In this notebook, we will use GEMMA to calculate the PRS. For more
information, visit the official GEMMA repository:
The GEMMA LM model takes the GWAS file, genotype data, and covariates and generates a new set of BETAS, which are then used to calculate PRS.
You can download the latest release of GEMMA from the following link:
To download and install GEMMA, use the following commands:
wget https://github.com/genetics-statistics/GEMMA/releases/download/v0.98.5/gemma-0.98.5-linux-static-AMD64.gz
gunzip gemma-0.98.5-linux-static-AMD64.gz
mv gemma-0.98.5-linux-static-AMD64 gemma
chmod u+x gemma
./gemmaFor detailed usage instructions, refer to the official GEMMA
documentation:
Note: GEMMA needs to be installed or placed in the same directory as this notebook.
When the effect size relates to disease risk and is thus given as an odds ratio (OR) rather than BETA (for continuous traits), the PRS is computed as a product of ORs. To simplify this calculation, take the natural logarithm of the OR so that the PRS can be computed using summation instead.
import os
import pandas as pd
import numpy as np
import sys
#filedirec = sys.argv[1]
filedirec = "SampleData1"
#filedirec = "asthma"
#filedirec = "asthma_19"
#filedirec = "migraine_0"
def check_phenotype_is_binary_or_continous(filedirec):
# Read the processed quality controlled file for a phenotype
df = pd.read_csv(filedirec+os.sep+filedirec+'_QC.fam',sep="\s+",header=None)
column_values = df[5].unique()
if len(set(column_values)) == 2:
return "Binary"
else:
return "Continous"
# Read the GWAS file.
GWAS = filedirec + os.sep + filedirec+".gz"
df = pd.read_csv(GWAS,compression= "gzip",sep="\s+")
if "BETA" in df.columns.to_list():
# For Continous Phenotype.
df = df[['CHR', 'BP', 'SNP', 'A1', 'A2', 'N', 'SE', 'P', 'BETA', 'INFO', 'MAF']]
else:
df["BETA"] = np.log(df["OR"])
df = df[['CHR', 'BP', 'SNP', 'A1', 'A2', 'N', 'SE', 'P', 'BETA', 'INFO', 'MAF']]
df['Z'] = df['BETA'] / df['SE']
transformed_df = df[['SNP', 'N', 'Z', 'A1', 'A2']].copy()
transformed_df.columns = ['SNP', 'N', 'Z', 'INC_ALLELE', 'DEC_ALLELE']
transformed_df.to_csv(filedirec + os.sep +"gemma.txt",sep="\t",index=False)
print(transformed_df.head().to_markdown())
print("Length of DataFrame!",len(transformed_df))
| | SNP | N | Z | INC_ALLELE | DEC_ALLELE | |---:|:-----------|-------:|----------:|:-------------|:-------------| | 0 | rs3131962 | 388028 | -0.701213 | A | G | | 1 | rs12562034 | 388028 | 0.20854 | A | G | | 2 | rs4040617 | 388028 | -0.790957 | G | A | | 3 | rs79373928 | 388028 | 0.241718 | G | T | | 4 | rs11240779 | 388028 | 0.53845 | G | A | Length of DataFrame! 499617
Define hyperparameters to be optimized and set initial values.
For Windows, download gwak, and for Linux, the awk command is
sufficient. For Windows, GWAK is required. You can download it from
here. Get it and place
it in the same directory.
At this stage, we have the genotype training data
newtrainfilename = "train_data.QC" and genotype test data
newtestfilename = "test_data.QC".
We modified the following variables:
filedirec = "SampleData1"orfiledirec = sys.argv[1]foldnumber = "0"orfoldnumber = sys.argv[2]for HPC.
Only these two variables can be modified to execute the code for specific data and specific folds. Though the code can be executed separately for each fold on HPC and separately for each dataset, it is recommended to execute it for multiple diseases and one fold at a time. Here’s the corrected text in Markdown format:
PRS calculation relies on P-values. SNPs with low P-values, indicating a high degree of association with a specific trait, are considered for calculation.
You can modify the code below to consider a specific set of P-values and save the file in the same format.
We considered the following parameters:
- Minimum P-value:
1e-10 - Maximum P-value:
1.0 - Minimum exponent:
10(Minimum P-value in exponent) - Number of intervals:
100(Number of intervals to be considered)
The code generates an array of logarithmically spaced P-values:
import numpy as np
import os
minimumpvalue = 10 # Minimum exponent for P-values
numberofintervals = 100 # Number of intervals to be considered
allpvalues = np.logspace(-minimumpvalue, 0, numberofintervals, endpoint=True) # Generating an array of logarithmically spaced P-values
print("Minimum P-value:", allpvalues[0])
print("Maximum P-value:", allpvalues[-1])
count = 1
with open(os.path.join(folddirec, 'range_list'), 'w') as file:
for value in allpvalues:
file.write(f'pv_{value} 0 {value}\n') # Writing range information to the 'range_list' file
count += 1
pvaluefile = os.path.join(folddirec, 'range_list')In this code: - minimumpvalue defines the minimum exponent for
P-values. - numberofintervals specifies how many intervals to
consider. - allpvalues generates an array of P-values spaced
logarithmically. - The script writes these P-values to a file named
range_list in the specified directory.
from operator import index
import pandas as pd
import numpy as np
import os
import subprocess
import sys
import pandas as pd
import statsmodels.api as sm
import pandas as pd
from sklearn.metrics import roc_auc_score, confusion_matrix
from statsmodels.stats.contingency_tables import mcnemar
def create_directory(directory):
"""Function to create a directory if it doesn't exist."""
if not os.path.exists(directory): # Checking if the directory doesn't exist
os.makedirs(directory) # Creating the directory if it doesn't exist
return directory # Returning the created or existing directory
#foldnumber = sys.argv[1]
foldnumber = "0" # Setting 'foldnumber' to "0"
folddirec = filedirec + os.sep + "Fold_" + foldnumber # Creating a directory path for the specific fold
trainfilename = "train_data" # Setting the name of the training data file
newtrainfilename = "train_data.QC" # Setting the name of the new training data file
testfilename = "test_data" # Setting the name of the test data file
newtestfilename = "test_data.QC" # Setting the name of the new test data file
# Number of PCA to be included as a covariate.
numberofpca = ["6"] # Setting the number of PCA components to be included
# Clumping parameters.
clump_p1 = [1] # List containing clump parameter 'p1'
clump_r2 = [0.1] # List containing clump parameter 'r2'
clump_kb = [200] # List containing clump parameter 'kb'
# Pruning parameters.
p_window_size = [200] # List containing pruning parameter 'window_size'
p_slide_size = [50] # List containing pruning parameter 'slide_size'
p_LD_threshold = [0.25] # List containing pruning parameter 'LD_threshold'
# Kindly note that the number of p-values to be considered varies, and the actual p-value depends on the dataset as well.
# We will specify the range list here.
minimumpvalue = 10 # Minimum p-value in exponent
numberofintervals = 20 # Number of intervals to be considered
allpvalues = np.logspace(-minimumpvalue, 0, numberofintervals, endpoint=True) # Generating an array of logarithmically spaced p-values
count = 1
with open(folddirec + os.sep + 'range_list', 'w') as file:
for value in allpvalues:
file.write(f'pv_{value} 0 {value}\n') # Writing range information to the 'range_list' file
count = count + 1
pvaluefile = folddirec + os.sep + 'range_list'
# Initializing an empty DataFrame with specified column names
prs_result = pd.DataFrame(columns=["clump_p1", "clump_r2", "clump_kb", "p_window_size", "p_slide_size", "p_LD_threshold",
"pvalue", "numberofpca","numberofvariants","Train_pure_prs", "Train_null_model", "Train_best_model",
"Test_pure_prs", "Test_null_model", "Test_best_model"])
- Perform Clumping and Pruning
- Calculate PCA Using Plink
- Fit Binary Phenotype and Save Results
- Fit Continuous Phenotype and Save Results
import os
import subprocess
import pandas as pd
import statsmodels.api as sm
from sklearn.metrics import explained_variance_score
def perform_clumping_and_pruning_on_individual_data(traindirec, newtrainfilename,numberofpca, p1_val, p2_val, p3_val, c1_val, c2_val, c3_val,Name,pvaluefile):
command = [
"./plink",
"--bfile", traindirec+os.sep+newtrainfilename,
"--indep-pairwise", p1_val, p2_val, p3_val,
"--out", traindirec+os.sep+trainfilename
]
subprocess.run(command)
# First perform pruning and then clumping and the pruning.
command = [
"./plink",
"--bfile", traindirec+os.sep+newtrainfilename,
"--clump-p1", c1_val,
"--extract", traindirec+os.sep+trainfilename+".prune.in",
"--clump-r2", c2_val,
"--clump-kb", c3_val,
"--clump", filedirec+os.sep+filedirec+".txt",
"--clump-snp-field", "SNP",
"--clump-field", "P",
"--out", traindirec+os.sep+trainfilename
]
subprocess.run(command)
# Extract the valid SNPs from th clumped file.
# For windows download gwak for linux awk commmand is sufficient.
### For windows require GWAK.
### https://sourceforge.net/projects/gnuwin32/
##3 Get it and place it in the same direc.
