score_markers_summary.hpp

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#ifndef SCRAN_SCORE_MARKERS_HPP
#define SCRAN_SCORE_MARKERS_HPP
 
#include "scran_blocks/scran_blocks.hpp"
#include "tatami/tatami.hpp"
#include "sanisizer/sanisizer.hpp"
#include "topicks/topicks.hpp"
#include "quickstats/quickstats.hpp"
 
#include <array>
#include <map>
#include <vector>
#include <optional>
 
#include "scan_matrix.hpp"
#include "cohens_d.hpp"
#include "simple_diff.hpp"
#include "block_averages.hpp"
#include "summarize_comparisons.hpp"
#include "average_group_stats.hpp"
#include "create_combinations.hpp"
#include "utils.hpp"
 
namespace scran_markers {
 
struct ScoreMarkersSummaryOptions {
    double threshold = 0;
 
    int num_threads = 1;
 
    bool compute_group_mean = true;
 
    bool compute_group_detected = true;
 
    bool compute_cohens_d = true;
 
    bool compute_auc = true;
 
    bool compute_delta_mean = true;
 
    bool compute_delta_detected = true;
 
    bool compute_summary_min = true;
 
    bool compute_summary_mean = true;
 
    bool compute_summary_median = true;
 
    bool compute_summary_max = true;
 
    std::optional<std::vector<double> > compute_summary_quantiles;
 
    bool compute_summary_min_rank = true;
 
    std::size_t min_rank_limit = 500;
 
    bool min_rank_preserve_ties = false;
 
    BlockAveragePolicy block_average_policy = BlockAveragePolicy::MEAN;
 
    scran_blocks::WeightPolicy block_weight_policy = scran_blocks::WeightPolicy::VARIABLE;
 
    scran_blocks::VariableWeightParameters variable_block_weight_parameters;
 
    double block_quantile = 0.5;
};
 
template<typename Stat_, typename Rank_>
struct ScoreMarkersSummaryBuffers {
    std::vector<Stat_*> mean;
 
    std::vector<Stat_*> detected;
 
    std::vector<SummaryBuffers<Stat_, Rank_> > cohens_d;
 
    std::vector<SummaryBuffers<Stat_, Rank_> > auc;
 
    std::vector<SummaryBuffers<Stat_, Rank_> > delta_mean;
 
    std::vector<SummaryBuffers<Stat_, Rank_> > delta_detected;
};
 
namespace internal {
 
// Inner vector is optional as we might not need it if SummaryBuffers::min_rank=NULL for a group.
template<typename Stat_, typename Index_>
using MinrankTopQueues = std::vector<std::optional<std::vector<topicks::TopQueue<Stat_, Index_> > > >;
 
template<typename Stat_, typename Index_, typename Rank_>
void preallocate_minrank_queues( 
    const std::size_t num_groups,
    MinrankTopQueues<Stat_, Index_>& queue,
    const std::vector<SummaryBuffers<Stat_, Rank_> >& summaries,
    const Index_ limit,
    const bool keep_ties
) { 
    topicks::TopQueueOptions<Stat_> qopt;
    qopt.keep_ties = keep_ties;
    qopt.check_nan = true;
 
    sanisizer::resize(queue, num_groups);
    for (I<decltype(num_groups)> g1 = 0; g1 < num_groups; ++g1) {
        if (summaries[g1].min_rank == NULL) {
            continue;
        }
        queue[g1].emplace();
 
        auto& g_queue = *(queue[g1]);
        sanisizer::reserve(g_queue, num_groups);
        for (I<decltype(num_groups)> g2 = 0; g2 < num_groups; ++g2) {
            g_queue.emplace_back(limit, true, qopt);
        }
    }
}
 
