XPBLUP

XPBLUP#

In this notebook, we will use XPBLUP to calculate the PRS. XPBLUP GitHub Repository

To get started, clone the repository:

git clone https://github.com/tanglab/XP-BLUP.git

Navigate to the XP-BLUP directory and make the script executable:

cd XP-BLUP
chmod u+x xpblup.sh

XPBLUP also requires Plink 1.9 and GCTA. Download these tools and place them in the working directory.

Modifying the Script for Linux#

When working on Linux, open the xpblup.sh file and modify the following lines:

Before:

PLINK_=plink
GCTA_=gcta64

After:

PLINK_=./plink
GCTA_=./gcta64

We also modified the line:

${GCTA_} --reml --mgrm-bin ${grmlist} --pheno ${pheno_} --out ${out_prefix} --reml-est-fix --reml-pred-rand

To:

if reml algorithm does not converge on can use the following line.

${GCTA_} --reml --mgrm-bin ${grmlist} --pheno ${pheno_} --reml-no-constrain --out ${out_prefix} --reml-est-fix --reml-pred-rand

On Linux, to execute tools, the ./ prefix is required.

Finally, copy the ./xpblup file into the current working directory.

GWAS file processing for XPBLUP#

This tool does not need the GWAS data, but it uses the genotype data.

import os
import pandas as pd
import numpy as np
import sys

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"

#filedirec = sys.argv[1]

filedirec = "SampleData1"
#filedirec = "asthma_19"
#filedirec = "migraine_0"

Define Hyperparameters#

Define hyperparameters to be optimized and set initial values.

Extract Valid SNPs from Clumped File#

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.

Execution Path#

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:

  1. filedirec = "SampleData1" or filedirec = sys.argv[1]

  2. foldnumber = "0" or foldnumber = 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:

P-values#

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"])

Define Helper Functions#

  1. Perform Clumping and Pruning

  2. Calculate PCA Using Plink

  3. Fit Binary Phenotype and Save Results

  4. 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",
    "--geno",str(0.00000000000001),
    "--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, 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, 

                    "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, 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, 

                    "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

Execute XPBLUP#

# Define a global variable to store results
prs_result = pd.DataFrame()
def transform_xpblup_data(traindirec, newtrainfilename,p, 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)
     
    # Asume the test and train set is the same. because later we will use Plink to process multiple ranges.
    
    command = [
        "./xpblup.sh",
        "--train="+traindirec+os.sep+newtrainfilename ,
        "--test="+traindirec+os.sep+newtrainfilename+".clumped.pruned",
        "--snplist="+traindirec+os.sep+trainfilename+".valid.snp",
        "--outdir="+traindirec+os.sep+Name+os.sep,
        "--outprefix="+"score"
    ]

    # Execute the command
    subprocess.run(command)
    print(" ".join(command))
    
    merged = pd.read_csv(traindirec+os.sep+Name+os.sep+"score.betaRecal",header=None,sep="\s+")
    print(merged.head())
    
    if check_phenotype_is_binary_or_continous(filedirec)=="Binary":
        merged[2] = np.log(merged[2])

    else:
        pass   
    merged[2] = merged[2].replace([np.inf, -np.inf], np.nan)  # Replace inf and -inf with NaN
    merged[2] = merged[2].fillna(0)     
    
    merged.to_csv(traindirec+os.sep+Name+os.sep+"score.gwas",sep="\t",index=False)
    
   
    # Caluclate Plink Score.
    command = [
        "./plink",
         "--bfile", traindirec+os.sep+newtrainfilename+".clumped.pruned",
        ### SNP column = 3, Effect allele column 1 = 4, OR column=9
        "--score", traindirec+os.sep+Name+os.sep+"score.gwas", "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", traindirec+os.sep+Name+os.sep+"score.gwas", "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)


    # At this stage we have to store all the files the score profiles in the pandas frame for finding the best profile value.
    #
    # Specify the directory containing the files
    trainprofiledirec = traindirec+os.sep+Name
    # Initialize an empty DataFrame to store the last column of each file
    result_df = pd.DataFrame()

