right_on='gene')
return HRcorr_df
def distrution_pipelinemap(self, genefpkm):
# mydata_factor must be sorted!
gene = genefpkm.name
#gene_FPKM = mydata_FPKM[mydata_FPKM.gene == gene]
#c1 = mydata_factor.merge(gene_FPKM, left_on='patient_id', right_on='patient_id',how='inner')
#c1= mydata_FPKM2[[gene,'death_observed','survivalMonth','myscore','mylevel']]
my_dict = {'fpkm':genefpkm,'death_observed':self.mydata_survival.death_observed,'survivalMonth':self.mydata_survival.survivalMonth,'myscore':self.mydata_survival.myscore,'mylevel':self.mydata_survival.mylevel}
c1 = pd.DataFrame(my_dict)
try:
pearson = c1.myscore.corr(c1.fpkm, method='pearson')
spearman = c1.myscore.corr(c1.fpkm, method='spearman')
except:
print(gene)
# print(c1.describe(include=[np.number]))
fpkm_numpy = c1.fpkm.to_numpy()
allq10 = np.quantile(fpkm_numpy, 0.10)
allq25 = np.quantile(fpkm_numpy, 0.25)
allq50 = np.quantile(fpkm_numpy, 0.50)
allq75 = np.quantile(fpkm_numpy, 0.75)
allq90 = np.quantile(fpkm_numpy, 0.90)
allmean = np.mean(fpkm_numpy)
allmeanq90 = allq90 / 2
all2FPKM = 2
positive_group = (c1[c1.mylevel == True]).copy()
negative_group = (c1[c1.mylevel == False]).copy()
positive_value = positive_group.fpkm.to_numpy()
negative_value = negative_group.fpkm.to_numpy()
ttest = scipy.stats.ttest_ind(
positive_value, negative_value, equal_var=False)
ks_2samp_pvalue = 1
try:
ks_2samp = scipy.stats.ks_2samp(positive_value, negative_value)
ks_2samp_pvalue = ks_2samp.pvalue
except:
ks_2samp_pvalue = 1
waddR_pd_pval = 1
waddR_pd_dwass2 = 0
waddR_pd_perc_loc = 0
waddR_pd_perc_size = 0
waddR_pd_perc_shape = 0
'''
try:
waddR_re = myrwaddR(positive_value, negative_value)
waddR_pd_pval = waddR_re.pval
waddR_pd_dwass2 = waddR_re.dwass2
waddR_pd_perc_loc = waddR_re.perc_loc
waddR_pd_perc_size = waddR_re.perc_size
waddR_pd_perc_shape = waddR_re.perc_shape
except:
pass
'''
negative_numpy = negative_value
positive_numpy = positive_value
'''
#add singular matrix
if len(np.unique(negative_numpy)) == 1:
negative_numpy = negative_numpy + (
np.random.randint(low=1, high=10, size=len(negative_numpy), dtype='l') / 100.0)
positive_numpy =positive_value
if len(np.unique(positive_numpy)) == 1:
positive_numpy = positive_numpy + np.random.randint(low=1, high=10, size=len(positive_numpy),
dtype='l') / 100.0
'''
negative_top1, negative_top1std, negative_top2, positive_top1, positive_top1std, positive_top2, negative_difftop1, negative_difftop1_std, negative_difftop2, positive_difftop1, positive_difftop1_std, positive_difftop2, cross_top1, cross_top2, negative_DBSCANtop, positive_DBSCANtop = self.getpeak(
negative_numpy, positive_numpy)
# peak_x = 30
negative_top1_HR = self.peak_HR(
negative_top1, negative_group, positive_group, 'negative_top1')
negative_top1std_HR = self.peak_HR(
negative_top1std, negative_group, positive_group, 'negative_top1std')
negative_top2_HR = self.peak_HR(
negative_top2, negative_group, positive_group, 'negative_top2')
positive_top1_HR = self.peak_HR(
positive_top1, negative_group, positive_group, 'positive_top1')
positive_top1std_HR = self.peak_HR(