template<typename Stat_, typename Index_, typename Rank_>
void compute_summary_stats_per_gene(
    const Index_ gene,
    const std::size_t num_groups,
    const Stat_* const pairwise_buffer_ptr,
    std::vector<Stat_>& summary_buffer,
    MaybeMultipleQuantiles<Stat_>& summary_qcalcs,
    MinrankTopQueues<Stat_, Index_>& minrank_queues,
    const std::vector<SummaryBuffers<Stat_, Rank_> >& summaries
) {
    for (I<decltype(num_groups)> gr = 0; gr < num_groups; ++gr) {
        auto& cursummary = summaries[gr];
        const auto in_offset = sanisizer::product_unsafe<std::size_t>(num_groups, gr);
        summarize_comparisons(num_groups, pairwise_buffer_ptr + in_offset, gr, gene, cursummary, summary_qcalcs, summary_buffer);
 
        if (cursummary.min_rank) {
            auto& gr_queue = *(minrank_queues[gr]);
            for (I<decltype(num_groups)> gr2 = 0; gr2 < num_groups; ++gr2) {
                if (gr != gr2) {
                    gr_queue[gr2].emplace(pairwise_buffer_ptr[in_offset + gr2], gene);
                }
            }
        }
    }
}
 
template<typename Stat_, typename Index_, typename Rank_>
void report_minrank_from_queues(
    const Index_ num_genes,
    const std::size_t num_groups,
    std::vector<std::optional<MinrankTopQueues<Stat_, Index_> > >& all_queues,
    const std::vector<SummaryBuffers<Stat_, Rank_> >& summaries,
    const int num_threads,
    const bool keep_ties
) {
    if (all_queues.empty()) {
        // If no queues were populated with ranks, this means that the matrix had no rows at all. 
        // If that's the case, there's no point iterating through the groups.
        // We don't need to fill the min_rank array because num_genes == 0.
        // Thus, we can just return immediately.
        return;
    }
 
    tatami::parallelize([&](const int, const std::size_t start, const std::size_t length) -> void {
        std::vector<Index_> tie_buffer;
 
        for (I<decltype(num_groups)> gr = start, grend = start + length; gr < grend; ++gr) {
            const auto mr_out = summaries[gr].min_rank;
            if (mr_out == NULL) {
                continue;
            }
 
            // Using the maximum possible rank (i.e., 'num_genes') as the default.
            const auto maxrank_placeholder = sanisizer::cast<Rank_>(num_genes);
            std::fill_n(mr_out, num_genes, maxrank_placeholder);
 
            auto& first_mr_queue = *(all_queues.front());
            for (I<decltype(num_groups)> gr2 = 0; gr2 < num_groups; ++gr2) {
                if (gr == gr2) {
                    continue;
                }
 
                // Moving contents into a thread-local variable to minimize false sharing,
                // at least while we repeatedly update the queue within this thread.
                auto current_out = std::move((*(first_mr_queue[gr]))[gr2]);
 
                const auto num_queues = all_queues.size();
                for (I<decltype(num_queues)> q = 1; q < num_queues; ++q) {
                    auto& current_mr_queue = *(all_queues[q]);
                    auto current_in = std::move((*(current_mr_queue[gr]))[gr2]); // moving it to minimize false sharing.
                    while (!current_in.empty()) {
                        current_out.push(current_in.top());
                        current_in.pop();
                    }
                }
 
                // Cast to Rank_ is safe as current_out.size() <= num_genes,
                // and we already checked that num_genes can fit into Rank_ in report_minrank_from_current_outs().
                if (!keep_ties) {
                    while (!current_out.empty()) {
                        auto& mr = mr_out[current_out.top().second];
                        mr = std::min(mr, static_cast<Rank_>(current_out.size()));
                        current_out.pop();
                    }
                } else {
                    while (!current_out.empty()) {
                        tie_buffer.clear();
                        const auto curtop = current_out.top();
                        current_out.pop();
 
                        while (!current_out.empty() && current_out.top().first == curtop.first) {
                            tie_buffer.push_back(current_out.top().second);
                            current_out.pop();
                        }
 