    # Loop through files in the directory
    for filename in os.listdir(trainprofiledirec):
        # Check if the file name contains 'train'
        if 'train' in filename and "profile" in filename:
            file_path = os.path.join(trainprofiledirec, filename)
            #print(file_path)
            # Read the file into a DataFrame
            current_df = pd.read_csv(file_path,sep="\s+")  # Adjust delimiter if needed
            
            # Extract the last column and add it to the result DataFrame
            last_column_name = filename.split("train_data")[1].split(".profile")[0]  # Extract the pvalue from the file.
            #print(filename.split("train_data")[1].split(".profile")[0][1:])
            result_df[last_column_name[1:]] = current_df["SCORE"].values # Save the last column that contains the score
    #print(result_df.head())
    result_df.to_csv(traindirec+os.sep+Name+os.sep+"train_prs_"+p+"_"+p1_val+"_"+p2_val+"_"+p3_val+"_"+c1_val+"_"+c2_val+"_"+c3_val+".csv",index=False)
    
    trainprofiledirec = traindirec+os.sep+Name
    # Initialize an empty DataFrame to store the last column of each file
    result_df = pd.DataFrame()

    # Loop through files in the directory
    for filename in os.listdir(trainprofiledirec):
        # Check if the file name contains 'train'
        if 'test' in filename and "profile" in filename:
            file_path = os.path.join(trainprofiledirec, filename)
            #print(file_path)
            # Read the file into a DataFrame
            current_df = pd.read_csv(file_path,sep="\s+")  # Adjust delimiter if needed
            
            # Extract the last column and add it to the result DataFrame
            last_column_name = filename.split("test_data")[1].split(".profile")[0]  # Extract the pvalue from the file.
            #print(filename.split("train_data")[1].split(".profile")[0][1:])
            result_df[last_column_name[1:]] = current_df["SCORE"].values # Save the last column that contains the score
    #print(result_df.head())
    result_df.to_csv(traindirec+os.sep+Name+os.sep+"test_prs_"+p+"_"+p1_val+"_"+p2_val+"_"+p3_val+"_"+c1_val+"_"+c2_val+"_"+c3_val+".csv",index=False)
    

    # At this stage the scores are finalizied. 
    # The next step is to fit the model and find the explained variance by each profile.

    # Load the PCA and Load the Covariates for trainingdatafirst.
    
    if check_phenotype_is_binary_or_continous(filedirec)=="Binary":
        print("Binary Phenotype!")
        fit_binary_phenotype_on_PRS(traindirec, newtrainfilename,p, 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, p1_val, p2_val, p3_val, c1_val, c2_val, c3_val,Name,pvaluefile)
            
 

 
result_directory = "XPBLUP"
# 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:
        
        transform_xpblup_data(folddirec, newtrainfilename, p, str(p1_val), str(p2_val), str(p3_val), str(c1_val), str(c2_val), str(c3_val), result_directory, pvaluefile)

./gcta
./plink
*******************************************************************
* Genome-wide Complex Trait Analysis (GCTA)
* version v1.94.1 Linux
* Built at Nov 15 2022 21:14:25, by GCC 8.5
* (C) 2010-present, Yang Lab, Westlake University
* Please report bugs to Jian Yang <jian.yang@westlake.edu.cn>
*******************************************************************
Analysis started at 05:33:31 AEST on Thu Oct 17 2024.
Hostname: login01

Accepted options:
--bfile SampleData1/Fold_0/train_data.QC
--extract SampleData1/Fold_0/train_data.valid.snp
--make-grm-bin
--reml-no-constrain
--out SampleData1/Fold_0/XPBLUP//score_small

Note: This is a multi-thread program. You could specify the number of threads by the --thread-num option to speed up the computation if there are multiple processors in your machine.

Reading PLINK FAM file from [SampleData1/Fold_0/train_data.QC.fam].
380 individuals to be included from [SampleData1/Fold_0/train_data.QC.fam].
Reading PLINK BIM file from [SampleData1/Fold_0/train_data.QC.bim].
491952 SNPs to be included from [SampleData1/Fold_0/train_data.QC.bim].
Reading a list of SNPs from [SampleData1/Fold_0/train_data.valid.snp].
172070 SNPs are extracted from [SampleData1/Fold_0/train_data.valid.snp].
Reading PLINK BED file from [SampleData1/Fold_0/train_data.QC.bed] in SNP-major format ...
Genotype data for 380 individuals and 172070 SNPs to be included from [SampleData1/Fold_0/train_data.QC.bed].
Calculating allele frequencies ...
Recoding genotypes (individual major mode) ...