#os.system("gawk "+"\""+"NR!=1{print $3}"+"\" "+ traindirec+os.sep+trainfilename+".clumped > "+traindirec+os.sep+trainfilename+".valid.snp")
#print("gawk "+"\""+"NR!=1{print $3}"+"\" "+ traindirec+os.sep+trainfilename+".clumped > "+traindirec+os.sep+trainfilename+".valid.snp")
#Linux:
command = f"awk 'NR!=1{{print $3}}' {traindirec}{os.sep}{trainfilename}.clumped > {traindirec}{os.sep}{trainfilename}.valid.snp"
os.system(command)
command = [
"./plink",
"--make-bed",
"--bfile", traindirec+os.sep+newtrainfilename,
"--indep-pairwise", p1_val, p2_val, p3_val,
"--extract", traindirec+os.sep+trainfilename+".valid.snp",
"--out", traindirec+os.sep+newtrainfilename+".clumped.pruned"
]
subprocess.run(command)
command = [
"./plink",
"--make-bed",
"--bfile", traindirec+os.sep+testfilename,
"--indep-pairwise", p1_val, p2_val, p3_val,
"--extract", traindirec+os.sep+trainfilename+".valid.snp",
"--out", traindirec+os.sep+testfilename+".clumped.pruned"
]
subprocess.run(command)
def calculate_pca_for_traindata_testdata_for_clumped_pruned_snps(traindirec, newtrainfilename,p):
# Calculate the PRS for the test data using the same set of SNPs and also calculate the PCA.
# Also extract the PCA at this point.
# PCA are calculated afer clumping and pruining.
command = [
"./plink",
"--bfile", folddirec+os.sep+testfilename+".clumped.pruned",
# Select the final variants after clumping and pruning.
"--extract", traindirec+os.sep+trainfilename+".valid.snp",
"--pca", p,
"--out", folddirec+os.sep+testfilename
]
subprocess.run(command)
command = [
"./plink",
"--bfile", traindirec+os.sep+newtrainfilename+".clumped.pruned",
# Select the final variants after clumping and pruning.
"--extract", traindirec+os.sep+trainfilename+".valid.snp",
"--pca", p,
"--out", traindirec+os.sep+trainfilename
]
subprocess.run(command)
# This function fit the binary model on the PRS.
def fit_binary_phenotype_on_PRS(traindirec, newtrainfilename,p,gemmamodel, p1_val, p2_val, p3_val, c1_val, c2_val, c3_val,Name,pvaluefile):
threshold_values = allpvalues
# Merge the covariates, pca and phenotypes.
tempphenotype_train = pd.read_table(traindirec+os.sep+newtrainfilename+".clumped.pruned"+".fam", sep="\s+",header=None)
phenotype_train = pd.DataFrame()
phenotype_train["Phenotype"] = tempphenotype_train[5].values
pcs_train = pd.read_table(traindirec+os.sep+trainfilename+".eigenvec", sep="\s+",header=None, names=["FID", "IID"] + [f"PC{str(i)}" for i in range(1, int(p)+1)])
covariate_train = pd.read_table(traindirec+os.sep+trainfilename+".cov",sep="\s+")
covariate_train.fillna(0, inplace=True)
covariate_train = covariate_train[covariate_train["FID"].isin(pcs_train["FID"].values) & covariate_train["IID"].isin(pcs_train["IID"].values)]
covariate_train['FID'] = covariate_train['FID'].astype(str)
pcs_train['FID'] = pcs_train['FID'].astype(str)
covariate_train['IID'] = covariate_train['IID'].astype(str)
pcs_train['IID'] = pcs_train['IID'].astype(str)
covandpcs_train = pd.merge(covariate_train, pcs_train, on=["FID","IID"])
covandpcs_train.fillna(0, inplace=True)
## Scale the covariates!
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import explained_variance_score
scaler = MinMaxScaler()
normalized_values_train = scaler.fit_transform(covandpcs_train.iloc[:, 2:])
#covandpcs_train.iloc[:, 2:] = normalized_values_test
tempphenotype_test = pd.read_table(traindirec+os.sep+testfilename+".clumped.pruned"+".fam", sep="\s+",header=None)
phenotype_test= pd.DataFrame()
phenotype_test["Phenotype"] = tempphenotype_test[5].values
pcs_test = pd.read_table(traindirec+os.sep+testfilename+".eigenvec", sep="\s+",header=None, names=["FID", "IID"] + [f"PC{str(i)}" for i in range(1, int(p)+1)])
covariate_test = pd.read_table(traindirec+os.sep+testfilename+".cov",sep="\s+")
covariate_test.fillna(0, inplace=True)
covariate_test = covariate_test[covariate_test["FID"].isin(pcs_test["FID"].values) & covariate_test["IID"].isin(pcs_test["IID"].values)]
covariate_test['FID'] = covariate_test['FID'].astype(str)
pcs_test['FID'] = pcs_test['FID'].astype(str)
covariate_test['IID'] = covariate_test['IID'].astype(str)
pcs_test['IID'] = pcs_test['IID'].astype(str)
covandpcs_test = pd.merge(covariate_test, pcs_test, on=["FID","IID"])
covandpcs_test.fillna(0, inplace=True)
normalized_values_test = scaler.transform(covandpcs_test.iloc[:, 2:])
#covandpcs_test.iloc[:, 2:] = normalized_values_test
tempalphas = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]
l1weights = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]
tempalphas = [0.1]
l1weights = [0.1]
phenotype_train["Phenotype"] = phenotype_train["Phenotype"].replace({1: 0, 2: 1})
phenotype_test["Phenotype"] = phenotype_test["Phenotype"].replace({1: 0, 2: 1})
for tempalpha in tempalphas:
for l1weight in l1weights:
try:
null_model = sm.Logit(phenotype_train["Phenotype"], sm.add_constant(covandpcs_train.iloc[:, 2:])).fit_regularized(alpha=tempalpha, L1_wt=l1weight)
#null_model = sm.Logit(phenotype_train["Phenotype"], sm.add_constant(covandpcs_train.iloc[:, 2:])).fit()
except:
print("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX")
continue
train_null_predicted = null_model.predict(sm.add_constant(covandpcs_train.iloc[:, 2:]))
from sklearn.metrics import roc_auc_score, confusion_matrix
from sklearn.metrics import r2_score
test_null_predicted = null_model.predict(sm.add_constant(covandpcs_test.iloc[:, 2:]))
global prs_result
for i in threshold_values:
try:
prs_train = pd.read_table(traindirec+os.sep+Name+os.sep+"train_data.pv_"+f"{i}.profile", sep="\s+", usecols=["FID", "IID", "SCORE"])
except:
continue
prs_train['FID'] = prs_train['FID'].astype(str)
prs_train['IID'] = prs_train['IID'].astype(str)
try:
prs_test = pd.read_table(traindirec+os.sep+Name+os.sep+"test_data.pv_"+f"{i}.profile", sep="\s+", usecols=["FID", "IID", "SCORE"])
except:
continue
prs_test['FID'] = prs_test['FID'].astype(str)
prs_test['IID'] = prs_test['IID'].astype(str)
pheno_prs_train = pd.merge(covandpcs_train, prs_train, on=["FID", "IID"])
pheno_prs_test = pd.merge(covandpcs_test, prs_test, on=["FID", "IID"])
try:
model = sm.Logit(phenotype_train["Phenotype"], sm.add_constant(pheno_prs_train.iloc[:, 2:])).fit_regularized(alpha=tempalpha, L1_wt=l1weight)
#model = sm.Logit(phenotype_train["Phenotype"], sm.add_constant(pheno_prs_train.iloc[:, 2:])).fit()
except:
continue
train_best_predicted = model.predict(sm.add_constant(pheno_prs_train.iloc[:, 2:]))
test_best_predicted = model.predict(sm.add_constant(pheno_prs_test.iloc[:, 2:]))
from sklearn.metrics import roc_auc_score, confusion_matrix
prs_result = prs_result._append({
"clump_p1": c1_val,
"clump_r2": c2_val,
"clump_kb": c3_val,
"p_window_size": p1_val,
"p_slide_size": p2_val,
"p_LD_threshold": p3_val,
"pvalue": i,
"numberofpca":p,
"gemmamodel":gemmamodel,
"tempalpha":str(tempalpha),
"l1weight":str(l1weight),
"Train_pure_prs":roc_auc_score(phenotype_train["Phenotype"].values,prs_train['SCORE'].values),
"Train_null_model":roc_auc_score(phenotype_train["Phenotype"].values,train_null_predicted.values),
"Train_best_model":roc_auc_score(phenotype_train["Phenotype"].values,train_best_predicted.values),
"Test_pure_prs":roc_auc_score(phenotype_test["Phenotype"].values,prs_test['SCORE'].values),
"Test_null_model":roc_auc_score(phenotype_test["Phenotype"].values,test_null_predicted.values),
"Test_best_model":roc_auc_score(phenotype_test["Phenotype"].values,test_best_predicted.values),
}, ignore_index=True)
prs_result.to_csv(traindirec+os.sep+Name+os.sep+"Results.csv",index=False)
return
# This function fit the binary model on the PRS.
def fit_continous_phenotype_on_PRS(traindirec, newtrainfilename,p, gemmamodel,p1_val, p2_val, p3_val, c1_val, c2_val, c3_val,Name,pvaluefile):
threshold_values = allpvalues
# Merge the covariates, pca and phenotypes.
tempphenotype_train = pd.read_table(traindirec+os.sep+newtrainfilename+".clumped.pruned"+".fam", sep="\s+",header=None)
phenotype_train = pd.DataFrame()
phenotype_train["Phenotype"] = tempphenotype_train[5].values
pcs_train = pd.read_table(traindirec+os.sep+trainfilename+".eigenvec", sep="\s+",header=None, names=["FID", "IID"] + [f"PC{str(i)}" for i in range(1, int(p)+1)])
covariate_train = pd.read_table(traindirec+os.sep+trainfilename+".cov",sep="\s+")
covariate_train.fillna(0, inplace=True)
covariate_train = covariate_train[covariate_train["FID"].isin(pcs_train["FID"].values) & covariate_train["IID"].isin(pcs_train["IID"].values)]
covariate_train['FID'] = covariate_train['FID'].astype(str)
pcs_train['FID'] = pcs_train['FID'].astype(str)
covariate_train['IID'] = covariate_train['IID'].astype(str)
pcs_train['IID'] = pcs_train['IID'].astype(str)
covandpcs_train = pd.merge(covariate_train, pcs_train, on=["FID","IID"])
covandpcs_train.fillna(0, inplace=True)