                        // Increment is safe as we already reduced the size at least once.
                        const Rank_ tied_rank = current_out.size() + 1;
 
                        mr_out[curtop.second] = std::min(mr_out[curtop.second], tied_rank);
                        for (const auto t : tie_buffer) {
                            mr_out[t] = std::min(mr_out[t], tied_rank);
                        }
                    }
                }
            }
        }
    }, num_groups, num_threads);
}
 
template<typename Index_, typename Stat_, typename Rank_>
void process_simple_summary_effects(
    const Index_ num_genes,
    const std::size_t num_groups,
    const std::size_t num_blocks,
    const std::size_t num_combos,
    const std::vector<Stat_>& combo_means,
    const std::vector<Stat_>& combo_vars,
    const std::vector<Stat_>& combo_detected,
    const double threshold,
    const BlockAverageInfo<Stat_>& average_info,
    const std::optional<std::vector<double> >& summary_quantiles,
    const Index_ minrank_limit,
    const bool minrank_keep_ties,
    const ScoreMarkersSummaryBuffers<Stat_, Rank_>& output,
    const int num_threads
) {
    std::optional<std::vector<std::optional<MinrankTopQueues<Stat_, Index_> > > > cohens_d_minrank_all_queues, delta_mean_minrank_all_queues, delta_detected_minrank_all_queues;
    if (output.cohens_d.size()) {
        cohens_d_minrank_all_queues.emplace(sanisizer::cast<I<decltype(cohens_d_minrank_all_queues->size())> >(num_threads));
    }
    if (output.delta_mean.size()) {
        delta_mean_minrank_all_queues.emplace(sanisizer::cast<I<decltype(delta_mean_minrank_all_queues->size())> >(num_threads));
    }
    if (output.delta_detected.size()) {
        delta_detected_minrank_all_queues.emplace(sanisizer::cast<I<decltype(delta_detected_minrank_all_queues->size())> >(num_threads));
    }
 
    std::optional<std::vector<Stat_> > total_weights_per_group;
    const Stat_* total_weights_ptr = NULL;
    if (average_info.use_mean()) {
        if (!output.mean.empty() || !output.detected.empty()) {
            if (num_blocks > 1) {
                total_weights_per_group = compute_total_weight_per_group(num_groups, num_blocks, average_info.combo_weights().data());
                total_weights_ptr = total_weights_per_group->data();
            } else {
                total_weights_ptr = average_info.combo_weights().data();
            }
        }
    }
 
    std::optional<PrecomputedPairwiseWeights<Stat_> > preweights;
    if (average_info.use_mean()) {
        if (!output.cohens_d.empty() || !output.delta_mean.empty() || !output.delta_detected.empty()) {
            preweights = PrecomputedPairwiseWeights<Stat_>(num_groups, num_blocks, average_info.combo_weights().data());
        }
    }
 
    const auto num_groups2 = sanisizer::product<typename std::vector<Stat_>::size_type>(num_groups, num_groups);
    const auto nused = tatami::parallelize([&](const int t, const Index_ start, const Index_ length) -> void {
        std::vector<Stat_> pairwise_buffer(num_groups2);
        std::vector<Stat_> summary_buffer(num_groups);
        auto summary_qcalcs = setup_multiple_quantiles<Stat_>(summary_quantiles, num_groups);
 
        std::optional<std::vector<Stat_> > qbuffer, qrevbuffer;
        std::optional<quickstats::SingleQuantileVariableNumber<Stat_> > qcalc;
        if (!average_info.use_mean()) {
            qbuffer.emplace();
            qrevbuffer.emplace();
            qcalc.emplace(num_blocks, average_info.quantile());
        }
 
