import numpy as np
import pandas as pd
from gmat.gmatrix import agmat
from gmat.longwas.unbalance import unbalance_varcom
import logging
logging.basicConfig(level=logging.INFO)
bed_file = '../data/mouse_long/plink'
kin_lst = agmat(bed_file, inv=True, small_val=0.001, out_fmt='id_id_val')
data_file = '../data/mouse_long/phe.unbalance.txt'
id = 'ID'
tpoint = 'weak'
trait = 'trait'
kin_inv_file = '../data/mouse_long/plink.agiv2'
tfix = 'Sex'
prefix_outfile = '../data/mouse_long/unbalance_varcom'
res_var = unbalance_varcom(data_file,
id,
tpoint,
trait,
kin_inv_file,
tfix=None,
prefix_outfile=prefix_outfile)
print(res_var)
kin_file = '../data/mouse_long/plink.agrm2'
var_com = pd.read_csv("../data/mouse_long/unbalance_varcom.var",
sep='\s+',
header=0)
from glta.uvlmm import wemai_multi_gmat
from gmat.gmatrix import agmat, dgmat_as
from gmat.remma.remma_cpu import remma_add_cpu
logging.basicConfig(level=logging.INFO)
# prepare the phenotypic vector, design matrixed for fixed effects and random effects
from gmat.uvlmm import design_matrix_wemai_multi_gmat
pheno_file = '../data/mouse/pheno'
bed_file = '../data/mouse/plink'
y, xmat, zmat = design_matrix_wemai_multi_gmat(pheno_file, bed_file)
# Calculate the genomic relationship matrix
from gmat.gmatrix.gmatrix import agmat, dgmat_as
a = agmat(bed_file, inv=False)
b = dgmat_as(bed_file, inv=False)
# Example 1: Just include additive relationship matrix
from gmat.uvlmm.uvlmm_varcom import wemai_multi_gmat
gmat_lst = [a[0]]
var_com_a = wemai_multi_gmat(y, xmat, zmat, gmat_lst)
res_a = remma_add_cpu(y,
xmat,
zmat,
gmat_lst,
var_com_a,
bed_file,
out_file='../data/mouse/remma_add_cpu_a')
# Example 2: Include additive relationship matrix and dominance relationship matrix
"""
import numpy as np
import pandas as pd
import logging
import os
from gmat.gmatrix import agmat, dgmat_as
logging.basicConfig(level=logging.INFO)
bed_file = '../data/mouse/plink'
# matrix form
agmat0 = agmat(bed_file, inv=True, small_val=0.001, out_fmt='mat')
grm0 = np.load('../data/mouse/plink.agrm0.npz')
giv0 = np.load('../data/mouse/plink.agiv0.npz')
np.savetxt('../data/mouse/plink.agrm0', grm0['mat'])
np.savetxt('../data/mouse/plink.agiv0', giv0['mat'])
# row-column-value form, which can be used by asreml
agmat1 = agmat(bed_file, inv=True, small_val=0.001, out_fmt='row_col_val')
grm1 = np.load('../data/mouse/plink.agrm1.npz')
giv1 = np.load('../data/mouse/plink.agiv1.npz')
grm1_dct = {'row': grm1['row'] + 1, 'col': grm1['col'] + 1, 'val': grm1['val']}
grm1_df = pd.DataFrame(grm1_dct, columns=['row', 'col', 'val'])
grm1_df.to_csv('../data/mouse/plink.agrm1', header=False, index=False, sep=' ')
giv1_dct = {'row': giv1['row'] + 1, 'col': giv1['col'] + 1, 'val': giv1['val']}
giv1_df = pd.DataFrame(giv1_dct, columns=['row', 'col', 'val'])
giv1_df.to_csv('../data/mouse/plink.agiv1', header=False, index=False, sep=' ')
import logging
import numpy as np
from gmat.uvlmm import design_matrix_wemai_multi_gmat
from gmat.uvlmm import wemai_multi_gmat
from gmat.gmatrix import agmat, dgmat_as
from gmat.remma.remma_cpu import remma_add_cpu
logging.basicConfig(level=logging.INFO)
# prepare the phenotypic vector, design matrixed for fixed effects and random effects
pheno_file = '../data/mouse/pheno'
bed_file = '../data/mouse/plink'
y, xmat, zmat = design_matrix_wemai_multi_gmat(pheno_file, bed_file)
# Calculate the genomic relationship matrix
a = agmat(bed_file, inv=False)
b = dgmat_as(bed_file, inv=False)
# Example 1: Just include additive relationship matrix
gmat_lst = [a[0]]
var_com_a = wemai_multi_gmat(y, xmat, zmat, gmat_lst)
res_a = remma_add_cpu(y,
xmat,
zmat,
gmat_lst,
var_com_a,
bed_file,
out_file='../data/mouse/remma_add_cpu_a')
# Example 2: Include additive relationship matrix and dominance relationship matrix
gmat_lst = [a[0], b[0]]