Calculating the genetic relationship matrix (GRM) ... (Note: default speed-optimized mode, may use huge RAM)

Summary of the GRM:
Mean of diagonals = 1.00266
Variance of diagonals = 0.00232014
Mean of off-diagonals = -0.00264554
Variance of off-diagonals = 3.35923e-05
GRM of 380 individuals has been saved in the file [SampleData1/Fold_0/XPBLUP//score_small.grm.bin] (in binary format).
Number of SNPs to calculate the genetic relationship between each pair of individuals has been saved in the file [SampleData1/Fold_0/XPBLUP//score_small.grm.N.bin] (in binary format).
IDs for the GRM file [SampleData1/Fold_0/XPBLUP//score_small.grm.bin] have been saved in the file [SampleData1/Fold_0/XPBLUP//score_small.grm.id].

Analysis finished at 05:33:37 AEST on Thu Oct 17 2024
Overall computational time: 6.04 sec.
*******************************************************************
* Genome-wide Complex Trait Analysis (GCTA)
* version v1.94.1 Linux
* Built at Nov 15 2022 21:14:25, by GCC 8.5
* (C) 2010-present, Yang Lab, Westlake University
* Please report bugs to Jian Yang <jian.yang@westlake.edu.cn>
*******************************************************************
Analysis started at 05:33:37 AEST on Thu Oct 17 2024.
Hostname: login01

Accepted options:
--bfile SampleData1/Fold_0/train_data.QC
--make-grm-bin
--reml-no-constrain
--out SampleData1/Fold_0/XPBLUP//score_full

Note: This is a multi-thread program. You could specify the number of threads by the --thread-num option to speed up the computation if there are multiple processors in your machine.

Reading PLINK FAM file from [SampleData1/Fold_0/train_data.QC.fam].
380 individuals to be included from [SampleData1/Fold_0/train_data.QC.fam].
Reading PLINK BIM file from [SampleData1/Fold_0/train_data.QC.bim].
491952 SNPs to be included from [SampleData1/Fold_0/train_data.QC.bim].
Reading PLINK BED file from [SampleData1/Fold_0/train_data.QC.bed] in SNP-major format ...
Genotype data for 380 individuals and 491952 SNPs to be included from [SampleData1/Fold_0/train_data.QC.bed].
Calculating allele frequencies ...
Recoding genotypes (individual major mode) ...

Calculating the genetic relationship matrix (GRM) ... (Note: default speed-optimized mode, may use huge RAM)

Summary of the GRM:
Mean of diagonals = 1.00305
Variance of diagonals = 0.00122901
Mean of off-diagonals = -0.00264684
Variance of off-diagonals = 4.29569e-05
GRM of 380 individuals has been saved in the file [SampleData1/Fold_0/XPBLUP//score_full.grm.bin] (in binary format).
Number of SNPs to calculate the genetic relationship between each pair of individuals has been saved in the file [SampleData1/Fold_0/XPBLUP//score_full.grm.N.bin] (in binary format).
IDs for the GRM file [SampleData1/Fold_0/XPBLUP//score_full.grm.bin] have been saved in the file [SampleData1/Fold_0/XPBLUP//score_full.grm.id].

Analysis finished at 05:33:52 AEST on Thu Oct 17 2024
Overall computational time: 15.19 sec.
*******************************************************************
* Genome-wide Complex Trait Analysis (GCTA)
* version v1.94.1 Linux
* Built at Nov 15 2022 21:14:25, by GCC 8.5
* (C) 2010-present, Yang Lab, Westlake University
* Please report bugs to Jian Yang <jian.yang@westlake.edu.cn>
*******************************************************************
Analysis started at 05:33:52 AEST on Thu Oct 17 2024.
Hostname: login01

Accepted options:
--reml
--mgrm-bin SampleData1/Fold_0/XPBLUP//grmList.txt
--pheno SampleData1/Fold_0/XPBLUP//train_.pheno
--reml-no-constrain
--out SampleData1/Fold_0/XPBLUP//score
--reml-est-fix
--reml-pred-rand

Note: This is a multi-thread program. You could specify the number of threads by the --thread-num option to speed up the computation if there are multiple processors in your machine.

