function [ ranked_list_genes, ranked_scores ] = prioritization( samples, F, F_opp, network_genes, choose_up_down_flag, rank_method_str)

% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

%   INPUTS:

%   samples:                cell array of samples, contains the DNA changes and differentially expressed genes of each sample

%   F:                      diffused network (insulated heat diffusion).

%   F_opp:                  diffused network opposite direction (insulated heat diffusion).

%   network_genes:          the names of all the network genes in the same order they appear in the adjacency matrix.

%   choose_up_down_flag:    1 diffuse only from mutations, 2 diffuse only from differentially expressed genes, 3 diffuse from both.

%   rank_method_str:        'MEDIAN' uses the median of the sample-specific ranks.

%                           'RRA' uses the Robust Rank Aggregation method to integrate sample-specific ranked lists.

%                           'SUM' uses the summation of the sample-specific ranks.

% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

%   OUTPUTS:

%   ranked_list_genes:      list with the ranked gene names

%   ranked_scores:          list with the scores of the ranked gene names

% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

    weights_all = zeros(length(samples.DNA_network),length(network_genes));

    positions_sorted = zeros(length(network_genes),length(samples.DNA_network));

    [final_weights_median,final_weights_sum,final_weights_rho] = deal(zeros(1,length(network_genes)));

    for i = 1:length(samples.DNA_network), %number of samples

        weights_all(i,:) = diffuse_all(choose_up_down_flag, samples.DNA_network{i}, samples.RNA_network{i}, network_genes, F, F_opp);

        to_sort_sorted = sortrows([weights_all(i,:) ;1:length(weights_all(i,:))]');

        positions_sorted(:,i) = fliplr(to_sort_sorted(:,2)')';      

    end

    for j = 1:length(network_genes), % number of genes

        rank = zeros(length(samples.DNA_network),1);

        for i = 1:length(samples.DNA_network), % number of samples

            rank(i) = find(positions_sorted(:,i) == j);

        end

        final_weights_median(j) = median(rank);

        final_weights_sum(j) = sum(rank);

        final_weights_rho(j) = rhoScores(rank/max(rank));    

    end

    %choose the aggregation method 

    if strcmp(rank_method_str, 'MEDIAN'),

        scores = final_weights_median;

    elseif strcmp(rank_method_str, 'SUM'),

        scores = final_weights_sum;     

    else

        scores = final_weights_rho;

    end

    %sort scores

    to_sort_sorted_rho = sortrows([scores ;1:length(scores)]');

    ranked_list_genes = network_genes(to_sort_sorted_rho(:,2));

    ranked_scores = scores(to_sort_sorted_rho(:,2));

end

function [ret_weights_all] = diffuse_all(choose_up_down_flag, sample_DNA, sample_RNA, network_genes, F, F_opp)

    %diffuse mutation labels

    if choose_up_down_flag == 1 || choose_up_down_flag == 3,

        mutation_weights = diffuse(intersect(sample_DNA,network_genes), network_genes, F);

    end

    %diffuse DE labels  

    if choose_up_down_flag == 2 || choose_up_down_flag == 3,

        DE_weights = diffuse(intersect(sample_RNA,network_genes), network_genes, F_opp);

    end

    %combines scores by multiplication (use both mutations and DE)

    if choose_up_down_flag == 3,

        ret_weights_all = mutation_weights.*DE_weights;

    elseif choose_up_down_flag == 1,

        ret_weights_all = mutation_weights;

    else

        ret_weights_all = DE_weights;

    end

end 

function [mutation_weights] = diffuse(inter_mut_sampl_network, network_genes, F)

    positions = find(ismember(network_genes,inter_mut_sampl_network));

    mutation_weights= (1/length(positions))*ones(1,length(positions))*F(positions,:);

end