## Scale the covariates!
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import explained_variance_score
scaler = MinMaxScaler()
normalized_values_train = scaler.fit_transform(covandpcs_train.iloc[:, 2:])
#covandpcs_train.iloc[:, 2:] = normalized_values_test
tempphenotype_test = pd.read_table(traindirec+os.sep+testfilename+".clumped.pruned"+".fam", sep="\s+",header=None)
phenotype_test= pd.DataFrame()
phenotype_test["Phenotype"] = tempphenotype_test[5].values
pcs_test = pd.read_table(traindirec+os.sep+testfilename+".eigenvec", sep="\s+",header=None, names=["FID", "IID"] + [f"PC{str(i)}" for i in range(1, int(p)+1)])
covariate_test = pd.read_table(traindirec+os.sep+testfilename+".cov",sep="\s+")
covariate_test.fillna(0, inplace=True)
covariate_test = covariate_test[covariate_test["FID"].isin(pcs_test["FID"].values) & covariate_test["IID"].isin(pcs_test["IID"].values)]
covariate_test['FID'] = covariate_test['FID'].astype(str)
pcs_test['FID'] = pcs_test['FID'].astype(str)
covariate_test['IID'] = covariate_test['IID'].astype(str)
pcs_test['IID'] = pcs_test['IID'].astype(str)
covandpcs_test = pd.merge(covariate_test, pcs_test, on=["FID","IID"])
covandpcs_test.fillna(0, inplace=True)
normalized_values_test = scaler.transform(covandpcs_test.iloc[:, 2:])
#covandpcs_test.iloc[:, 2:] = normalized_values_test
tempalphas = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]
l1weights = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]
tempalphas = [0.1]
l1weights = [0.1]
#phenotype_train["Phenotype"] = phenotype_train["Phenotype"].replace({1: 0, 2: 1})
#phenotype_test["Phenotype"] = phenotype_test["Phenotype"].replace({1: 0, 2: 1})
for tempalpha in tempalphas:
for l1weight in l1weights:
try:
#null_model = sm.OLS(phenotype_train["Phenotype"], sm.add_constant(covandpcs_train.iloc[:, 2:])).fit_regularized(alpha=tempalpha, L1_wt=l1weight)
null_model = sm.OLS(phenotype_train["Phenotype"], sm.add_constant(covandpcs_train.iloc[:, 2:])).fit()
#null_model = sm.OLS(phenotype_train["Phenotype"], sm.add_constant(covandpcs_train.iloc[:, 2:])).fit()
except:
print("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX")
continue
train_null_predicted = null_model.predict(sm.add_constant(covandpcs_train.iloc[:, 2:]))
from sklearn.metrics import roc_auc_score, confusion_matrix
from sklearn.metrics import r2_score
test_null_predicted = null_model.predict(sm.add_constant(covandpcs_test.iloc[:, 2:]))
global prs_result
for i in threshold_values:
try:
prs_train = pd.read_table(traindirec+os.sep+Name+os.sep+"train_data.pv_"+f"{i}.profile", sep="\s+", usecols=["FID", "IID", "SCORE"])
except:
continue
prs_train['FID'] = prs_train['FID'].astype(str)
prs_train['IID'] = prs_train['IID'].astype(str)
try:
prs_test = pd.read_table(traindirec+os.sep+Name+os.sep+"test_data.pv_"+f"{i}.profile", sep="\s+", usecols=["FID", "IID", "SCORE"])
except:
continue
prs_test['FID'] = prs_test['FID'].astype(str)
prs_test['IID'] = prs_test['IID'].astype(str)
pheno_prs_train = pd.merge(covandpcs_train, prs_train, on=["FID", "IID"])
pheno_prs_test = pd.merge(covandpcs_test, prs_test, on=["FID", "IID"])
try:
#model = sm.OLS(phenotype_train["Phenotype"], sm.add_constant(pheno_prs_train.iloc[:, 2:])).fit_regularized(alpha=tempalpha, L1_wt=l1weight)
model = sm.OLS(phenotype_train["Phenotype"], sm.add_constant(pheno_prs_train.iloc[:, 2:])).fit()
except:
continue
train_best_predicted = model.predict(sm.add_constant(pheno_prs_train.iloc[:, 2:]))
test_best_predicted = model.predict(sm.add_constant(pheno_prs_test.iloc[:, 2:]))
from sklearn.metrics import roc_auc_score, confusion_matrix
prs_result = prs_result._append({
"clump_p1": c1_val,
"clump_r2": c2_val,
"clump_kb": c3_val,
"p_window_size": p1_val,
"p_slide_size": p2_val,
"p_LD_threshold": p3_val,
"pvalue": i,
"numberofpca":p,
"gemmamodel":gemmamodel,
"tempalpha":str(tempalpha),
"l1weight":str(l1weight),
"Train_pure_prs":explained_variance_score(phenotype_train["Phenotype"],prs_train['SCORE'].values),
"Train_null_model":explained_variance_score(phenotype_train["Phenotype"],train_null_predicted),
"Train_best_model":explained_variance_score(phenotype_train["Phenotype"],train_best_predicted),
"Test_pure_prs":explained_variance_score(phenotype_test["Phenotype"],prs_test['SCORE'].values),
"Test_null_model":explained_variance_score(phenotype_test["Phenotype"],test_null_predicted),
"Test_best_model":explained_variance_score(phenotype_test["Phenotype"],test_best_predicted),
}, ignore_index=True)
prs_result.to_csv(traindirec+os.sep+Name+os.sep+"Results.csv",index=False)
return
# Define a global variable to store results
prs_result = pd.DataFrame()
def transform_gemma_lm_data(traindirec, newtrainfilename,p,gemmamodel, p1_val, p2_val, p3_val, c1_val, c2_val, c3_val,Name,pvaluefile):
### First perform clumping on the file and save the clumpled file.
perform_clumping_and_pruning_on_individual_data(traindirec, newtrainfilename,p, p1_val, p2_val, p3_val, c1_val, c2_val, c3_val,Name,pvaluefile)
#newtrainfilename = newtrainfilename+".clumped.pruned"
#testfilename = testfilename+".clumped.pruned"
#clupmedfile = traindirec+os.sep+newtrainfilename+".clump"
#prunedfile = traindirec+os.sep+newtrainfilename+".clumped.pruned"
# Also extract the PCA at this point for both test and training data.
calculate_pca_for_traindata_testdata_for_clumped_pruned_snps(traindirec, newtrainfilename,p)
#Extract p-values from the GWAS file.
# Command for Linux.
os.system("awk "+"\'"+"{print $3,$8}"+"\'"+" ./"+filedirec+os.sep+filedirec+".txt > ./"+traindirec+os.sep+"SNP.pvalue")
# Command for windows.
### For windows get GWAK.
### https://sourceforge.net/projects/gnuwin32/
##3 Get it and place it in the same direc.
#os.system("gawk "+"\""+"{print $3,$8}"+"\""+" ./"+filedirec+os.sep+filedirec+".txt > ./"+traindirec+os.sep+"SNP.pvalue")
#print("gawk "+"\""+"{print $3,$8}"+"\""+" ./"+filedirec+os.sep+filedirec+".txt > ./"+traindirec+os.sep+"SNP.pvalue")
#exit(0)
# Merge the covariates, pca and phenotypes.
tempphenotype_train = pd.read_table(traindirec+os.sep+newtrainfilename+".clumped.pruned"+".fam", sep="\s+",header=None)
phenotype_train = pd.DataFrame()
phenotype_train["Phenotype"] = tempphenotype_train[5].values
pcs_train = pd.read_table(traindirec+os.sep+trainfilename+".eigenvec", sep="\s+",header=None, names=["FID", "IID"] + [f"PC{str(i)}" for i in range(1, int(p)+1)])
covariate_train = pd.read_table(traindirec+os.sep+trainfilename+".cov",sep="\s+")
covariate_train.fillna(0, inplace=True)
covariate_train = covariate_train[covariate_train["FID"].isin(pcs_train["FID"].values) & covariate_train["IID"].isin(pcs_train["IID"].values)]
covariate_train['FID'] = covariate_train['FID'].astype(str)
pcs_train['FID'] = pcs_train['FID'].astype(str)
covariate_train['IID'] = covariate_train['IID'].astype(str)
pcs_train['IID'] = pcs_train['IID'].astype(str)
covandpcs_train = pd.merge(covariate_train, pcs_train, on=["FID","IID"])
covandpcs_train.fillna(0, inplace=True)
covandpcs_train.to_csv(traindirec+os.sep+trainfilename+".COV_PCA",sep="\t",index=False)
covandpcs_train.iloc[:, 2:].to_csv(traindirec+os.sep+trainfilename+".COV_PCAgemma", header=False, index=False,sep="\t")
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import explained_variance_score
scaler = MinMaxScaler()
normalized_values_train = scaler.fit_transform(covandpcs_train.iloc[:, 2:])
tempphenotype_test = pd.read_table(traindirec+os.sep+testfilename+".clumped.pruned"+".fam", sep="\s+",header=None)
phenotype_test= pd.DataFrame()
phenotype_test["Phenotype"] = tempphenotype_test[5].values
pcs_test = pd.read_table(traindirec+os.sep+testfilename+".eigenvec", sep="\s+",header=None, names=["FID", "IID"] + [f"PC{str(i)}" for i in range(1, int(p)+1)])
covariate_test = pd.read_table(traindirec+os.sep+testfilename+".cov",sep="\s+")
covariate_test.fillna(0, inplace=True)
covariate_test = covariate_test[covariate_test["FID"].isin(pcs_test["FID"].values) & covariate_test["IID"].isin(pcs_test["IID"].values)]
covariate_test['FID'] = covariate_test['FID'].astype(str)
pcs_test['FID'] = pcs_test['FID'].astype(str)
covariate_test['IID'] = covariate_test['IID'].astype(str)
pcs_test['IID'] = pcs_test['IID'].astype(str)
covandpcs_test = pd.merge(covariate_test, pcs_test, on=["FID","IID"])
covandpcs_test.fillna(0, inplace=True)
normalized_values_test = scaler.transform(covandpcs_test.iloc[:, 2:])
covandpcs_test.to_csv(traindirec+os.sep+testfilename+".COV_PCA",sep="\t",index=False)
covandpcs_test.iloc[:, 2:].to_csv(traindirec+os.sep+testfilename+".COV_PCAgemma", header=False, index=False,sep="\t")
# Create a directory output to store the results
create_directory("output")
