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main_nef.m
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main_nef.m
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%%
% *CODE FOR IDENTIFYING CO-EVOLUTIONARY SECTORS IN HIV NEF USING RoCA*
%% Setting up paths (of functions and data files required) and necessary parameters
clear all;close all;clc
% Adding paths of required functions and datafiles
addpath functions
addpath datafiles
% Setting font type and size
set(0,'DefaultAxesFontName','Arial')
set(0,'DefaultTextFontName','Arial')
set(0,'DefaultAxesFontSize',17)
set(0,'DefaultTextFontSize',17)
% Specifying the name of protein, and whether to run the shuffling code
protein = 'Nef';
run_shuffling = 0;
%1 = yes, run the shuffling code,
%0 = no, use saved data.
run_stat_ind_test = 0;
%1 = yes, run the statistical independence test code,
%0 = no, use saved data.
%% Loading biochemical domains
biodomain = biochemical_domains(protein);
%% Preprocessing the data
load msa_nef
[B,Bcap,lambda,site_freq_no_mutation,true_indices,freq_bin,prev_aa,N,M,ls] = preprocessing_nef(msa);
%% Computing the number of significant eigenvectors, alpha
if run_shuffling == 1
N_shuffles = 1e5; %Number of shuffles
thresh = 1; %percentile to define threshold for significant positive and negative correlations
[lambda_max_rnd,pos_thresh,neg_thresh] = ...
computing_lambda_max_rnd(Bcap,N_shuffles,thresh);
else
load data_shuffling_nef
end
alpha = 4; %sum(lambda>lambda_max_rnd);
%In case of Nef, alpha is equal to 7. However, we didn't find significance
%of sectors 5 to 7. Thus, we focus only on the first 4 sectors henceforth.
%% Forming sectors using RoCA
% Computing the sparsity threshold, gamma_k for Corr-ITSPCA
gamma_k = computing_gamma_k(alpha,lambda,N,M);
% Computing sparse principal components using Corr-ITSPCA
PC_roca = CorrITSPCA(Bcap, gamma_k, alpha);
% Forming sectors using RoCA
[sec_eig_roca, sec_eig_roca_true, sec_eig_roca_incl_cs, length_sec_roca] = ...
form_sectors_roca(PC_roca,alpha,site_freq_no_mutation,true_indices,ls);
n_secs_roca = length(sec_eig_roca);
%% Forming sectors using PCA
[PC_pca, sec_eig_pca, sec_eig_pca_true, sec_eig_pca_incl_cs, length_sec_pca] = ...
form_sectors_pca(Bcap,alpha,site_freq_no_mutation,true_indices,ls,pos_thresh,neg_thresh);
n_secs_pca = length(sec_eig_pca);
fprintf('\n-----------------------------------------------------------------------------------\n')
fprintf('Number of sectors formed\n')
fprintf('-----------------------------------------------------------------------------------\n')
fprintf('RoCA method = %d\n',n_secs_roca)
fprintf('PCA method = %d\n',n_secs_pca)
%% Computing correlation matrices
C = corrcoef(Bcap); %sample correlation matrix
C_hat = compute_clean_C(Bcap,alpha,lambda); %cleaned standardized correlation matrix
%% Heat maps
heatmap_corr_matrix(C,'eastoutside','Sample correlation matrix')
% Heat map of cleaned correlation matrix (Supplementary figures 1-6)
per_sites_in_roca_sectors = heatmap_corr_matrix_cleaned(C_hat,sec_eig_roca,sec_eig_roca_incl_cs,n_secs_roca,ls,'RoCA');
per_sites_in_pca_sectors = heatmap_corr_matrix_cleaned(C_hat,sec_eig_pca,sec_eig_pca_incl_cs,n_secs_pca,ls,'PCA');
fprintf('\n-----------------------------------------------------------------------------------\n')
fprintf('Total number of sites in inferred RoCA and PCA sectors\n')
fprintf('-----------------------------------------------------------------------------------\n')
fprintf('Percentage of sites in RoCA inferred sectors = %.2f\n',per_sites_in_roca_sectors)
fprintf('Percentage of sites in PCA inferred sectors = %.2f\n',per_sites_in_pca_sectors)
%% Comparison of PCA and RoCA sector sizes
% Supplementary figure
compare_size_sectors(sec_eig_roca_incl_cs,sec_eig_pca_incl_cs,alpha)
%% Location of sectors on primary structure
%(Supplementary figures 1-3)
location_secs_on_primary_structure_protein(sec_eig_roca,sec_eig_pca,true_indices,freq_bin,M)
%% Percentage overlap
%(Supplementary figures 4-6)
calculate_overlap(sec_eig_roca,sec_eig_pca,n_secs_roca,n_secs_pca);
%% Statistics of RoCA sectors
%Supplementary figure
[mc,mean_abs_corr,per_neg_corr,per_pos_corr] = ...