        std::optional<MinrankTopQueues<Stat_, Index_> > cohens_d_minrank_queue, delta_mean_minrank_queue, delta_detected_minrank_queue;
        if (output.cohens_d.size()) {
            cohens_d_minrank_queue.emplace();
            preallocate_minrank_queues(num_groups, *cohens_d_minrank_queue, output.cohens_d, minrank_limit, minrank_keep_ties);
        }
        if (output.delta_mean.size()) {
            delta_mean_minrank_queue.emplace();
            preallocate_minrank_queues(num_groups, *delta_mean_minrank_queue, output.delta_mean, minrank_limit, minrank_keep_ties);
        }
        if (output.delta_detected.size()) {
            delta_detected_minrank_queue.emplace();
            preallocate_minrank_queues(num_groups, *delta_detected_minrank_queue, output.delta_detected, minrank_limit, minrank_keep_ties);
        }
 
        for (Index_ gene = start, end = start + length; gene < end; ++gene) {
            const auto in_offset = sanisizer::product_unsafe<std::size_t>(gene, num_combos);
 
            if (!output.mean.empty()) {
                const auto tmp_means = combo_means.data() + in_offset;
                if (average_info.use_mean()) {
                    average_group_stats_blockmean(gene, num_groups, num_blocks, tmp_means, average_info.combo_weights().data(), total_weights_ptr, output.mean);
                } else {
                    average_group_stats_blockquantile(gene, num_groups, num_blocks, tmp_means, *qbuffer, *qcalc, output.mean);
                }
            }
 
            if (!output.detected.empty()) {
                const auto tmp_detected = combo_detected.data() + in_offset;
                if (average_info.use_mean()) {
                    average_group_stats_blockmean(gene, num_groups, num_blocks, tmp_detected, average_info.combo_weights().data(), total_weights_ptr, output.detected);
                } else {
                    average_group_stats_blockquantile(gene, num_groups, num_blocks, tmp_detected, *qbuffer, *qcalc, output.detected);
                }
            }
 
            if (output.cohens_d.size()) {
                const auto tmp_means = combo_means.data() + in_offset;
                const auto tmp_variances = combo_vars.data() + in_offset;
                if (average_info.use_mean()) {
                    compute_pairwise_cohens_d_blockmean(tmp_means, tmp_variances, num_groups, num_blocks, threshold, *preweights, pairwise_buffer.data());
                } else {
                    compute_pairwise_cohens_d_blockquantile(tmp_means, tmp_variances, num_groups, num_blocks, threshold, *qbuffer, *qrevbuffer, *qcalc, pairwise_buffer.data());
                }
                compute_summary_stats_per_gene(gene, num_groups, pairwise_buffer.data(), summary_buffer, summary_qcalcs, *cohens_d_minrank_queue, output.cohens_d);
            }
 
            if (output.delta_mean.size()) {
                const auto tmp_means = combo_means.data() + in_offset;
                if (average_info.use_mean()) {
                    compute_pairwise_simple_diff_blockmean(tmp_means, num_groups, num_blocks, *preweights, pairwise_buffer.data());
                } else {
                    compute_pairwise_simple_diff_blockquantile(tmp_means, num_groups, num_blocks, *qbuffer, *qcalc, pairwise_buffer.data());
                }
                compute_summary_stats_per_gene(gene, num_groups, pairwise_buffer.data(), summary_buffer, summary_qcalcs, *delta_mean_minrank_queue, output.delta_mean);
            }
 
            if (output.delta_detected.size()) {
                const auto tmp_det = combo_detected.data() + in_offset;
                if (average_info.use_mean()) {
                    compute_pairwise_simple_diff_blockmean(tmp_det, num_groups, num_blocks, *preweights, pairwise_buffer.data());
                } else {
                    compute_pairwise_simple_diff_blockquantile(tmp_det, num_groups, num_blocks, *qbuffer, *qcalc, pairwise_buffer.data());
                }
                compute_summary_stats_per_gene(gene, num_groups, pairwise_buffer.data(), summary_buffer, summary_qcalcs, *delta_detected_minrank_queue, output.delta_detected);
            }
        }
 