# Results will be saved in output/SampleData1/Fold_0/gemma-lm and will delete that directory for each iteration.
try:
os.makedirs(os.path.join("output", traindirec, "gemma-"+gemmamodel), exist_ok=True)
except OSError as e:
print(f"Error creating directory: {e}")
if gemmamodel=='lm':
command = [
"./gemma",
"--bfile", traindirec+os.sep+newtrainfilename+".clumped.pruned",
"-beta",filedirec + os.sep +"gemma.txt",
'-c', traindirec+os.sep+trainfilename+".COV_PCAgemma",
"-lm",str(1),
"-o", traindirec+os.sep+"gemma-"+gemmamodel+os.sep+"gemma",
]
print(" ".join(command))
subprocess.run(command)
try:
temp = pd.read_csv("output"+os.sep+traindirec+os.sep+"gemma-"+gemmamodel+os.sep+"gemma.assoc.txt",sep="\s+")
except:
print("GWAS not generated!")
return
if check_phenotype_is_binary_or_continous(filedirec)=="Binary":
temp['beta'] = np.log(temp['beta'])
else:
pass
temp.replace([np.nan, -np.inf, np.inf], 0, inplace=True)
temp.iloc[:,[1,5,8]].to_csv("output"+os.sep+traindirec+os.sep+"gemma-"+gemmamodel+os.sep+"gemma.assoc.txt",sep="\t",index=False)
command = [
"./plink",
"--bfile", traindirec+os.sep+newtrainfilename+".clumped.pruned",
### SNP column = 3, Effect allele column 1 = 4, OR column=9
"--score", "output"+os.sep+ traindirec+os.sep+"gemma-"+gemmamodel+os.sep+"gemma.assoc.txt", "1", "2", "3", "header",
"--q-score-range", traindirec+os.sep+"range_list",traindirec+os.sep+"SNP.pvalue",
"--extract", traindirec+os.sep+trainfilename+".valid.snp",
"--out", traindirec+os.sep+Name+os.sep+trainfilename
]
#exit(0)
subprocess.run(command)
command = [
"./plink",
"--bfile", folddirec+os.sep+testfilename+".clumped.pruned",
### SNP column = 3, Effect allele column 1 = 4, Beta column=12
"--score", "output"+os.sep+ traindirec+os.sep+"gemma-"+gemmamodel+os.sep+"gemma.assoc.txt", "1", "2", "3", "header",
"--q-score-range", traindirec+os.sep+"range_list",traindirec+os.sep+"SNP.pvalue",
"--extract", traindirec+os.sep+trainfilename+".valid.snp",
"--out", folddirec+os.sep+Name+os.sep+testfilename
]
subprocess.run(command)
if check_phenotype_is_binary_or_continous(filedirec)=="Binary":
print("Binary Phenotype!")
fit_binary_phenotype_on_PRS(traindirec, newtrainfilename,p,gemmamodel, p1_val, p2_val, p3_val, c1_val, c2_val, c3_val,Name,pvaluefile)
else:
print("Continous Phenotype!")
fit_continous_phenotype_on_PRS(traindirec, newtrainfilename,p,gemmamodel, p1_val, p2_val, p3_val, c1_val, c2_val, c3_val,Name,pvaluefile)
result_directory = "GEMMA-LM"
gemmamodels = ['lm' ]
# Nested loops to iterate over different parameter values
create_directory(folddirec+os.sep+result_directory)
for p1_val in p_window_size:
for p2_val in p_slide_size:
for p3_val in p_LD_threshold:
for c1_val in clump_p1:
for c2_val in clump_r2:
for c3_val in clump_kb:
for p in numberofpca:
for gemmamodel in gemmamodels:
transform_gemma_lm_data(folddirec, newtrainfilename, p,gemmamodel, str(p1_val), str(p2_val), str(p3_val), str(c1_val), str(c2_val), str(c3_val), result_directory, pvaluefile)
PLINK v1.90b7.2 64-bit (11 Dec 2023) www.cog-genomics.org/plink/1.9/ (C) 2005-2023 Shaun Purcell, Christopher Chang GNU General Public License v3 Logging to SampleData1/Fold_0/train_data.log. Options in effect: --bfile SampleData1/Fold_0/train_data.QC --indep-pairwise 200 50 0.25 --out SampleData1/Fold_0/train_data 63761 MB RAM detected; reserving 31880 MB for main workspace. 491952 variants loaded from .bim file. 380 people (183 males, 197 females) loaded from .fam. 380 phenotype values loaded from .fam. Using 1 thread (no multithreaded calculations invoked). Before main variant filters, 380 founders and 0 nonfounders present. Calculating allele frequencies... 10111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091929394959697989 done. Total genotyping rate is 0.999894. 491952 variants and 380 people pass filters and QC. Phenotype data is quantitative. Pruned 18860 variants from chromosome 1, leaving 20363. Pruned 19645 variants from chromosome 2, leaving 20067. Pruned 16414 variants from chromosome 3, leaving 17080. Pruned 15404 variants from chromosome 4, leaving 16035. Pruned 14196 variants from chromosome 5, leaving 15379. Pruned 19368 variants from chromosome 6, leaving 14770. Pruned 13110 variants from chromosome 7, leaving 13997. Pruned 12431 variants from chromosome 8, leaving 12966. Pruned 9982 variants from chromosome 9, leaving 11477. Pruned 11999 variants from chromosome 10, leaving 12850. Pruned 12156 variants from chromosome 11, leaving 12221. Pruned 10979 variants from chromosome 12, leaving 12050. Pruned 7923 variants from chromosome 13, leaving 9247. Pruned 7624 variants from chromosome 14, leaving 8448. Pruned 7387 variants from chromosome 15, leaving 8145. Pruned 8063 variants from chromosome 16, leaving 8955. Pruned 7483 variants from chromosome 17, leaving 8361. Pruned 6767 variants from chromosome 18, leaving 8240. Pruned 6438 variants from chromosome 19, leaving 6432. Pruned 5972 variants from chromosome 20, leaving 7202. Pruned 3426 variants from chromosome 21, leaving 4102. Pruned 3801 variants from chromosome 22, leaving 4137. Pruning complete. 239428 of 491952 variants removed. Marker lists written to SampleData1/Fold_0/train_data.prune.in and SampleData1/Fold_0/train_data.prune.out . PLINK v1.90b7.2 64-bit (11 Dec 2023) www.cog-genomics.org/plink/1.9/ (C) 2005-2023 Shaun Purcell, Christopher Chang GNU General Public License v3 Logging to SampleData1/Fold_0/train_data.log. Options in effect: --bfile SampleData1/Fold_0/train_data.QC --clump SampleData1/SampleData1.txt --clump-field P --clump-kb 200 --clump-p1 1 --clump-r2 0.1 --clump-snp-field SNP --extract SampleData1/Fold_0/train_data.prune.in --out SampleData1/Fold_0/train_data 63761 MB RAM detected; reserving 31880 MB for main workspace. 491952 variants loaded from .bim file. 380 people (183 males, 197 females) loaded from .fam. 380 phenotype values loaded from .fam. --extract: 252524 variants remaining. Using 1 thread (no multithreaded calculations invoked). Before main variant filters, 380 founders and 0 nonfounders present. Calculating allele frequencies... 10111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091929394959697989 done. Total genotyping rate is 0.999894. 252524 variants and 380 people pass filters and QC. Phenotype data is quantitative. --clump: 172878 clumps formed from 252524 top variants. Results written to SampleData1/Fold_0/train_data.clumped .
Warning: 'rs3134762' is missing from the main dataset, and is a top variant. Warning: 'rs3132505' is missing from the main dataset, and is a top variant. Warning: 'rs3130424' is missing from the main dataset, and is a top variant. 247090 more top variant IDs missing; see log file.