Reading phenotypes from [SampleData1/Fold_0/XPBLUP//train_.pheno].
Non-missing phenotypes of 380 individuals are included from [SampleData1/Fold_0/XPBLUP//train_.pheno].

There are 2 GRM file names specified in the file [SampleData1/Fold_0/XPBLUP//grmList.txt].
Reading the GRM from the 1th file ...
Reading IDs of the GRM from [SampleData1/Fold_0/XPBLUP//score_full.grm.id].
380 IDs are read from [SampleData1/Fold_0/XPBLUP//score_full.grm.id].
Reading the GRM from [SampleData1/Fold_0/XPBLUP//score_full.grm.bin].
GRM for 380 individuals are included from [SampleData1/Fold_0/XPBLUP//score_full.grm.bin].
Reading the GRM from the 2th file ...
Reading IDs of the GRM from [SampleData1/Fold_0/XPBLUP//score_small.grm.id].
380 IDs are read from [SampleData1/Fold_0/XPBLUP//score_small.grm.id].
Reading the GRM from [SampleData1/Fold_0/XPBLUP//score_small.grm.bin].
GRM for 380 individuals are included from [SampleData1/Fold_0/XPBLUP//score_small.grm.bin].
380 individuals are in common in these files.

Performing  REML analysis ... (Note: may take hours depending on sample size).
380 observations, 1 fixed effect(s), and 3 variance component(s)(including residual variance).
Calculating prior values of variance components by EM-REML ...
Updated prior values: 0.297811 0.297739 0.297086
logL: -170.32
Running AI-REML algorithm ...
Iter.	logL	V(G1)	V(G2)	V(e)	
1	-170.32	0.63269	0.31137	-0.05495	
2	-169.83	1.55281	0.28653	-0.95391	
3	-169.20	1.82423	0.14108	-1.07364	
4	-169.17	1.87472	0.10380	-1.08644	
5	-169.17	1.88321	0.09739	-1.08848	
6	-169.17	1.88463	0.09632	-1.08882	
7	-169.17	1.88487	0.09614	-1.08888	
8	-169.17	1.88490	0.09611	-1.08889	
Log-likelihood ratio converged.

Calculating the logLikelihood for the reduced model ...
(variance component 1 is dropped from the model)
Calculating prior values of variance components by EM-REML ...
Updated prior values: 0.44691 0.44536
logL: -170.58843
Running AI-REML algorithm ...
Iter.	logL	V(G2)	V(e)	
1	-170.59	0.72675	0.16282	
2	-170.22	1.43195	-0.54529	
3	-169.79	1.52469	-0.63387	
4	-169.78	1.52936	-0.63843	
5	-169.78	1.52954	-0.63861	
6	-169.78	1.52955	-0.63862	
Log-likelihood ratio converged.

Summary result of REML analysis:
Source	Variance	SE
V(G1)	1.884905	1.562556
V(G2)	0.096110	1.464899
V(e)	-1.088888	0.900560
Vp	0.892126	0.065541
V(G1)/Vp	2.112822	1.729402
V(G2)/Vp	0.107731	1.642158

Sum of V(G)/Vp	2.220554	0.992920

Sampling variance/covariance of the estimates of variance components:
2.441582e+00	-1.872689e+00	-5.498696e-01	
-1.872689e+00	2.145930e+00	-2.745534e-01	
-5.498696e-01	-2.745534e-01	8.110080e-01	
Estimateof fixed effect:

Source	Estimate	SE
mean	170.139683	-nan

Summary result of REML analysis has been saved in the file [SampleData1/Fold_0/XPBLUP//score.hsq].

BLUP solutions of the genetic effects for 380 individuals have been saved in the file [SampleData1/Fold_0/XPBLUP//score.indi.blp].