positive_top1std, negative_group, positive_group, 'positive_top1std')
positive_top2_HR = self.peak_HR(
positive_top2, negative_group, positive_group, 'positive_top2')
negative_difftop1_HR = self.peak_HR(
negative_difftop1, negative_group, positive_group, 'negative_difftop1')
negative_difftop1_std_HR = self.peak_HR(negative_difftop1_std, negative_group, positive_group,
'negative_difftop1_std')
negative_difftop2_HR = self.peak_HR(
negative_difftop2, negative_group, positive_group, 'negative_difftop2')
positive_difftop1_HR = self.peak_HR(
positive_difftop1, negative_group, positive_group, 'positive_difftop1')
positive_difftop1_std_HR = self.peak_HR(positive_difftop1_std, negative_group, positive_group,
'positive_difftop1_std')
positive_difftop2_HR = self.peak_HR(
positive_difftop2, negative_group, positive_group, 'positive_difftop2')
cross_top1_HR = self.peak_HR(
cross_top1, negative_group, positive_group, 'cross_top1')
cross_top2_HR = self.peak_HR(
cross_top2, negative_group, positive_group, 'cross_top2')
negative_DBSCANtop_HR = self.peak_HR(
negative_DBSCANtop, negative_group, positive_group, 'negative_DBSCANtop')
positive_DBSCANtop_HR = self.peak_HR(
positive_DBSCANtop, negative_group, positive_group, 'positive_DBSCANtop')
allq10_df_HR = self.peak_HR(
allq10, negative_group, positive_group, 'allq10')
allq25_df_HR = self.peak_HR(
allq25, negative_group, positive_group, 'allq25')
allq50_df_HR = self.peak_HR(
allq50, negative_group, positive_group, 'allq50')
allq75_df_HR = self.peak_HR(
allq75, negative_group, positive_group, 'allq75')
allq90_df_HR = self.peak_HR(
allq90, negative_group, positive_group, 'allq90')
allmean_df_HR = self.peak_HR(
allmean, negative_group, positive_group, 'allmean')
allmeanq90_df_HR = self.peak_HR(
allmeanq90, negative_group, positive_group, 'allmeanq90')
all2FPKM_df_HR = self.peak_HR(
all2FPKM, negative_group, positive_group, 'all2FPKM')
stat_df = collections.OrderedDict(
{'gene': [gene], 'pearson': [pearson], 'spearman': [spearman], 'allq10': [allq10], 'allq25': [allq25],
'allq50': [allq50],
'allq75': [allq75], 'allq90': [allq90], 'allmean': [allmean], 'allmeanq90': [allmeanq90],
'mylevellow_std': [np.std(negative_value)],
'mylevellow_q25': [np.quantile(negative_value, 0.25)],
'mylevellow_median': [np.median(negative_value)],
'mylevellow_q75': [np.quantile(negative_value, 0.75)],
'mylevellow_q95': [np.quantile(negative_value, 0.95)],
'mylevelhigh_std': [np.std(positive_value)],
'mylevelhigh_q25': [np.quantile(positive_value, 0.25)],
'mylevelhigh_median': [np.median(positive_value)],
'mylevelhigh_q75': [np.quantile(positive_value, 0.75)],
'mylevelhigh_q95': [np.quantile(positive_value, 0.95)],
'foldchange_median': np.median(positive_value)/(np.median(negative_value)+0.001),
'foldchange_mean': np.mean(positive_value)/(np.mean(negative_value)+0.001),
'foldchange_q25': np.quantile(positive_value, 0.25)/(np.quantile(negative_value, 0.25)+0.001),
'foldchange_q75': np.quantile(positive_value, 0.75)/(np.quantile(negative_value, 0.75)+0.001),
'foldchange_q90': np.quantile(positive_value, 0.90)/(np.quantile(negative_value, 0.90)+0.001),