stats_sectors(C_hat,sec_eig_roca,n_secs_roca,freq_bin,pos_thresh,neg_thresh);
%% Biplots of eigenvectors
markersize = 12; %Size of circles
jitter_roca = 0.005; %Random jitter to avoid super-imposition of data points
jitter_pca = 0;
%(Supplementary figures 1-3)
generate_biplots(PC_roca,sec_eig_roca,alpha,markersize,jitter_roca,'RoCA');
%(Supplementary figures 4-6)
generate_biplots(PC_pca,sec_eig_pca,alpha,markersize,jitter_pca,'PCA');
%% NED calculation plot
%(Supplementary figures 1-3)
if run_stat_ind_test == 1
[NED_abs_random,NED_abs_inter,NED_abs_intra] = calculate_NED(B,PC_roca,alpha);
figure_NED(alpha,NED_abs_random,NED_abs_inter,NED_abs_intra)
else
load data_NED_nef
figure_NED(alpha,NED_abs_random,NED_abs_inter,NED_abs_intra)
end
%% Statistical signficance of biochemical association of RoCA sectors
%Fig. 4
sec_asso_roca = zeros(1,length(biodomain)); %sector associated to a particular biochemical domain
pvalue_asso_roca = zeros(1,length(biodomain)); %statistical significance of the sector-biochemical domain association
sec_asso_pca = zeros(1,length(biodomain)); %sector associated to a particular biochemical domain
pvalue_asso_pca = zeros(1,length(biodomain)); %statistical significance of the sector-biochemical domain association
for kk = 1:length(biodomain)
[sec_asso_roca(kk),pvalue_asso_roca(kk)] = ...
compute_association(biodomain(kk).sites,sec_eig_roca_incl_cs,sec_eig_roca_true,ls,M);
[sec_asso_pca(kk),pvalue_asso_pca(kk)] = ...
compute_association(biodomain(kk).sites,sec_eig_pca_incl_cs,sec_eig_pca_true,ls,M);
end
fprintf('\n-----------------------------------------------------------------------------------\n')
fprintf('Significance of inferred %s sectors using RoCA\n',protein)
fprintf('-----------------------------------------------------------------------------------\n')
for kk = 1:length(biodomain)
fprintf('Sector %d is associated with %s (P = %.2e).\n',...
sec_asso_roca(kk),biodomain(kk).name,pvalue_asso_roca(kk));
end
fprintf('\n-----------------------------------------------------------------------------------\n')
fprintf('Significance of inferred %s sectors using PCA\n',protein)
fprintf('-----------------------------------------------------------------------------------\n')
for kk = 1:length(biodomain)
fprintf('Sector %d is associated with %s (P = %.2e).\n',...
sec_asso_pca(kk),biodomain(kk).name,pvalue_asso_pca(kk));
end
%% Data for red spheres in protein crystal structure figures
sec_sites_in_biodomain = cell(1,length(biodomain));
%(Supplementary figures)
for kk = 1:length(biodomain)
sec_sites_in_biodomain{kk} = biodomain(kk).sites(ismember(biodomain(kk).sites,sec_eig_roca_incl_cs{sec_asso_roca(kk)}));
end