Analysis finished at 05:33:52 AEST on Thu Oct 17 2024
Overall computational time: 0.29 sec.
*******************************************************************
* Genome-wide Complex Trait Analysis (GCTA)
* version v1.94.1 Linux
* Built at Nov 15 2022 21:14:25, by GCC 8.5
* (C) 2010-present, Yang Lab, Westlake University
* Please report bugs to Jian Yang <jian.yang@westlake.edu.cn>
*******************************************************************
Analysis started at 05:33:52 AEST on Thu Oct 17 2024.
Hostname: login01

Accepted options:
--bfile SampleData1/Fold_0/train_data.QC
--mgrm-bin SampleData1/Fold_0/XPBLUP//grmList.txt
--blup-snp SampleData1/Fold_0/XPBLUP//score.indi.blp
--out SampleData1/Fold_0/XPBLUP//score


Reading PLINK FAM file from [SampleData1/Fold_0/train_data.QC.fam].
380 individuals to be included from [SampleData1/Fold_0/train_data.QC.fam].
BLUP solution to the total genetic effects for 380 individuals have been read from [SampleData1/Fold_0/XPBLUP//score.indi.blp].
Reading PLINK BIM file from [SampleData1/Fold_0/train_data.QC.bim].

491952 SNPs to be included from [SampleData1/Fold_0/train_data.QC.bim].
Reading PLINK BED file from [SampleData1/Fold_0/train_data.QC.bed] in SNP-major format ...
Genotype data for 380 individuals and 491952 SNPs to be included from [SampleData1/Fold_0/train_data.QC.bed].
Calculating allele frequencies ...
Calculating the BLUP solutions to SNP effects ...
Writing BLUP solutions of SNP effects for 491952 SNPs to [SampleData1/Fold_0/XPBLUP//score.snp.blp].
BLUP solutions of SNP effects for 491952 SNPs have been saved in the file [SampleData1/Fold_0/XPBLUP//score.snp.blp].

Analysis finished at 05:34:04 AEST on Thu Oct 17 2024
Overall computational time: 11.72 sec.
SampleData1/Fold_0/XPBLUP//score.betaRecal
SampleData1/Fold_0/XPBLUP//score.betaRecal
SampleData1/Fold_0/XPBLUP//score.betaRecal
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/XPBLUP//score.predict.log.
Options in effect:
  --allow-no-sex
  --bfile SampleData1/Fold_0/train_data.QC.clumped.pruned
  --out SampleData1/Fold_0/XPBLUP//score.predict
  --score SampleData1/Fold_0/XPBLUP//score.betaRecal

63761 MB RAM detected; reserving 31880 MB for main workspace.
172070 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 exactly 1.
172070 variants and 380 people pass filters and QC.
Phenotype data is quantitative.

Warning: 319882 lines skipped in --score file (319882 due to variant ID
mismatch, 0 due to allele code mismatch); see
SampleData1/Fold_0/XPBLUP//score.predict.nopred for details.

--score: 172070 valid predictors loaded.
--score: Results written to SampleData1/Fold_0/XPBLUP//score.predict.profile .
./xpblup.sh --train=SampleData1/Fold_0/train_data.QC --test=SampleData1/Fold_0/train_data.QC.clumped.pruned --snplist=SampleData1/Fold_0/train_data.valid.snp --outdir=SampleData1/Fold_0/XPBLUP/ --outprefix=score
            0  1         2
0   rs3131962  A -182.6790
1   rs4040617  G -142.6400
2  rs79373928  G  -90.5573
3  rs11240779  G  100.3950
4  rs57181708  G  427.3270
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/XPBLUP/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/XPBLUP/train_data
  --q-score-range SampleData1/Fold_0/range_list SampleData1/Fold_0/SNP.pvalue
  --score SampleData1/Fold_0/XPBLUP/score.gwas 1 2 3 header

63761 MB RAM detected; reserving 31880 MB for main workspace.
172070 variants loaded from .bim file.
380 people (183 males, 197 females) loaded from .fam.
380 phenotype values loaded from .fam.
--extract: 172070 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 exactly 1.
172070 variants and 380 people pass filters and QC.
Phenotype data is quantitative.
--score: 172070 valid predictors loaded.

