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permutations.py
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permutations.py
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import pandas as pd
import numpy as np
import sys
import os
import warnings
from pandas.core.common import SettingWithCopyWarning
warnings.simplefilter(action="ignore", category=SettingWithCopyWarning)
warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.simplefilter(action='ignore', category=RuntimeWarning)
class block_permutation():
def __init__(self, anc_data, chr_pos, standard_beta_threshed, standard_se_threshed, sib_beta_threshed,
sib_se_threshed, ascp, thresh, outlabel, eigenvecs, eigenvalues, emp_direct_vc, emp_sad_vc,
emp_covar_vc, emp_standard_decomp, emp_sib_decomp, emp_diff_decomp, variance_direct_vc,
variance_sad_vc, variance_covar_vc, outdir, pos_label, pcs_to_test=15, nperm = 1000):
self.chr_pos = chr_pos
self.standard_beta_threshed = standard_beta_threshed.reset_index(drop=True)
self.standard_se_threshed = standard_se_threshed.reset_index(drop=True)
self.sib_beta_threshed = sib_beta_threshed.reset_index(drop=True)
self.sib_se_threshed = sib_se_threshed.reset_index(drop=True)
self.nperm = nperm
self.thresh = thresh
self.pcs_to_test = pcs_to_test
self.outdir = outdir
self.outlabel = outlabel
self.pos_label = pos_label
self.emp_direct_vc, self.emp_sad_vc, self.emp_covar_vc = emp_direct_vc, emp_sad_vc, emp_covar_vc
self.emp_standard_decomp, self.emp_sib_decomp, self.emp_diff_decomp = emp_standard_decomp, emp_sib_decomp, emp_diff_decomp
self.variance_direct_vc, self.variance_sad_vc, self.variance_covar_vc = variance_direct_vc, variance_sad_vc, variance_covar_vc
self.block_bound_process()
self.block_dots(self.standard_beta_threshed, self.standard_se_threshed, self.sib_beta_threshed, self.sib_se_threshed, thresh, eigenvecs, self.chr_pos)
self.permute_blocks(eigenvalues)
self.calculate_pvalues()
self.proportion_table()
def block_bound_process(self):
df = pd.read_csv(anc_data, delim_whitespace = True)
df['chr'] = df['chr'].str.replace('chr','')
blocks = []
for index, row in df.iterrows():
snps = self.chr_pos[(self.chr_pos['CHR'].astype(float) == float(row['chr'])) & (float(row['start']) <= self.chr_pos[self.pos_label].astype(float)) & (float(row['stop']) > self.chr_pos[self.pos_label].astype(float))].index.tolist()
self.chr_pos.loc[snps,'block'] = int(index)
def element_multiplier(self,x,y):
return np.multiply(x,y)
def summations(self,eff_sizes,eigenvecs,nsnps):
temp = np.matmul(eff_sizes.T,eigenvecs)
return temp
## First, save the relevant dot products on a per-block basis.
## Each of these will be a matrix with #rows equal to the number of blocks and #columns
## equal to the number of PCs. Once we have matrices like this for standard, sib, difference, and corresponding
## errors, we can get permutation distributions by summing columns but flipping signs randomly
## and bootstrap by summing columns that have rows randomly chosen w/ replacement from the originals.
## Take in the same thing as the est.components, but return a list of three matrices corresponding
## to relevant dot products per block.
def block_dots(self, standard_beta, standard_se, sib_beta, sib_se, thresh, eigenvecs, pickrell_blocks):
blocks = self.chr_pos['block'].unique()
standard_dots = pd.DataFrame(np.zeros((len(blocks),eigenvecs.shape[1])), index = blocks)
sib_dots = pd.DataFrame(np.zeros((len(blocks),eigenvecs.shape[1])), index = blocks)
diff_dots = pd.DataFrame(np.zeros((len(blocks),eigenvecs.shape[1])), index = blocks)
sum_dots = pd.DataFrame(np.zeros((len(blocks),eigenvecs.shape[1])), index = blocks)
gw_err_dots = pd.DataFrame(np.zeros((len(blocks),eigenvecs.shape[1])), index = blocks)
sib_err_dots = pd.DataFrame(np.zeros((len(blocks),eigenvecs.shape[1])), index = blocks)
for block in blocks:
block_indices = self.chr_pos[self.chr_pos['block'] == block].index.tolist()
standard_dots.loc[block] = self.summations(standard_beta.loc[block_indices],eigenvecs[block_indices,:], len(block_indices))
sib_dots.loc[block] = self.summations(sib_beta.loc[block_indices],eigenvecs[block_indices,:], len(block_indices))
diff_dots.loc[block] = self.summations(standard_beta.loc[block_indices]-sib_beta.loc[block_indices],eigenvecs[block_indices,:], len(block_indices))
sum_dots.loc[block] = self.summations(standard_beta.loc[block_indices]+sib_beta.loc[block_indices],eigenvecs[block_indices,:], len(block_indices))
#I think something is wrong in this decomposition
gw_err_dots.loc[block] = self.summations(standard_se.loc[block_indices]**2,eigenvecs[block_indices,:]**2, len(block_indices))
sib_err_dots.loc[block] = self.summations(sib_se.loc[block_indices]**2,eigenvecs[block_indices,:]**2, len(block_indices))
self.standard_dots, self.sib_dots, self.diff_dots, self.sum_dots, self.gw_err_dots, self.sib_err_dots = standard_dots, sib_dots, diff_dots, sum_dots, gw_err_dots, sib_err_dots
def var_comps_from_block_matrices_perm(self, standard_dots, sib_dots, diff_dots, gw_err_dots, sib_err_dots, eigenvalues):
standard_decomp = eigenvalues * standard_dots.sum(axis=0)**2
sib_decomp = eigenvalues * sib_dots.sum(axis=0)**2
diff_decomp = eigenvalues * diff_dots.sum(axis=0)**2
standard_projection = eigenvalues * standard_dots.sum(axis=0)
sib_projection = eigenvalues * sib_dots.sum(axis=0)
diff_projection = eigenvalues * diff_dots.sum(axis=0)
standard_se_decomp = eigenvalues * gw_err_dots.sum(axis=0)
sib_se_decomp = eigenvalues * sib_err_dots.sum(axis=0)
direct_vc = sib_decomp - sib_se_decomp
sad_vc = diff_decomp - sib_se_decomp - standard_se_decomp
covar_vc = standard_decomp - diff_decomp - sib_decomp + 2*sib_se_decomp
return [direct_vc, sad_vc, covar_vc, standard_decomp, sib_decomp, diff_decomp, standard_se_decomp, sib_se_decomp, standard_projection, sib_projection, diff_projection]
def permute_blocks(self, eigenvalues):
direct_vc_perm = pd.DataFrame(np.zeros((self.nperm,len(eigenvalues))))
sad_vc_perm = pd.DataFrame(np.zeros((self.nperm,len(eigenvalues))))
covar_vc_perm = pd.DataFrame(np.zeros((self.nperm,len(eigenvalues))))
standard_proj_perm = pd.DataFrame(np.zeros((self.nperm,len(eigenvalues))))
sib_proj_perm = pd.DataFrame(np.zeros((self.nperm,len(eigenvalues))))
diff_proj_perm = pd.DataFrame(np.zeros((self.nperm,len(eigenvalues))))
standard_decomp_perm = pd.DataFrame(np.zeros((self.nperm,len(eigenvalues))))
sib_decomp_perm = pd.DataFrame(np.zeros((self.nperm,len(eigenvalues))))
diff_decomp_perm = pd.DataFrame(np.zeros((self.nperm,len(eigenvalues))))
for perm in range(self.nperm):
random_signs = 2 * np.random.binomial(1,0.5,size = len(self.chr_pos['block'].unique().tolist())) - 1
mat_standard_perm = np.apply_along_axis(self.element_multiplier, 0 ,self.standard_dots, random_signs)
mat_sib_perm = np.apply_along_axis(self.element_multiplier, 0 ,self.sib_dots, random_signs)
mat_diff_perm = np.apply_along_axis(self.element_multiplier, 0 ,self.diff_dots, random_signs)
vcs_perm = self.var_comps_from_block_matrices_perm(mat_standard_perm, mat_sib_perm, mat_diff_perm, self.gw_err_dots, self.sib_err_dots, eigenvalues)
direct_vc_perm.loc[perm] = vcs_perm[0]
sad_vc_perm.loc[perm] = vcs_perm[1]
covar_vc_perm.loc[perm] = vcs_perm[2]
standard_decomp_perm.loc[perm] = vcs_perm[3]
sib_decomp_perm.loc[perm] = vcs_perm[4]
diff_decomp_perm.loc[perm] = vcs_perm[5]
standard_proj_perm.loc[perm] = vcs_perm[8]
sib_proj_perm.loc[perm] = vcs_perm[9]
diff_proj_perm.loc[perm] = vcs_perm[10]
self.direct_vc_perm = direct_vc_perm
self.sad_vc_perm = sad_vc_perm
self.covar_vc_perm = covar_vc_perm
self.standard_proj_perm = standard_proj_perm
self.sib_proj_perm = sib_proj_perm
self.diff_proj_perm = diff_proj_perm