        // Only flushing it to the output buffer at the very end to minimize false sharing.
        if (output.cohens_d.size()) {
            (*cohens_d_minrank_all_queues)[t] = std::move(cohens_d_minrank_queue);
        }
        if (output.delta_mean.size()) {
            (*delta_mean_minrank_all_queues)[t] = std::move(delta_mean_minrank_queue);
        }
        if (output.delta_detected.size()) {
            (*delta_detected_minrank_all_queues)[t] = std::move(delta_detected_minrank_queue);
        }
    }, num_genes, num_threads);
 
    if (output.cohens_d.size()) {
        cohens_d_minrank_all_queues->resize(nused);
        report_minrank_from_queues(num_genes, num_groups, *cohens_d_minrank_all_queues, output.cohens_d, num_threads, minrank_keep_ties);
    }
    if (output.delta_mean.size()) {
        delta_mean_minrank_all_queues->resize(nused);
        report_minrank_from_queues(num_genes, num_groups, *delta_mean_minrank_all_queues, output.delta_mean, num_threads, minrank_keep_ties);
    }
    if (output.delta_detected.size()) {
        delta_detected_minrank_all_queues->resize(nused);
        report_minrank_from_queues(num_genes, num_groups, *delta_detected_minrank_all_queues, output.delta_detected, num_threads, minrank_keep_ties);
    }
}
 
template<
    bool single_block_,
    typename Value_,
    typename Index_,
    typename Group_,
    typename Block_,
    typename Stat_,
    typename Rank_
>
void score_markers_summary(
    const tatami::Matrix<Value_, Index_>& matrix, 
    const Group_* const group, 
    const std::size_t num_groups,
    const Block_* const block,
    const std::size_t num_blocks,
    const std::size_t* const combo,
    const std::size_t num_combos,
    const std::vector<Index_>& combo_sizes,
    const ScoreMarkersSummaryOptions& options,
    const ScoreMarkersSummaryBuffers<Stat_, Rank_>& output
) {
    const auto num_genes = matrix.nrow();
    const auto payload_size = sanisizer::product<typename std::vector<Stat_>::size_type>(num_genes, num_combos);
    std::vector<Stat_> combo_means, combo_vars, combo_detected;
    if (!output.mean.empty() || !output.cohens_d.empty() || !output.delta_mean.empty()) {
        combo_means.resize(payload_size);
    }
    if (!output.cohens_d.empty()) {
        combo_vars.resize(payload_size);
    }
    if (!output.detected.empty() || !output.delta_detected.empty()) {
        combo_detected.resize(payload_size);
    }
 
    // For a single block, this usually doesn't really matter, but we do it for consistency with the multi-block case,
    // and to account for variable weighting where non-zero block sizes get zero weight.
    BlockAverageInfo<Stat_> average_info;
    if (options.block_average_policy == BlockAveragePolicy::MEAN) {
        average_info = BlockAverageInfo<Stat_>(
            scran_blocks::compute_weights<Stat_>(
                combo_sizes,
                options.block_weight_policy,
                options.variable_block_weight_parameters
            )
        );
    } else {
        average_info = BlockAverageInfo<Stat_>(options.block_quantile);
    }
 
    const Index_ minrank_limit = sanisizer::cap<Index_>(options.min_rank_limit);
    internal::validate_quantiles(options.compute_summary_quantiles);
 
    if (!output.auc.empty()) {
        auto auc_minrank_all_queues = sanisizer::create<std::vector<std::optional<MinrankTopQueues<Stat_, Index_> > > >(options.num_threads);
 
        struct AucResultWorkspace {
            AucResultWorkspace(const std::size_t num_groups, const std::optional<std::vector<double> >& summary_quantiles) :
                pairwise_buffer(sanisizer::product<typename std::vector<Stat_>::size_type>(num_groups, num_groups)),
                summary_buffer(sanisizer::cast<typename std::vector<Stat_>::size_type>(num_groups)),
                summary_qcalcs(setup_multiple_quantiles<Stat_>(summary_quantiles, num_groups))
            {};
 
