diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index 5008ddf..0000000 Binary files a/.DS_Store and /dev/null differ diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 5c14634..0000000 --- a/.gitignore +++ /dev/null @@ -1,13 +0,0 @@ -runner.py -Source/__pycache__/ -top10k_pruned_BMIres.csv -pareto_front.png -snp_hub.csv -overall_feature_importance.csv -top_20_features.png -all_pipelines_PFI_values.csv -non_pruned.csv -Random_Pipelines/ -runner-original.sb -runner-new.sb -total_runtime.csv \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..f288702 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md index 811deee..f9268f6 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,17 @@ -# StarBASE +# StarBASE-GP
- Logo + Logo
-StarBASE : Star-Based Automated Single-locus and Epistasis analysis pipeline - Genetic Programming +StarBASE-GP : Star-Based Automated Single-locus and Epistasis analysis tool - Genetic Programming ================================== + +## Abstract + +> We present the Star-Based Automated Single-locus and Epistasis analysis tool – Genetic Programming (StarBASE-GP), an automated framework for discovering meaningful genetic variants associated with phenotypic variation in large-scale genomic datasets. +StarBASE-GP uses a genetic programming–based multi-objective optimization strategy to evolve machine learning pipelines that simultaneously maximize explanatory power ($r^2$) and minimize pipeline complexity. +Biological domain knowledge is integrated at multiple stages, including the use of nine inheritance encoding strategies to model deviations from additivity, a custom linkage disequilibrium pruning node that minimizes redundancy among features, and a dynamic variant recommendation system that prioritizes informative candidates for pipeline inclusion. +We evaluate StarBASE-GP on a cohort of \textit{Rattus norvegicus} (brown rat) to identify variants associated with body mass index, benchmarking its performance against a random baseline and a biologically naïve version of the tool. +StarBASE-GP consistently evolves Pareto fronts with superior performance, yielding higher accuracy in identifying both ground truth and novel quantitative trait loci, highlighting relevant targets for future validation. +By incorporating evolutionary search and relevant biological theory into a flexible automated machine learning framework, StarBASE-GP demonstrates robust potential for advancing variant discovery in complex traits. diff --git a/Source/evolver.py b/Source/evolver.py index 1fcf9d1..92cb112 100644 --- a/Source/evolver.py +++ b/Source/evolver.py @@ -1055,10 +1055,14 @@ def plot_pareto_front(self, pop: List[Pipeline]) -> None: plt.clf() # check if ground truth is found in the Pareto front - def check_ground_truth(self) -> bool: + def check_ground_truth(self) -> None: # get the pareto front from the population _, rank = nsga.non_dominated_sorting(obj_scores=self.get_pipeline_scores(self.population, weights=(r2_t(1.0), feature_cnt_t(-1)))) - pareto_front = self.population[rank == 0] + pareto_front = [] + # get rank == 0 pipelines + for i, r in enumerate(rank): + if r == 0: + pareto_front.append(self.population[i]) # get snps from the pareto front that are not prunned good_snps = set() @@ -1073,8 +1077,7 @@ def check_ground_truth(self) -> bool: chrom, pos = self.snp_chrm_pos(snp) if true_chrom == chrom and np.abs(true_pos - pos) <= self.truth_distance: print('Found ground truth SNP:', true_snp, flush=True) - - return + # helper to generate chromosome number and snp position def snp_chrm_pos(self, snp: snp_t) -> Tuple[gen_chrom_num_t, gen_chrom_pos_t]: diff --git a/Source/scikit_node.py b/Source/scikit_node.py index 4821bf9..9fceee3 100644 --- a/Source/scikit_node.py +++ b/Source/scikit_node.py @@ -1133,39 +1133,20 @@ def __init__(self, self.snp_details_after_ld = None + def fit(self, X_original, X_encoded, y, snp_r2_dict): - """ - Fit the feature selector to the data. - - Parameters: - - X (array-like): The input features (2D array of shape [n_samples, n_features]). - - y : The target variable. - - Returns: - - self: The fitted selector. - """ - selected_snps = [] # List to store the selected SNPs - ld_removed_snps = set() # Set to store the SNPs removed due to LD pruning - ld_removed_details = {} # Dictionary to store the details of the SNPs removed due to LD pruning - snp_details_after_ld = {} # Dictionary