PLINK v1.90b7.2 64-bit (11 Dec 2023) www.cog-genomics.org/plink/1.9/ (C) 2005-2023 Shaun Purcell, Christopher Chang GNU General Public License v3 Logging to SampleData1/Fold_0/train_data.QC.clumped.pruned.log. Options in effect: --bfile SampleData1/Fold_0/train_data.QC --extract SampleData1/Fold_0/train_data.valid.snp --indep-pairwise 200 50 0.25 --make-bed --out SampleData1/Fold_0/train_data.QC.clumped.pruned 63761 MB RAM detected; reserving 31880 MB for main workspace. 491952 variants loaded from .bim file. 380 people (183 males, 197 females) loaded from .fam. 380 phenotype values loaded from .fam. --extract: 172878 variants remaining. Using 1 thread (no multithreaded calculations invoked). Before main variant filters, 380 founders and 0 nonfounders present. Calculating allele frequencies... 10111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091929394959697989 done. Total genotyping rate is 0.999891. 172878 variants and 380 people pass filters and QC. Phenotype data is quantitative. --make-bed to SampleData1/Fold_0/train_data.QC.clumped.pruned.bed + SampleData1/Fold_0/train_data.QC.clumped.pruned.bim + SampleData1/Fold_0/train_data.QC.clumped.pruned.fam ... 101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899done. Pruned 2 variants from chromosome 1, leaving 14011. Pruned 2 variants from chromosome 2, leaving 13811. Pruned 2 variants from chromosome 3, leaving 11783. Pruned 0 variants from chromosome 4, leaving 11041. Pruned 1 variant from chromosome 5, leaving 10631. Pruned 50 variants from chromosome 6, leaving 10018. Pruned 0 variants from chromosome 7, leaving 9496. Pruned 4 variants from chromosome 8, leaving 8863. Pruned 0 variants from chromosome 9, leaving 7768. Pruned 5 variants from chromosome 10, leaving 8819. Pruned 10 variants from chromosome 11, leaving 8410. Pruned 0 variants from chromosome 12, leaving 8198. Pruned 0 variants from chromosome 13, leaving 6350. Pruned 1 variant from chromosome 14, leaving 5741. Pruned 0 variants from chromosome 15, leaving 5569. Pruned 2 variants from chromosome 16, leaving 6067. Pruned 1 variant from chromosome 17, leaving 5722. Pruned 0 variants from chromosome 18, leaving 5578. Pruned 0 variants from chromosome 19, leaving 4364. Pruned 0 variants from chromosome 20, leaving 4916. Pruned 0 variants from chromosome 21, leaving 2811. Pruned 0 variants from chromosome 22, leaving 2831. Pruning complete. 80 of 172878 variants removed. Marker lists written to SampleData1/Fold_0/train_data.QC.clumped.pruned.prune.in and SampleData1/Fold_0/train_data.QC.clumped.pruned.prune.out . PLINK v1.90b7.2 64-bit (11 Dec 2023) www.cog-genomics.org/plink/1.9/ (C) 2005-2023 Shaun Purcell, Christopher Chang GNU General Public License v3 Logging to SampleData1/Fold_0/test_data.clumped.pruned.log. Options in effect: --bfile SampleData1/Fold_0/test_data --extract SampleData1/Fold_0/train_data.valid.snp --indep-pairwise 200 50 0.25 --make-bed --out SampleData1/Fold_0/test_data.clumped.pruned 63761 MB RAM detected; reserving 31880 MB for main workspace. 551892 variants loaded from .bim file. 95 people (44 males, 51 females) loaded from .fam. 95 phenotype values loaded from .fam. --extract: 172878 variants remaining. Using 1 thread (no multithreaded calculations invoked). Before main variant filters, 95 founders and 0 nonfounders present. Calculating allele frequencies... 10111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091929394959697989 done. Total genotyping rate is 0.999891. 172878 variants and 95 people pass filters and QC. Phenotype data is quantitative. --make-bed to SampleData1/Fold_0/test_data.clumped.pruned.bed + SampleData1/Fold_0/test_data.clumped.pruned.bim + SampleData1/Fold_0/test_data.clumped.pruned.fam ... 101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899done. Pruned 1829 variants from chromosome 1, leaving 12184. Pruned 1861 variants from chromosome 2, leaving 11952. Pruned 1567 variants from chromosome 3, leaving 10218. Pruned 1415 variants from chromosome 4, leaving 9626. Pruned 1347 variants from chromosome 5, leaving 9285. Pruned 1291 variants from chromosome 6, leaving 8777. Pruned 1238 variants from chromosome 7, leaving 8258. Pruned 1144 variants from chromosome 8, leaving 7723. Pruned 902 variants from chromosome 9, leaving 6866. Pruned 1090 variants from chromosome 10, leaving 7734. Pruned 1036 variants from chromosome 11, leaving 7384. Pruned 1061 variants from chromosome 12, leaving 7137. Pruned 771 variants from chromosome 13, leaving 5579. Pruned 683 variants from chromosome 14, leaving 5059. Pruned 603 variants from chromosome 15, leaving 4966. Pruned 710 variants from chromosome 16, leaving 5359. Pruned 605 variants from chromosome 17, leaving 5118. Pruned 648 variants from chromosome 18, leaving 4930. Pruned 384 variants from chromosome 19, leaving 3980. Pruned 559 variants from chromosome 20, leaving 4357. Pruned 297 variants from chromosome 21, leaving 2514. Pruned 276 variants from chromosome 22, leaving 2555. Pruning complete. 21317 of 172878 variants removed. Marker lists written to SampleData1/Fold_0/test_data.clumped.pruned.prune.in and SampleData1/Fold_0/test_data.clumped.pruned.prune.out . PLINK v1.90b7.2 64-bit (11 Dec 2023) www.cog-genomics.org/plink/1.9/ (C) 2005-2023 Shaun Purcell, Christopher Chang GNU General Public License v3 Logging to SampleData1/Fold_0/test_data.log. Options in effect: --bfile SampleData1/Fold_0/test_data.clumped.pruned --extract SampleData1/Fold_0/train_data.valid.snp --out SampleData1/Fold_0/test_data --pca 6 63761 MB RAM detected; reserving 31880 MB for main workspace. 172878 variants loaded from .bim file. 95 people (44 males, 51 females) loaded from .fam. 95 phenotype values loaded from .fam. --extract: 172878 variants remaining. Using up to 8 threads (change this with --threads). Before main variant filters, 95 founders and 0 nonfounders present. Calculating allele frequencies... 10111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091929394959697989 done. Total genotyping rate is 0.999891. 172878 variants and 95 people pass filters and QC. Phenotype data is quantitative. Relationship matrix calculation complete. --pca: Results saved to SampleData1/Fold_0/test_data.eigenval and SampleData1/Fold_0/test_data.eigenvec . PLINK v1.90b7.2 64-bit (11 Dec 2023) www.cog-genomics.org/plink/1.9/ (C) 2005-2023 Shaun Purcell, Christopher Chang GNU General Public License v3 Logging to SampleData1/Fold_0/train_data.log. Options in effect: --bfile SampleData1/Fold_0/train_data.QC.clumped.pruned --extract SampleData1/Fold_0/train_data.valid.snp --out SampleData1/Fold_0/train_data --pca 6 63761 MB RAM detected; reserving 31880 MB for main workspace. 172878 variants loaded from .bim file. 380 people (183 males, 197 females) loaded from .fam. 380 phenotype values loaded from .fam. --extract: 172878 variants remaining. Using up to 8 threads (change this with --threads). Before main variant filters, 380 founders and 0 nonfounders present. Calculating allele frequencies... 10111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091929394959697989 done. Total genotyping rate is 0.999891. 172878 variants and 380 people pass filters and QC. Phenotype data is quantitative. Relationship matrix calculation complete. --pca: Results saved to SampleData1/Fold_0/train_data.eigenval and SampleData1/Fold_0/train_data.eigenvec . ./gemma --bfile SampleData1/Fold_0/train_data.QC.clumped.pruned -beta SampleData1/gemma.txt -c SampleData1/Fold_0/train_data.COV_PCAgemma -lm 1 -o SampleData1/Fold_0/gemma-lm/gemma GEMMA 0.98.5 (2021-08-25) by Xiang Zhou, Pjotr Prins and team (C) 2012-2021 Reading Files ...
**** INFO: no intercept term is found in the cvt file: a column of 1s is added.
## number of total individuals = 380 ## number of analyzed individuals = 380 ## number of covariates = 8 ## number of phenotypes = 1 ## number of total SNPs/var = 172878 ## number of analyzed SNPs = 172878 ================================================== 100%
**** INFO: Done.
PLINK v1.90b7.2 64-bit (11 Dec 2023) www.cog-genomics.org/plink/1.9/ (C) 2005-2023 Shaun Purcell, Christopher Chang GNU General Public License v3 Logging to SampleData1/Fold_0/GEMMA-LM/train_data.log. Options in effect: --bfile SampleData1/Fold_0/train_data.QC.clumped.pruned --extract SampleData1/Fold_0/train_data.valid.snp --out SampleData1/Fold_0/GEMMA-LM/train_data --q-score-range SampleData1/Fold_0/range_list SampleData1/Fold_0/SNP.pvalue --score output/SampleData1/Fold_0/gemma-lm/gemma.assoc.txt 1 2 3 header 63761 MB RAM detected; reserving 31880 MB for main workspace. 172878 variants loaded from .bim file. 380 people (183 males, 197 females) loaded from .fam. 380 phenotype values loaded from .fam. --extract: 172878 variants remaining. Using 1 thread (no multithreaded calculations invoked). Before main variant filters, 380 founders and 0 nonfounders present. Calculating allele frequencies... 0%��1%��2%��3%��4%��5%��6%��7%��8%��9%��10%���11%���12%���13%���14%���15%���16%���17%���18%���19%���20%���21%���22%���23%���24%���25%���26%���27%���28%���29%���30%���31%���32%���33%���34%���35%���36%���37%���38%���39%���40%���41%���42%���43%���44%���45%���46%���47%���48%���49%���50%���51%���52%���53%���54%���55%���56%���57%���58%���59%���60%���61%���62%���63%���64%���65%���66%���67%���68%���69%���70%���71%���72%���73%���74%���75%���76%���77%���78%���79%���80%���81%���82%���83%���84%���85%���86%���87%���88%���89%���90%���91%���92%���93%���94%���95%���96%���97%���98%���99%���� done. Total genotyping rate is 0.999891. 172878 variants and 380 people pass filters and QC. Phenotype data is quantitative. --score: 172878 valid predictors loaded.
Warning: 326740 lines skipped in --q-score-range data file.