'ttest': [ttest.pvalue], 'ks_2samp': [ks_2samp_pvalue],
'waddR_pval': waddR_pd_pval,
'stand_wassd': self.standardized_wasserstein_distance(positive_value, negative_value),
'waddR_dwass2': waddR_pd_dwass2,
'waddR_perc_loc': waddR_pd_perc_loc,
'waddR_perc_size': waddR_pd_perc_size,
'waddR_perc_shape': waddR_pd_perc_shape
})
HR_result0 = stat_df | negative_top1_HR | negative_top1std_HR | negative_top2_HR | positive_top1_HR | positive_top1std_HR | positive_top2_HR | negative_difftop1_HR | negative_difftop1_std_HR \
| negative_difftop2_HR | positive_difftop1_HR | positive_difftop1_std_HR | positive_difftop2_HR | cross_top1_HR | cross_top2_HR | allq10_df_HR | allq25_df_HR | allq50_df_HR | allq75_df_HR | allq90_df_HR | allmean_df_HR | allmeanq90_df_HR | all2FPKM_df_HR
HR_result = pd.DataFrame.from_dict(HR_result0)
if self.judgeconstant(positive_value, negative_value):
HRcorr_df = pd.DataFrame({'gene': gene,
'cut_HR_pearsonr_neg_r': 0,
'cut_HR_pearsonr_neg_pvalue': 1,
'cut_HR_spearmanr_neg_r': 0,
'cut_HR_spearmanr_neg_pvalue': 1,
'cut_HR_pearsonr_pos_r': 0,
'cut_HR_pearsonr_pos_pvalue': 1,
'cut_HR_spearmanr_pos_r': 0,
'cut_HR_spearmanr_pos_pvalue': 1,
'socre_HR_pearsonr_neg_r': 0,
'socre_HR_pearsonr_neg_pvalue': 1,
'socre_HR_spearmanr_neg_r': 0,
'socre_HR_spearmanr_neg_pvalue': 1,
'socre_HR_pearsonr_pos_r': 0,
'socre_HR_pearsonr_pos_pvalue': 1,
'socre_HR_spearmanr_pos_r': 0,
'socre_HR_spearmanr_pos_pvalue': 1,
'cut_mscore_HR_neg_r2': 0,
'cut_mscore_HR_pos_r2': 1,
'count_neg_pvalue': 0,
'count_pos_pvalue': 0,
'select_negcutoff': 0,
'select_negHR': 1,
'select_negpavlue': 1,
'select_poscutoff': 0,
'select_posHR': 1,
'select_pospavlue': 1,
'neg_cut_percent': 0,
'pos_cut_percent': 0,
'pos_neg_cut_ratio': 0,
}, index=[0])
all_result = HR_result.merge(HRcorr_df, left_on='gene', right_on='gene')
all_result['ttest'] = 1
all_result['pearson'] = 0
all_result['spearman'] = 0
else:
HRcorr_df = self.HR_corr(gene, HR_result)
all_result = HR_result.merge(HRcorr_df, left_on='gene', right_on='gene')
#print(all_result)
# positive_difftop1_cut-negative_difftop1_cut
all_result.loc[:, 'CS_BFscore'] = (
all_result['positive_difftop1_cut'] - all_result['negative_difftop1_cut'])
# (positive_difftop1_cut-negative_difftop1_cut)/mylevellow_median
all_result.loc[:, 'CS_foldchang'] = (all_result['positive_difftop1_cut'] - all_result[
'negative_difftop1_cut']) / (all_result['mylevellow_median'] + 0.001)
# (positive_difftop1_cut-negative_difftop1_cut)/mylevellow_std
all_result.loc[:, 'CS_zscore'] = (all_result['positive_difftop1_cut'] - all_result['negative_difftop1_cut']) / (
all_result['mylevellow_std'] + 0.001)
return all_result.iloc[0,:]
'''
# test
mydata_FPKM6 = pd.read_pickle(
'/share_storage/home/nacoo/enst/mydata_FPKM6.pickle')
start = time.time()
warnings.filterwarnings("ignore")
#gene='5S_rRNA_ENST00000364415.1'
gene = 'RTN3_ENST00000537981.5_1'
result = RNA_groupsurvival(mydata_FPKM6).distrution_pipelinemap(gene, 'death_observed', 'survivalMonth', 'myscore', 'mylevel')
print(result)
end = time.time()
print(end - start)
result.to_csv('/share_storage/home/nacoo/enst/r3.xls', sep='\t')
'''