self.standard_decomp_perm = standard_decomp_perm
self.sib_decomp_perm = sib_decomp_perm
self.diff_decomp_perm = diff_decomp_perm
def calculate_pvalues(self):
pvals_direct = np.ones(self.pcs_to_test)
upper95_perm_direct = np.ones(self.pcs_to_test)
lower0_perm_direct = np.ones(self.pcs_to_test)
pvals_sad = np.ones(self.pcs_to_test)
upper95_perm_sad = np.ones(self.pcs_to_test)
lower0_perm_sad = np.ones(self.pcs_to_test)
pvals_covar = np.ones(self.pcs_to_test)
upper975_perm_covar = np.ones(self.pcs_to_test)
lower025_perm_covar = np.ones(self.pcs_to_test)
for k in range(self.pcs_to_test):
ranked_direct = (self.direct_vc_perm[k]/np.sum(self.standard_decomp_perm, axis = 1)).sort_values().reset_index(drop=True)
upper95_perm_direct[k] = ranked_direct.loc[949]
lower0_perm_direct[k] = ranked_direct.loc[0]
ranked_sad = (self.sad_vc_perm[k]/np.sum(self.standard_decomp_perm, axis = 1)).sort_values().reset_index(drop=True)
upper95_perm_sad[k] = ranked_sad.loc[949]
lower0_perm_sad[k] = ranked_sad.loc[0]
ranked_covar = (self.covar_vc_perm[k]/np.sum(self.standard_decomp_perm, axis = 1)).sort_values().reset_index(drop=True)
upper975_perm_covar[k] = ranked_covar.loc[975]
lower025_perm_covar[k] = ranked_covar.loc[25]
pvals_direct[k] = np.mean(np.where((self.emp_direct_vc[k]/np.sum(self.emp_standard_decomp)) <= np.array(ranked_direct), 1, 0))
pvals_sad[k] = np.mean(np.where((self.emp_sad_vc[k]/np.sum(self.emp_standard_decomp)) <= np.array(ranked_sad), 1, 0))
pvals_covar[k] = np.min([float(np.mean(np.where((self.emp_covar_vc[k]/np.sum(self.emp_standard_decomp)) <= np.array(ranked_covar), 1, 0))),float(np.mean(np.where((self.emp_covar_vc[k]/np.sum(self.emp_standard_decomp)) >= np.array(ranked_covar), 1, 0)))])
self.pvals_direct, self.pvals_sad, self.pvals_covar, self.upper95_perm_direct, self.upper95_perm_sad, self.upper975_perm_covar, self.lower0_perm_direct, self.lower0_perm_sad, self.lower025_perm_covar = pvals_direct, pvals_sad, pvals_covar, upper95_perm_direct, upper95_perm_sad, upper975_perm_covar, lower0_perm_direct, lower0_perm_sad, lower025_perm_covar
def proportion_table(self):
direct_prop_by_pcs = self.emp_direct_vc[:self.pcs_to_test]/np.sum(self.emp_standard_decomp)
direct_prop_se = np.sqrt(self.variance_direct_vc[:self.pcs_to_test]/np.sum(self.emp_standard_decomp))
direct_pvals = self.pvals_direct[:self.pcs_to_test]
sad_prop_by_pcs = self.emp_sad_vc[:self.pcs_to_test]/np.sum(self.emp_standard_decomp)
sad_prop_se = np.sqrt(self.variance_sad_vc[:self.pcs_to_test]/np.sum(self.emp_standard_decomp))
sad_pvals = self.pvals_sad[:self.pcs_to_test]
covar_prop_by_pcs = self.emp_covar_vc[:self.pcs_to_test]/np.sum(self.emp_standard_decomp)
covar_prop_se = np.sqrt(self.variance_covar_vc[:self.pcs_to_test]/np.sum(self.emp_standard_decomp))
covar_pvals = self.pvals_covar[:self.pcs_to_test]
outarray = np.vstack((direct_prop_by_pcs, direct_prop_se, direct_pvals, self.upper95_perm_direct,self.lower0_perm_direct, sad_prop_by_pcs, sad_prop_se, sad_pvals, self.upper95_perm_sad,self.lower0_perm_sad, covar_prop_by_pcs, covar_prop_se, covar_pvals, self.upper975_perm_covar,self.lower025_perm_covar))
outarray = np.round(outarray,4)
indices = ['direct_vc_estimate', 'direct_vc_estimate_se', 'direct_vc_pvals','upper95_perm_direct','lower0_perm_direct','sad_vc_estimate', 'sad_vc_estimate_se', 'sad_vc_pvals','upper95_perm_sad','lower0_perm_sad','covar_vc_estimate', 'covar_vc_estimate_se', 'covar_vc_pvals','upper975_perm_covar','lower025_perm_covar']
tests_out = pd.DataFrame(outarray, index = indices, columns = ['PC' + str(i+1) for i in range(outarray.shape[1])])
if self.outlabel == '':
tests_out.to_csv(self.outdir + '/block.permutation.stats.pval.' + str(self.thresh) + '.txt', sep = '\t')
else:
tests_out.to_csv(self.outdir + '/' + self.outlabel + '.block.permutation.stats.pval.' + str(self.thresh) + '.txt', sep = '\t')