--score: 20 ranges processed. Results written to SampleData1/Fold_0/GEMMA-LM/train_data.*.profile. PLINK v1.90b7.2 64-bit (11 Dec 2023) www.cog-genomics.org/plink/1.9/ (C) 2005-2023 Shaun Purcell, Christopher Chang GNU General Public License v3 Logging to SampleData1/Fold_0/GEMMA-LM/test_data.log. Options in effect: --bfile SampleData1/Fold_0/test_data.clumped.pruned --extract SampleData1/Fold_0/train_data.valid.snp --out SampleData1/Fold_0/GEMMA-LM/test_data --q-score-range SampleData1/Fold_0/range_list SampleData1/Fold_0/SNP.pvalue --score output/SampleData1/Fold_0/gemma-lm/gemma.assoc.txt 1 2 3 header 63761 MB RAM detected; reserving 31880 MB for main workspace. 172878 variants loaded from .bim file. 95 people (44 males, 51 females) loaded from .fam. 95 phenotype values loaded from .fam. --extract: 172878 variants remaining. Using 1 thread (no multithreaded calculations invoked). Before main variant filters, 95 founders and 0 nonfounders present. Calculating allele frequencies... 0%��1%��2%��3%��4%��5%��6%��7%��8%��9%��10%���11%���12%���13%���14%���15%���16%���17%���18%���19%���20%���21%���22%���23%���24%���25%���26%���27%���28%���29%���30%���31%���32%���33%���34%���35%���36%���37%���38%���39%���40%���41%���42%���43%���44%���45%���46%���47%���48%���49%���50%���51%���52%���53%���54%���55%���56%���57%���58%���59%���60%���61%���62%���63%���64%���65%���66%���67%���68%���69%���70%���71%���72%���73%���74%���75%���76%���77%���78%���79%���80%���81%���82%���83%���84%���85%���86%���87%���88%���89%���90%���91%���92%���93%���94%���95%���96%���97%���98%���99%���� done. Total genotyping rate is 0.999891. 172878 variants and 95 people pass filters and QC. Phenotype data is quantitative. --score: 172878 valid predictors loaded.
Warning: 326740 lines skipped in --q-score-range data file.
--score: 20 ranges processed. Results written to SampleData1/Fold_0/GEMMA-LM/test_data.*.profile. Continous Phenotype!
Change the foldnumber variable.
#foldnumber = sys.argv[1]
foldnumber = "0" # Setting 'foldnumber' to "0"Or uncomment the following line:
# foldnumber = sys.argv[1]
python GEMMA-LM.py 0
python GEMMA-LM.py 1
python GEMMA-LM.py 2
python GEMMA-LM.py 3
python GEMMA-LM.py 4The following files should exist after the execution:
SampleData1/Fold_0/GEMMA-LM/Results.csvSampleData1/Fold_1/GEMMA-LM/Results.csvSampleData1/Fold_2/GEMMA-LM/Results.csvSampleData1/Fold_3/GEMMA-LM/Results.csvSampleData1/Fold_4/GEMMA-LM/Results.csv
# List of file names to check for existence
f = [
"./"+filedirec+"/Fold_0"+os.sep+result_directory+"Results.csv",
"./"+filedirec+"/Fold_1"+os.sep+result_directory+"Results.csv",
"./"+filedirec+"/Fold_2"+os.sep+result_directory+"Results.csv",
"./"+filedirec+"/Fold_3"+os.sep+result_directory+"Results.csv",
"./"+filedirec+"/Fold_4"+os.sep+result_directory+"Results.csv",
]
# Loop through each file name in the list
for loop in range(0,5):
# Check if the file exists in the specified directory for the given fold
if os.path.exists(filedirec+os.sep+"Fold_"+str(loop)+os.sep+result_directory+os.sep+"Results.csv"):
temp = pd.read_csv(filedirec+os.sep+"Fold_"+str(loop)+os.sep+result_directory+os.sep+"Results.csv")
print("Fold_",loop, "Yes, the file exists.")
#print(temp.head())
print("Number of P-values processed: ",len(temp))
# Print a message indicating that the file exists
else:
# Print a message indicating that the file does not exist
print("Fold_",loop, "No, the file does not exist.")
Fold_ 0 Yes, the file exists. Number of P-values processed: 20 Fold_ 1 Yes, the file exists. Number of P-values processed: 20 Fold_ 2 Yes, the file exists. Number of P-values processed: 20 Fold_ 3 Yes, the file exists. Number of P-values processed: 20 Fold_ 4 Yes, the file exists. Number of P-values processed: 20
print("We have to ensure when we sum the entries across all Folds, the same rows are merged!")
def sum_and_average_columns(data_frames):
"""Sum and average numerical columns across multiple DataFrames, and keep non-numerical columns unchanged."""
# Initialize DataFrame to store the summed results for numerical columns
summed_df = pd.DataFrame()
non_numerical_df = pd.DataFrame()
for df in data_frames:
# Identify numerical and non-numerical columns
numerical_cols = df.select_dtypes(include=[np.number]).columns
non_numerical_cols = df.select_dtypes(exclude=[np.number]).columns
# Sum numerical columns
if summed_df.empty:
summed_df = pd.DataFrame(0, index=range(len(df)), columns=numerical_cols)
summed_df[numerical_cols] = summed_df[numerical_cols].add(df[numerical_cols], fill_value=0)
# Keep non-numerical columns (take the first non-numerical entry for each column)
if non_numerical_df.empty:
non_numerical_df = df[non_numerical_cols]
else:
non_numerical_df[non_numerical_cols] = non_numerical_df[non_numerical_cols].combine_first(df[non_numerical_cols])
# Divide the summed values by the number of dataframes to get the average
averaged_df = summed_df / len(data_frames)
# Combine numerical and non-numerical DataFrames
result_df = pd.concat([averaged_df, non_numerical_df], axis=1)
return result_df
from functools import reduce
import os
import pandas as pd
from functools import reduce
def find_common_rows(allfoldsframe):
# Define the performance columns that need to be excluded
performance_columns = [
'Train_null_model', 'Train_pure_prs', 'Train_best_model',
'Test_pure_prs', 'Test_null_model', 'Test_best_model'
]
important_columns = [
'clump_p1',
'clump_r2',
'clump_kb',
'p_window_size',
'p_slide_size',
'p_LD_threshold',
'pvalue',
'referencepanel',
'PRSice-2_Model',
'effectsizes',
'gemmamodel',
'numberofpca',
'tempalpha',
'l1weight',
]
# Function to remove performance columns from a DataFrame
def drop_performance_columns(df):
return df.drop(columns=performance_columns, errors='ignore')
def get_important_columns(df ):
existing_columns = [col for col in important_columns if col in df.columns]
if existing_columns:
return df[existing_columns].copy()
else:
return pd.DataFrame()
# Drop performance columns from all DataFrames in the list
allfoldsframe_dropped = [drop_performance_columns(df) for df in allfoldsframe]
# Get the important columns.
allfoldsframe_dropped = [get_important_columns(df) for df in allfoldsframe_dropped]
# Iteratively find common rows and track unique and common rows
common_rows = allfoldsframe_dropped[0]
for i in range(1, len(allfoldsframe_dropped)):
# Get the next DataFrame
next_df = allfoldsframe_dropped[i]
# Count unique rows in the current DataFrame and the next DataFrame
unique_in_common = common_rows.shape[0]
unique_in_next = next_df.shape[0]
# Find common rows between the current common_rows and the next DataFrame
common_rows = pd.merge(common_rows, next_df, how='inner')
# Count the common rows after merging
common_count = common_rows.shape[0]
# Print the unique and common row counts
print(f"Iteration {i}:")
print(f"Unique rows in current common DataFrame: {unique_in_common}")
print(f"Unique rows in next DataFrame: {unique_in_next}")
print(f"Common rows after merge: {common_count}\n")
# Now that we have the common rows, extract these from the original DataFrames
extracted_common_rows_frames = []
for original_df in allfoldsframe:
# Merge the common rows with the original DataFrame, keeping only the rows that match the common rows
extracted_common_rows = pd.merge(common_rows, original_df, how='inner', on=common_rows.columns.tolist())
# Add the DataFrame with the extracted common rows to the list
extracted_common_rows_frames.append(extracted_common_rows)
# Print the number of rows in the common DataFrames
for i, df in enumerate(extracted_common_rows_frames):
print(f"DataFrame {i + 1} with extracted common rows has {df.shape[0]} rows.")
# Return the list of DataFrames with extracted common rows
return extracted_common_rows_frames
# Example usage (assuming allfoldsframe is populated as shown earlier):
allfoldsframe = []
# Loop through each file name in the list
for loop in range(0, 5):
# Check if the file exists in the specified directory for the given fold
file_path = os.path.join(filedirec, "Fold_" + str(loop), result_directory, "Results.csv")
if os.path.exists(file_path):
allfoldsframe.append(pd.read_csv(file_path))
# Print a message indicating that the file exists
print("Fold_", loop, "Yes, the file exists.")
else:
# Print a message indicating that the file does not exist
print("Fold_", loop, "No, the file does not exist.")