Warning: 319882 lines skipped in --score file (319882 due to variant ID
mismatch, 0 due to allele code mismatch); see
SampleData1/Fold_0/XPBLUP/train_data.nopred for details.
Warning: 327548 lines skipped in --q-score-range data file.

--score: 20 ranges processed.
Results written to SampleData1/Fold_0/XPBLUP/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/XPBLUP/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/XPBLUP/test_data
  --q-score-range SampleData1/Fold_0/range_list SampleData1/Fold_0/SNP.pvalue
  --score SampleData1/Fold_0/XPBLUP/score.gwas 1 2 3 header

63761 MB RAM detected; reserving 31880 MB for main workspace.
172070 variants loaded from .bim file.
95 people (44 males, 51 females) loaded from .fam.
95 phenotype values loaded from .fam.
--extract: 172070 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 exactly 1.
172070 variants and 95 people pass filters and QC.
Phenotype data is quantitative.

Warning: 319882 lines skipped in --score file (319882 due to variant ID
mismatch, 0 due to allele code mismatch); see
SampleData1/Fold_0/XPBLUP/test_data.nopred for details.
Warning: 327548 lines skipped in --q-score-range data file.

--score: 172070 valid predictors loaded.
--score: 20 ranges processed.
Results written to SampleData1/Fold_0/XPBLUP/test_data.*.profile.
Continous Phenotype!

Repeat the process for each fold.#

Change the foldnumber variable.

#foldnumber = sys.argv[1]
foldnumber = "0"  # Setting 'foldnumber' to "0"

Or uncomment the following line:

# foldnumber = sys.argv[1]
python XPBLUP.py 0
python XPBLUP.py 1
python XPBLUP.py 2
python XPBLUP.py 3
python XPBLUP.py 4

The following files should exist after the execution:

  1. SampleData1/Fold_0/XPBLUP/Results.csv

  2. SampleData1/Fold_1/XPBLUP/Results.csv

  3. SampleData1/Fold_2/XPBLUP/Results.csv

  4. SampleData1/Fold_3/XPBLUP/Results.csv

  5. SampleData1/Fold_4/XPBLUP/Results.csv

Check the results file for each fold.#

import os
import pandas as pd
import numpy as np

 

# 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

Sum the results for each fold.#

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',
        'h2model',
        
        'model',
        '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      -25.733745   
1   3.359818e-10          6.0        0.1       0.1      -21.961333   
2   1.128838e-09          6.0        0.1       0.1      -20.538440   
3   3.792690e-09          6.0        0.1       0.1      -18.024061   
4   1.274275e-08          6.0        0.1       0.1      -15.212727   
5   4.281332e-08          6.0        0.1       0.1      -13.526064   
6   1.438450e-07          6.0        0.1       0.1      -12.629870   
7   4.832930e-07          6.0        0.1       0.1      -11.280950   
8   1.623777e-06          6.0        0.1       0.1      -10.375528   
9   5.455595e-06          6.0        0.1       0.1       -9.554998   
10  1.832981e-05          6.0        0.1       0.1       -8.881383   
11  6.158482e-05          6.0        0.1       0.1       -8.678754   
12  2.069138e-04          6.0        0.1       0.1       -7.993278   
13  6.951928e-04          6.0        0.1       0.1       -7.826460   
14  2.335721e-03          6.0        0.1       0.1       -7.770797   
15  7.847600e-03          6.0        0.1       0.1       -7.550855   
16  2.636651e-02          6.0        0.1       0.1       -7.380200   
17  8.858668e-02          6.0        0.1       0.1       -7.406541   
18  2.976351e-01          6.0        0.1       0.1       -7.207353   
19  1.000000e+00          6.0        0.1       0.1       -7.060744   