to store the details of the SNPs after LD pruning - ld_threshold = self.threshold # Threshold for LD pruning - max_distance = self.genomic_distance # Maximum genomic distance in between SNPs to be considered for LD pruning - final_selected_snps = [] # List to store the final selected SNPs after LD pruning and conditional analysis - - if X_original.empty: # If empty list is passed to the LD operator - # set self.selected_features_ to None and return self + if X_original.empty: self.selected_features_ = None return self - # function to remove same groups being checked for LD and also to remove subsets of groups + # Function to calculate LD (R²) between two SNPs + def calculate_ld(X, snp1: np.str_, snp2: np.str_): + correlation = np.corrcoef(X[snp1], X[snp2])[0, 1] + r_squared = correlation ** 2 + return r_squared + + # Function to remove subset groups from a list of groups def remove_subsets(groups): - """ - Removes subsets from a list of SNP groups. - Args: - groups: List of groups, where each group is a list of SNP names (or tuples of chromosome and position). - Returns: - List of unique groups with subsets removed. - """ unique_groups = [] for group in groups: is_subset = False @@ -1177,58 +1158,38 @@ def remove_subsets(groups): unique_groups.append(group) return unique_groups - # function to calculate the correlation coefficient between two SNPs - def calculate_ld(X, snp1: np.str_, snp2: np.str_): # LD coefficient shoud be calculated using original data not reencoded data - # Calculate correlation coefficient (Pearson's r) - correlation = np.corrcoef(X[snp1], X[snp2])[0, 1] - - # Compute R² value - r_squared = correlation ** 2 + ld_threshold = self.threshold + max_distance = self.genomic_distance + final_selected_snps = [] + ld_removed_snps = set() + ld_removed_details = {} + snp_details_after_ld = {} - return r_squared - - # get the column names of the original data which are in numpy array format column_names = X_original.columns - chr,pos = [],[] + chr, pos = [], [] for snp in column_names: chr.append(int(snp.split('.')[0])) pos.append(int(snp.split('.')[1])) - # Create a DataFrame with SNP names, chromosomes, and positions genotype_df_columns = pd.DataFrame({'chrom': chr, 'pos': pos, 'snp': column_names}) - # sort the genotype_df_columns based on chromosome and position genotype_df_columns = genotype_df_columns.sort_values(['chrom', 'pos']) - # get a list of sorted snps sorted_snps = genotype_df_columns['snp'].tolist() - # this dataframe will have the original unencoded data in a sorted order genotype_df_original = X_original[sorted_snps] - # this dataframe will have the encoded data in a sorted order genotype_df_encoded = X_encoded[sorted_snps] - # all the chromosomes in the data chromosomes = genotype_df_columns['chrom'].unique() - # calculate marginal R² values for each SNP - marginal_r2 = {} - # use the snp_r2_dict to get the marginal r2 values - for snp in column_names: - marginal_r2[snp] = snp_r2_dict[snp] - - # set the snp_details_after_ld to True for all the snps - True indicates LD pruned snps + marginal_r2 = {snp: snp_r2_dict[snp] for snp in column_names} for snp in column_names: snp_details_after_ld[snp] = True for chrom in chromosomes: - # list of snps to check for conditional analysis in the current chromosome - snps_to_check_for_ca_in_chr = [] - # get SNPs and their positions for the current chromosome chr_snps_df = genotype_df_columns[genotype_df_columns['chrom'] == chrom] chr_snps = chr_snps_df['snp'].tolist() - # ---------------------- - # Group SNPs by distance - # ---------------------- + groups = [] current_group = [(chr_snps_df.iloc[0]['snp'], chr_snps_df.iloc[0]['pos'])] + for i in range(1, len(chr_snps)): curr_snp = (chr_snps_df.iloc[i]['snp'], chr_snps_df.iloc[i]['pos']) prev_snp = (chr_snps_df.iloc[i - 1]['snp'], chr_snps_df.iloc[i - 1]['pos']) @@ -1238,145 +1199,112 @@ def calculate_ld(X, snp1: np.str_, snp2: np.str_): # LD coefficient shoud be cal groups.append(current_group) current_group = [curr_snp] groups.append(current_group) - # Remove duplicate groups and subsets + groups = remove_subsets(groups) - # Convert groups back to original SNP names groups = [[snp[0] for snp in group] for group in groups] - # -------------------- - # Within-group LD prune - # -------------------- + + # LD Pruning and Conditional Analysis within each group for group in groups: + #print(f"Processing group: {group}") + ld_removed_snps_in_group = set() if len(group) == 1: - final_selected_snps.append(group[0]) - else: - group_df = genotype_df_original[group] - snp_list = group_df.columns.tolist() - # Mark SNPs in high LD - for i, snp1 in enumerate(snp_list): - if snp1 in ld_removed_snps: + snp = group[0] + final_selected_snps.append(snp) + snp_details_after_ld[snp] = "Single SNP in group, retained by default" + continue + group_df = genotype_df_original[group] + snp_list = group_df.columns.tolist() + + for i, snp1 in enumerate(snp_list): + if snp1 in ld_removed_snps: + continue + for j in range(i + 1, len(snp_list)): + snp2 = snp_list[j] + if snp2 in ld_removed_snps: continue - for j in range(i + 1, len(snp_list)): - snp2 = snp_list[j] - if snp2 in ld_removed_snps: - continue - ld_value = calculate_ld(genotype_df_original, snp1, snp2) - if ld_value > ld_threshold: - # Remove the lower marginal R² SNP - if marginal_r2[snp1] > marginal_r2[snp2]: - ld_removed_snps.add(snp2) - ld_removed_details[snp2] = ( - f"Removed due to high LD (R²={ld_value:.3f}) with {snp1}" - ) - else: - ld_removed_snps.add(snp1) - ld_removed_details[snp1] = ( - f"Removed due to high LD (R²={ld_value:.3f}) with {snp2}" - ) - # Collect non-removed SNPs - non_removed = [s for s in snp_list if s not in ld_removed_snps] - snps_to_check_for_ca_in_chr.extend(non_removed) - - # If no SNPs remain in this chromosome, skip - snps_to_check_for_ca_in_chr = list(set(snps_to_check_for_ca_in_chr)) - if len(snps_to_check_for_ca_in_chr) == 0: - continue - - if len(snps_to_check_for_ca_in_chr) == 1: - final_selected_snps.extend(snps_to_check_for_ca_in_chr) - continue - # -------------------------------------- - # Iterative (stepwise) conditional analysis - # -------------------------------------- - snps_remaining = snps_to_check_for_ca_in_chr[:] - final_chr_snps = [] - # we loop until we can't prune any more SNPs - while True: - if len(snps_remaining) < 2: - # Either 0 or 1 SNP left, just add them all and break - final_chr_snps.extend(snps_remaining) - break - # find the next peak SNP (highest marginal R² among snps_remaining) - candidate_r2 = [(snp, marginal_r2[snp]) for snp in snps_remaining] - peak_snp = max(candidate_r2, key=lambda x: x[1])[0] - # perform conditional analysis using ONLY the peak SNP as covariate + ld_value = calculate_ld(genotype_df_original, snp1, snp2) + if ld_value > ld_threshold: + if marginal_r2[snp1] > marginal_r2[snp2]: + ld_removed_snps.add(snp2) + ld_removed_snps_in_group.add(snp2) + ld_removed_details[snp2] = f"Removed due to high LD (R²={ld_value:.3f}) with {snp1}" + else: + ld_removed_snps.add(snp1) + ld_removed_snps_in_group.add(snp1) + ld_removed_details[snp1] = f"Removed due to high LD (R²={ld_value:.3f}) with {snp2}" + + non_pruned_snps_in_group = [s for s in snp_list if s not in ld_removed_snps_in_group] # remaining SNPs after LD pruning + + if len(non_pruned_snps_in_group) == 0: + continue + if len(non_pruned_snps_in_group) == 1: + snp = non_pruned_snps_in_group[0] + final_selected_snps.append(snp) + snp_details_after_ld[snp] = "Single SNP after LD pruning, retained by default" + + continue + + # Start Conditional Analysis + #print(f"Performing Conditional Analysis on group: {non_pruned_snps_in_group}") + snps_remaining_for_ca = non_pruned_snps_in_group[:] + final_group_snps = [] + + + if len(snps_remaining_for_ca) < 2: + final_group_snps.extend(snps_remaining_for_ca) + final_selected_snps.extend(final_group_snps) + for s in snps_remaining_for_ca: + snp_details_after_ld[s] = "Retained by CA due to lack of more SNPs" + # move to next group + continue + + peak_snp = max(snps_remaining_for_ca, key=lambda x: marginal_r2[x]) X_peak = genotype_df_encoded[[peak_snp]] - y = y.reshape(-1, 1) p_values = [] tested_snps = [] - for snp in snps_remaining: + + for snp in snps_remaining_for_ca: if snp == peak_snp: continue X_full = pd.concat([X_peak, genotype_df_encoded[[snp]]], axis=1) - model_full = LinearRegression().fit(X_full, y) - beta_snp = model_full.coef_[-1] - residuals = y - model_full.predict(X_full) + model = LinearRegression().fit(X_full, y) + beta = model.coef_[-1] + residuals = y - model.predict(X_full) sigma_sq = np.sum(residuals**2) / (len(y) - X_full.shape[1]) - X_snp_values = genotype_df_encoded[[snp]].values - std_error = np.sqrt(sigma_sq / np.sum((X_snp_values - X_snp_values.mean())**2)) - wald_stat = beta_snp / std_error + X_vals = genotype_df_encoded[[snp]].values + std_err = np.sqrt(sigma_sq / np.sum((X_vals - X_vals.mean())**2)) + wald_stat = beta / std_err p_val = 2 * (1 - stats.norm.cdf(abs(wald_stat))) p_values.append(p_val) tested_snps.append(snp) - # correct for multiple testing - alpha = 0.05 - # ensure p_values is a 1-dimensional array - if len(p_values) == 0: - continue # or handle this case differently if needed - # convert to 1D array - p_values = np.array(p_values).flatten() + if not p_values: # If no SNPs were tested, break the while loop + break - # handle single p-value explicitly + p_values = np.array(p_values).flatten() if len(p_values) == 1: - # print("Only one p-value provided. Skipping multiple testing correction.") - # decide on how to handle this case - if p_values[0] < alpha: - rejected = [True] - else: - rejected = [False] - pvals_corr = p_values # No correction needed + rejected = [p_values[0] < 0.05] else: - # perform multiple testing correction - rejected, pvals_corr, _, _ = multipletests(p_values, alpha=alpha, method='fdr_bh') - # decide which SNPs to remove - to_remove = {s for s, r in zip(tested_snps, rejected) if not r} - # if we didn't prune anything this round, we're done - if not to_remove: - # add the peak SNP to final list if not already present - if peak_snp not in final_chr_snps: - final_chr_snps.append(peak_snp) - break - # otherwise, remove the pruned SNPs - snps_remaining = [s for s in snps_remaining if s not in to_remove] - # add the peak SNP to final list (it is an independent peak) - if peak_snp not in final_chr_snps: - final_chr_snps.append(peak_snp) - # remove the peak SNP from further consideration so the next iteration - # can find the next peak ignoring this one - snps_remaining.remove(peak_snp) - # loop continues with the updated snps_remaining - # add the final chromosome SNPs to the overall final selection - final_selected_snps.extend(list(set(final_chr_snps))) - - - assert len(final_selected_snps) > 0, "No SNPs were selected by the LDSelector" - - # setting the name of the selected feature to be used during evolution - if len(final_selected_snps) == 1: - self.name_of_selected_features = final_selected_snps[0] - - # change the details in snp_details_after_ld to False for the selected snps - for snp in final_selected_snps: - snp_details_after_ld[snp] = False - - - self.snp_details_after_ld = snp_details_after_ld - - # create a boolean mask for the selected SNPs - boolean_mask = np.isin(column_names, final_selected_snps) - self.bool_mask = boolean_mask - - self.selected_features_ = np.array(final_selected_snps) + rejected, _, _, _ = multipletests(p_values, alpha=0.05, method='fdr_bh') + + to_remove = {s for s, r in zip(tested_snps, rejected) if not r} # if rejected is False, then we remove the SNP + for snp in snps_remaining_for_ca: + if snp not in to_remove and snp != peak_snp: + final_group_snps.append(snp) + snp_details_after_ld[snp] = "Retained by CA" + # Add the peak SNP to the final group regardless of other SNPs + final_group_snps.append(peak_snp) + snp_details_after_ld[peak_snp] = "Peak SNP retained by CA" + + final_selected_snps.extend(final_group_snps) # final SNPs after LD pruning and CA added to the list + + self.final_selected_snps = list(set(final_selected_snps)) + self.name_of_selected_features = list(set(final_selected_snps)) + self.ld_removed_snps = ld_removed_snps + self.ld_removed_details = ld_removed_details + self.bool_mask = genotype_df_original.columns.isin(self.final_selected_snps) + self.snp_details_after_ld = {snp: snp not in self.final_selected_snps for snp in genotype_df_original.columns} + self.selected_features_ = np.array(self.final_selected_snps) return self def transform(self, X): diff --git a/docs/StarBASE_logo-1.png b/docs/StarBASE_logo-1.png deleted file mode 100644 index 92de68e..0000000 Binary files a/docs/StarBASE_logo-1.png and /dev/null differ diff --git a/docs/StarBASE_logo.pdf b/docs/StarBASE_logo.pdf deleted file mode 100644 index b13d46d..0000000 Binary files a/docs/StarBASE_logo.pdf and /dev/null differ diff --git a/docs/StarBASE_logo_small.png b/docs/StarBASE_logo_small.png deleted file mode 100644 index 187afa9..0000000 Binary files a/docs/StarBASE_logo_small.png and /dev/null differ diff --git a/docs/starbase-gp-logo.png b/docs/starbase-gp-logo.png new file mode 100644 index 0000000..5607d83 Binary files /dev/null and b/docs/starbase-gp-logo.png differ