# Find the common rows across all folds and return the list of extracted common rows
extracted_common_rows_list = find_common_rows(allfoldsframe)
# Sum the values column-wise
# For string values, do not sum it the values are going to be the same for each fold.
# Only sum the numeric values.
divided_result = sum_and_average_columns(extracted_common_rows_list)
print(divided_result)
We have to ensure when we sum the entries across all Folds, the same rows are merged! Fold_ 0 Yes, the file exists. Fold_ 1 Yes, the file exists. Fold_ 2 Yes, the file exists. Fold_ 3 Yes, the file exists. Fold_ 4 Yes, the file exists. Iteration 1: Unique rows in current common DataFrame: 20 Unique rows in next DataFrame: 20 Common rows after merge: 20 Iteration 2: Unique rows in current common DataFrame: 20 Unique rows in next DataFrame: 20 Common rows after merge: 20 Iteration 3: Unique rows in current common DataFrame: 20 Unique rows in next DataFrame: 20 Common rows after merge: 20 Iteration 4: Unique rows in current common DataFrame: 20 Unique rows in next DataFrame: 20 Common rows after merge: 20 DataFrame 1 with extracted common rows has 20 rows. DataFrame 2 with extracted common rows has 20 rows. DataFrame 3 with extracted common rows has 20 rows. DataFrame 4 with extracted common rows has 20 rows. DataFrame 5 with extracted common rows has 20 rows. clump_p1 clump_r2 clump_kb p_window_size p_slide_size p_LD_threshold 0 1.0 0.1 200.0 200.0 50.0 0.25 1 1.0 0.1 200.0 200.0 50.0 0.25 2 1.0 0.1 200.0 200.0 50.0 0.25 3 1.0 0.1 200.0 200.0 50.0 0.25 4 1.0 0.1 200.0 200.0 50.0 0.25 5 1.0 0.1 200.0 200.0 50.0 0.25 6 1.0 0.1 200.0 200.0 50.0 0.25 7 1.0 0.1 200.0 200.0 50.0 0.25 8 1.0 0.1 200.0 200.0 50.0 0.25 9 1.0 0.1 200.0 200.0 50.0 0.25 10 1.0 0.1 200.0 200.0 50.0 0.25 11 1.0 0.1 200.0 200.0 50.0 0.25 12 1.0 0.1 200.0 200.0 50.0 0.25 13 1.0 0.1 200.0 200.0 50.0 0.25 14 1.0 0.1 200.0 200.0 50.0 0.25 15 1.0 0.1 200.0 200.0 50.0 0.25 16 1.0 0.1 200.0 200.0 50.0 0.25 17 1.0 0.1 200.0 200.0 50.0 0.25 18 1.0 0.1 200.0 200.0 50.0 0.25 19 1.0 0.1 200.0 200.0 50.0 0.25 pvalue numberofpca tempalpha l1weight Train_pure_prs 0 1.000000e-10 6.0 0.1 0.1 0.002434 1 3.359818e-10 6.0 0.1 0.1 0.002400 2 1.128838e-09 6.0 0.1 0.1 0.002485 3 3.792690e-09 6.0 0.1 0.1 0.002415 4 1.274275e-08 6.0 0.1 0.1 0.002328 5 4.281332e-08 6.0 0.1 0.1 0.002251 6 1.438450e-07 6.0 0.1 0.1 0.002255 7 4.832930e-07 6.0 0.1 0.1 0.002204 8 1.623777e-06 6.0 0.1 0.1 0.002185 9 5.455595e-06 6.0 0.1 0.1 0.002158 10 1.832981e-05 6.0 0.1 0.1 0.002142 11 6.158482e-05 6.0 0.1 0.1 0.002163 12 2.069138e-04 6.0 0.1 0.1 0.002127 13 6.951928e-04 6.0 0.1 0.1 0.002128 14 2.335721e-03 6.0 0.1 0.1 0.002135 15 7.847600e-03 6.0 0.1 0.1 0.002121 16 2.636651e-02 6.0 0.1 0.1 0.002105 17 8.858668e-02 6.0 0.1 0.1 0.002106 18 2.976351e-01 6.0 0.1 0.1 0.002093 19 1.000000e+00 6.0 0.1 0.1 0.002079 Train_null_model Train_best_model Test_pure_prs Test_null_model 0 0.23001 0.612312 0.000189 0.118692 1 0.23001 0.650862 0.000161 0.118692 2 0.23001 0.700011 0.000287 0.118692 3 0.23001 0.735191 0.000189 0.118692 4 0.23001 0.770487 0.000133 0.118692 5 0.23001 0.805497 0.000086 0.118692 6 0.23001 0.833525 0.000117 0.118692 7 0.23001 0.861316 0.000085 0.118692 8 0.23001 0.882882 0.000053 0.118692 9 0.23001 0.907348 0.000066 0.118692 10 0.23001 0.928414 0.000050 0.118692 11 0.23001 0.944670 0.000058 0.118692 12 0.23001 0.959983 0.000031 0.118692 13 0.23001 0.972348 0.000035 0.118692 14 0.23001 0.980373 0.000048 0.118692 15 0.23001 0.986389 0.000044 0.118692 16 0.23001 0.990613 0.000018 0.118692 17 0.23001 0.993441 0.000027 0.118692 18 0.23001 0.995111 0.000017 0.118692 19 0.23001 0.996284 0.000008 0.118692 Test_best_model gemmamodel 0 -0.131868 lm 1 -0.095716 lm 2 -0.092356 lm 3 -0.101386 lm 4 -0.062397 lm 5 -0.028827 lm 6 0.005456 lm 7 0.009978 lm 8 0.009618 lm 9 0.022998 lm 10 0.042863 lm 11 0.083666 lm 12 0.095783 lm 13 0.107346 lm 14 0.125370 lm 15 0.124072 lm 16 0.112928 lm 17 0.123797 lm 18 0.120410 lm 19 0.118083 lm
/tmp/ipykernel_72044/1455152146.py:24: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy non_numerical_df[non_numerical_cols] = non_numerical_df[non_numerical_cols].combine_first(df[non_numerical_cols]) /tmp/ipykernel_72044/1455152146.py:24: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy non_numerical_df[non_numerical_cols] = non_numerical_df[non_numerical_cols].combine_first(df[non_numerical_cols]) /tmp/ipykernel_72044/1455152146.py:24: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy non_numerical_df[non_numerical_cols] = non_numerical_df[non_numerical_cols].combine_first(df[non_numerical_cols]) /tmp/ipykernel_72044/1455152146.py:24: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy non_numerical_df[non_numerical_cols] = non_numerical_df[non_numerical_cols].combine_first(df[non_numerical_cols])
- One can report the results based on the best performance of the training data. For example, if for a specific combination of hyperparameters, the training performance is high, report the corresponding test performance.
- Example code:
python df = divided_result.sort_values(by='Train_best_model', ascending=False) print(df.iloc[0].to_markdown())
You can find the performance quality for binary phenotype using the following template:
PerformanceBinary
This figure shows the 8 different scenarios that can exist in the results, and the following table explains each scenario.
We classified performance based on the following table:
| Performance Level | Range |
|---|---|
| Low Performance | 0 to 0.5 |
| Moderate Performance | 0.6 to 0.7 |
| High Performance | 0.8 to 1 |
You can match the performance based on the following scenarios:
| Sce nario | What’s Happening | Implication |
|---|---|---|
High Test, High Tr ain* |
The model performs well on both training and test datasets, effectively learning the underlying patterns. | The model is well-tuned, generalizes well, and makes accurate predictions on both datasets. |
High Test, Mod erate Tr ain* |
The model generalizes well but may not be fully optimized on training data, missing some underlying patterns. | The model is fairly robust but may benefit from further tuning or more training to improve its learning. |
High Test, Low Tr ain* |
An unusual scenario, potentially indicating data leakage or overestimation of test performance. | The model’s performance is likely unreliable; investigate potential data issues or random noise. |
| Mod erate Test, High Tr ain | The model fits the training data well but doesn’t generalize as effectively, capturing only some test patterns. | The model is slightly overfitting; adjustments may be needed to improve generalization on unseen data. |
| Mod erate Test, Mod erate Tr ain | The model shows balanced but moderate performance on both datasets, capturing some patterns but missing others. | The model is moderately fitting; further improvements could be made in both training and generalization. |
| Mod erate Test, Low Tr ain | The model underperforms on training data and doesn’t generalize well, leading to moderate test performance. | The model may need more complexity, additional features, or better training to improve on both datasets. |
| Low Test, High Tr ain | The model overfits the training data, performing poorly on the test set. | The model doesn’t generalize well; simplifying the model or using regularization may help reduce overfitting. |
| Low Test, Low Tr ain | The model performs poorly on both training and test datasets, failing to learn the data patterns effectively. | The model is underfitting; it may need more complexity, additional features, or more data to improve performance. |
When publishing results, scenarios with moderate train and moderate test performance can be used for complex phenotypes or diseases. However, results showing high train and moderate test, high train and high test, and moderate train and high test are recommended.
For most phenotypes, results typically fall in the moderate train and moderate test performance category.
You can find the performance quality for continuous phenotypes using the following template:
PerformanceContinous
This figure shows the 8 different scenarios that can exist in the results, and the following table explains each scenario.
We classified performance based on the following table:
| Performance Level | Range |
|---|---|
| Low Performance | 0 to 0.2 |
| Moderate Performance | 0.3 to 0.7 |
| High Performance | 0.8 to 1 |
You can match the performance based on the following scenarios:
| Sce nario | What’s Happening | Implication |
|---|---|---|
High Test, High Tr ain* |
The model performs well on both training and test datasets, effectively learning the underlying patterns. | The model is well-tuned, generalizes well, and makes accurate predictions on both datasets. |
High Test, Mod erate Tr ain* |
The model generalizes well but may not be fully optimized on training data, missing some underlying patterns. | The model is fairly robust but may benefit from further tuning or more training to improve its learning. |
High Test, Low Tr ain* |
An unusual scenario, potentially indicating data leakage or overestimation of test performance. | The model’s performance is likely unreliable; investigate potential data issues or random noise. |
| Mod erate Test, High Tr ain | The model fits the training data well but doesn’t generalize as effectively, capturing only some test patterns. | The model is slightly overfitting; adjustments may be needed to improve generalization on unseen data. |
| Mod erate Test, Mod erate Tr ain | The model shows balanced but moderate performance on both datasets, capturing some patterns but missing others. | The model is moderately fitting; further improvements could be made in both training and generalization. |
| Mod erate Test, Low Tr ain | The model underperforms on training data and doesn’t generalize well, leading to moderate test performance. | The model may need more complexity, additional features, or better training to improve on both datasets. |
| Low Test, High Tr ain | The model overfits the training data, performing poorly on the test set. | The model doesn’t generalize well; simplifying the model or using regularization may help reduce overfitting. |
| Low Test, Low Tr ain | The model performs poorly on both training and test datasets, failing to learn the data patterns effectively. | The model is underfitting; it may need more complexity, additional features, or more data to improve performance. |
When publishing results, scenarios with moderate train and moderate test performance can be used for complex phenotypes or diseases. However, results showing high train and moderate test, high train and high test, and moderate train and high test are recommended.