    Train_null_model  Train_best_model  Test_pure_prs  Test_null_model  \
0            0.22945          0.581445     -16.275039          0.12828   
1            0.22945          0.616574     -13.545241          0.12828   
2            0.22945          0.666009     -11.738694          0.12828   
3            0.22945          0.698591      -9.380632          0.12828   
4            0.22945          0.729021      -7.321995          0.12828   
5            0.22945          0.763070      -5.912187          0.12828   
6            0.22945          0.796037      -4.496649          0.12828   
7            0.22945          0.827195      -3.341155          0.12828   
8            0.22945          0.852599      -2.615464          0.12828   
9            0.22945          0.881237      -1.919708          0.12828   
10           0.22945          0.908502      -1.361345          0.12828   
11           0.22945          0.929042      -1.050080          0.12828   
12           0.22945          0.949106      -0.792615          0.12828   
13           0.22945          0.962974      -0.570558          0.12828   
14           0.22945          0.973556      -0.347106          0.12828   
15           0.22945          0.980664      -0.185841          0.12828   
16           0.22945          0.985132      -0.122804          0.12828   
17           0.22945          0.989023      -0.043622          0.12828   
18           0.22945          0.991361      -0.011274          0.12828   
19           0.22945          0.992673      -0.009371          0.12828   

    Test_best_model  
0         -0.048726  
1         -0.053756  
2         -0.001910  
3         -0.026597  
4         -0.026754  
5         -0.037847  
6         -0.014160  
7         -0.016247  
8         -0.005797  
9          0.008429  
10         0.011771  
11         0.016251  
12        -0.005903  
13         0.006487  
14         0.028110  
15         0.032464  
16         0.016675  
17         0.027324  
18         0.018488  
19         0.013314

Results#

1. Reporting Based on Best Training Performance:#

  • 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:

    df = divided_result.sort_values(by='Train_best_model', ascending=False)
print(df.iloc[0].to_markdown())

Binary Phenotypes Result Analysis#

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:

Scenario

What’s Happening

Implication

High Test, High Train

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, Moderate Train

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 Train

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.

Moderate Test, High Train

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.

Moderate Test, Moderate Train

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.

Moderate Test, Low Train

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 Train

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 Train

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.
Recommendations for Publishing Results#

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.

Continuous Phenotypes Result Analysis#

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:

Scenario

What’s Happening

Implication

High Test, High Train

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, Moderate Train

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 Train

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.

Moderate Test, High Train

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.

Moderate Test, Moderate Train

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.

Moderate Test, Low Train

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 Train

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 Train

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.
Recommendations for Publishing Results#

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.

2. Reporting Generalized Performance:#

  • 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())

3. Reporting Hyperparameters Affecting Test and Train Performance:#

  • 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_model

    • Train_pure_prs

    • Train_best_model

    • Test_pure_prs

    • Test_null_model

    • Test_best_model

4. Other Analysis#

  1. Once you have the results, you can find how hyperparameters affect the model performance.

  2. Analysis, like overfitting and underfitting, can be performed as well.

  3. The way you are going to report the results can vary.

  4. 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          |
| clump_r2         |   0.1        |
| clump_kb         | 200          |
| p_window_size    | 200          |
| p_slide_size     |  50          |
| p_LD_threshold   |   0.25       |
| pvalue           |   1          |
| numberofpca      |   6          |
| tempalpha        |   0.1        |
| l1weight         |   0.1        |
| Train_pure_prs   |  -7.06074    |
| Train_null_model |   0.22945    |
| Train_best_model |   0.992673   |
| Test_pure_prs    |  -0.00937071 |
| Test_null_model  |   0.12828    |
| Test_best_model  |   0.0133139  |

2. Reporting Generalized Performance:

|                  |          17 |
|:-----------------|------------:|
| clump_p1         |   1         |
| clump_r2         |   0.1       |
| clump_kb         | 200         |
| p_window_size    | 200         |
| p_slide_size     |  50         |
| p_LD_threshold   |   0.25      |
| pvalue           |   0.0885867 |
| numberofpca      |   6         |
| tempalpha        |   0.1       |
| l1weight         |   0.1       |
| Train_pure_prs   |  -7.40654   |
| Train_null_model |   0.22945   |
| Train_best_model |   0.989023  |
| Test_pure_prs    |  -0.0436223 |
| Test_null_model  |   0.12828   |
| Test_best_model  |   0.027324  |
| Difference       |   0.961699  |
| Sum              |   1.01635   |
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.