For most continuous phenotypes, results typically fall in the moderate train and moderate test performance category.
One can also report the generalized performance by calculating the difference between the training and test performance, and the sum of the test and training performance. Report the result or hyperparameter combination for which the sum is high and the difference is minimal.
Example code:
df = divided_result.copy() df['Difference'] = abs(df['Train_best_model'] - df['Test_best_model']) df['Sum'] = df['Train_best_model'] + df['Test_best_model'] sorted_df = df.sort_values(by=['Sum', 'Difference'], ascending=[False, True]) print(sorted_df.iloc[0].to_markdown())
- Find the hyperparameters that have more than one unique value and
calculate their correlation with the following columns to understand
how they are affecting the performance of train and test sets:
Train_null_modelTrain_pure_prsTrain_best_modelTest_pure_prsTest_null_modelTest_best_model
- Once you have the results, you can find how hyperparameters affect the model performance.
- Analysis, like overfitting and underfitting, can be performed as well.
- The way you are going to report the results can vary.
- Results can be visualized, and other patterns in the data can be explored.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib notebook
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
df = divided_result.sort_values(by='Train_best_model', ascending=False)
print("1. Reporting Based on Best Training Performance:\n")
print(df.iloc[0].to_markdown())
df = divided_result.copy()
# Plot Train and Test best models against p-values
plt.figure(figsize=(10, 6))
plt.plot(df['pvalue'], df['Train_best_model'], label='Train_best_model', marker='o', color='royalblue')
plt.plot(df['pvalue'], df['Test_best_model'], label='Test_best_model', marker='o', color='darkorange')
# Highlight the p-value where both train and test are high
best_index = df[['Train_best_model']].sum(axis=1).idxmax()
best_pvalue = df.loc[best_index, 'pvalue']
best_train = df.loc[best_index, 'Train_best_model']
best_test = df.loc[best_index, 'Test_best_model']
# Use dark colors for the circles
plt.scatter(best_pvalue, best_train, color='darkred', s=100, label=f'Best Performance (Train)', edgecolor='black', zorder=5)
plt.scatter(best_pvalue, best_test, color='darkblue', s=100, label=f'Best Performance (Test)', edgecolor='black', zorder=5)
# Annotate the best performance with p-value, train, and test values
plt.text(best_pvalue, best_train, f'p={best_pvalue:.4g}\nTrain={best_train:.4g}', ha='right', va='bottom', fontsize=9, color='darkred')
plt.text(best_pvalue, best_test, f'p={best_pvalue:.4g}\nTest={best_test:.4g}', ha='right', va='top', fontsize=9, color='darkblue')
# Calculate Difference and Sum
df['Difference'] = abs(df['Train_best_model'] - df['Test_best_model'])
df['Sum'] = df['Train_best_model'] + df['Test_best_model']
# Sort the DataFrame
sorted_df = df.sort_values(by=['Sum', 'Difference'], ascending=[False, True])
#sorted_df = df.sort_values(by=[ 'Difference','Sum'], ascending=[ True,False])
# Highlight the general performance
general_index = sorted_df.index[0]
general_pvalue = sorted_df.loc[general_index, 'pvalue']
general_train = sorted_df.loc[general_index, 'Train_best_model']
general_test = sorted_df.loc[general_index, 'Test_best_model']
plt.scatter(general_pvalue, general_train, color='darkgreen', s=150, label='General Performance (Train)', edgecolor='black', zorder=6)
plt.scatter(general_pvalue, general_test, color='darkorange', s=150, label='General Performance (Test)', edgecolor='black', zorder=6)
# Annotate the general performance with p-value, train, and test values
plt.text(general_pvalue, general_train, f'p={general_pvalue:.4g}\nTrain={general_train:.4g}', ha='left', va='bottom', fontsize=9, color='darkgreen')
plt.text(general_pvalue, general_test, f'p={general_pvalue:.4g}\nTest={general_test:.4g}', ha='left', va='top', fontsize=9, color='darkorange')
# Add labels and legend
plt.xlabel('p-value')
plt.ylabel('Model Performance')
plt.title('Train vs Test Best Models')
plt.legend()
plt.show()
print("2. Reporting Generalized Performance:\n")
df = divided_result.copy()
df['Difference'] = abs(df['Train_best_model'] - df['Test_best_model'])
df['Sum'] = df['Train_best_model'] + df['Test_best_model']
sorted_df = df.sort_values(by=['Sum', 'Difference'], ascending=[False, True])
print(sorted_df.iloc[0].to_markdown())
print("3. Reporting the correlation of hyperparameters and the performance of 'Train_null_model', 'Train_pure_prs', 'Train_best_model', 'Test_pure_prs', 'Test_null_model', and 'Test_best_model':\n")
print("3. For string hyperparameters, we used one-hot encoding to find the correlation between string hyperparameters and 'Train_null_model', 'Train_pure_prs', 'Train_best_model', 'Test_pure_prs', 'Test_null_model', and 'Test_best_model'.")
print("3. We performed this analysis for those hyperparameters that have more than one unique value.")
correlation_columns = [
'Train_null_model', 'Train_pure_prs', 'Train_best_model',
'Test_pure_prs', 'Test_null_model', 'Test_best_model'
]
hyperparams = [col for col in divided_result.columns if len(divided_result[col].unique()) > 1]
hyperparams = list(set(hyperparams+correlation_columns))
# Separate numeric and string columns
numeric_hyperparams = [col for col in hyperparams if pd.api.types.is_numeric_dtype(divided_result[col])]
string_hyperparams = [col for col in hyperparams if pd.api.types.is_string_dtype(divided_result[col])]
# Encode string columns using one-hot encoding
divided_result_encoded = pd.get_dummies(divided_result, columns=string_hyperparams)
# Combine numeric hyperparams with the new one-hot encoded columns
encoded_columns = [col for col in divided_result_encoded.columns if col.startswith(tuple(string_hyperparams))]
hyperparams = numeric_hyperparams + encoded_columns
# Calculate correlations
correlations = divided_result_encoded[hyperparams].corr()
# Display correlation of hyperparameters with train/test performance columns
hyperparam_correlations = correlations.loc[hyperparams, correlation_columns]
hyperparam_correlations = hyperparam_correlations.fillna(0)
# Plotting the correlation heatmap
plt.figure(figsize=(12, 8))
ax = sns.heatmap(hyperparam_correlations, annot=True, cmap='viridis', fmt='.2f', cbar=True)
ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha='right')
# Rotate y-axis labels to horizontal
#ax.set_yticklabels(ax.get_yticklabels(), rotation=0, va='center')
plt.title('Correlation of Hyperparameters with Train/Test Performance')
plt.show()
sns.set_theme(style="whitegrid") # Choose your preferred style
pairplot = sns.pairplot(divided_result_encoded[hyperparams],hue = 'Test_best_model', palette='viridis')
# Adjust the figure size
pairplot.fig.set_size_inches(15, 15) # You can adjust the size as needed
for ax in pairplot.axes.flatten():
ax.set_xlabel(ax.get_xlabel(), rotation=90, ha='right') # X-axis labels vertical
#ax.set_ylabel(ax.get_ylabel(), rotation=0, va='bottom') # Y-axis labels horizontal
# Show the plot
plt.show()
1. Reporting Based on Best Training Performance: | | 19 | |:-----------------|:----------------------| | clump_p1 | 1.0 | | clump_r2 | 0.1 | | clump_kb | 200.0 | | p_window_size | 200.0 | | p_slide_size | 50.0 | | p_LD_threshold | 0.25 | | pvalue | 1.0 | | numberofpca | 6.0 | | tempalpha | 0.1 | | l1weight | 0.1 | | Train_pure_prs | 0.0020788226257149197 | | Train_null_model | 0.23001030414198947 | | Train_best_model | 0.9962835948364372 | | Test_pure_prs | 7.571856271981758e-06 | | Test_null_model | 0.11869244971793831 | | Test_best_model | 0.11808345245138263 | | gemmamodel | lm |
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2. Reporting Generalized Performance: | | 17 | |:-----------------|:-----------------------| | clump_p1 | 1.0 | | clump_r2 | 0.1 | | clump_kb | 200.0 | | p_window_size | 200.0 | | p_slide_size | 50.0 | | p_LD_threshold | 0.25 | | pvalue | 0.0885866790410083 | | numberofpca | 6.0 | | tempalpha | 0.1 | | l1weight | 0.1 | | Train_pure_prs | 0.00210616505687766 | | Train_null_model | 0.23001030414198947 | | Train_best_model | 0.9934409436267051 | | Test_pure_prs | 2.7152105664063342e-05 | | Test_null_model | 0.11869244971793831 | | Test_best_model | 0.1237971661455635 | | gemmamodel | lm | | Difference | 0.8696437774811416 | | Sum | 1.1172381097722686 | 3. Reporting the correlation of hyperparameters and the performance of 'Train_null_model', 'Train_pure_prs', 'Train_best_model', 'Test_pure_prs', 'Test_null_model', and 'Test_best_model': 3. For string hyperparameters, we used one-hot encoding to find the correlation between string hyperparameters and 'Train_null_model', 'Train_pure_prs', 'Train_best_model', 'Test_pure_prs', 'Test_null_model', and 'Test_best_model'. 3. We performed this analysis for those hyperparameters that have more than one unique value.
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