        public:
            std::vector<Stat_> pairwise_buffer;
            std::vector<Stat_> summary_buffer;
            MaybeMultipleQuantiles<Stat_> summary_qcalcs;
            MinrankTopQueues<Stat_, Index_> queue;
        };
 
        const auto num_used = scan_matrix_by_row_custom_auc<single_block_>(
            matrix, 
            group,
            num_groups,
            block,
            num_blocks,
            combo,
            num_combos,
            combo_sizes,
            average_info,
            combo_means,
            combo_vars,
            combo_detected,
            /* do_auc = */ true,
            /* auc_result_initialize = */ [&](const int) -> AucResultWorkspace {
                AucResultWorkspace res_work(num_groups, options.compute_summary_quantiles);
                preallocate_minrank_queues(num_groups, res_work.queue, output.auc, minrank_limit, options.min_rank_preserve_ties);
                return res_work;
            },
            /* auc_result_process = */ [&](const Index_ gene, AucScanWorkspace<Value_, Group_, Stat_, Index_>& auc_work, AucResultWorkspace& res_work) -> void {
                process_auc_for_rows(auc_work, num_groups, num_blocks, options.threshold, res_work.pairwise_buffer.data());
                compute_summary_stats_per_gene(gene, num_groups, res_work.pairwise_buffer.data(), res_work.summary_buffer, res_work.summary_qcalcs, res_work.queue, output.auc);
            },
            /* auc_result_finalize = */ [&](const int t, AucResultWorkspace& res_work) -> void {
                auc_minrank_all_queues[t] = std::move(res_work.queue);
            },
            options.num_threads
        );
 
        auc_minrank_all_queues.resize(num_used);
        report_minrank_from_queues(num_genes, num_groups, auc_minrank_all_queues, output.auc, options.num_threads, options.min_rank_preserve_ties);
 
    } else if (matrix.prefer_rows()) {
        scan_matrix_by_row_full_auc<single_block_>(
            matrix, 
            group,
            num_groups,
            block,
            num_blocks,
            combo,
            num_combos,
            combo_sizes,
            average_info,
            combo_means,
            combo_vars,
            combo_detected,
            static_cast<Stat_*>(NULL),
            options.threshold,
            options.num_threads
        );
 
    } else {
        scan_matrix_by_column(
            matrix,
            [&]{
                if constexpr(single_block_) {
                    return group;
                } else {
                    return combo;
                }
            }(),
            [&]{
                if constexpr(single_block_) {
                    return num_groups;
                } else {
                    return num_combos;
                }
            }(),
            combo_sizes,
            combo_means,
            combo_vars,
            combo_detected,
            options.num_threads
        );
    }
 
    process_simple_summary_effects(
        matrix.nrow(),
        num_groups,
        num_blocks,
        num_combos,
        combo_means,
        combo_vars,
        combo_detected,
        options.threshold,
        average_info,
        options.compute_summary_quantiles,
        minrank_limit,
        options.min_rank_preserve_ties,
        output,
        options.num_threads
    );
}
 
}
template<typename Value_, typename Index_, typename Group_, typename Stat_, typename Rank_>
void score_markers_summary(
    const tatami::Matrix<Value_, Index_>& matrix, 
    const Group_* const group, 
    const std::size_t num_groups,
    const ScoreMarkersSummaryOptions& options,
    const ScoreMarkersSummaryBuffers<Stat_, Rank_>& output
) {
    const auto group_sizes = tabulate_groups(matrix.ncol(), group, num_groups); 
    internal::score_markers_summary<true>(
        matrix,
        group,
        num_groups,
        static_cast<int*>(NULL),
        1,
        static_cast<std::size_t*>(NULL),
        num_groups,
        group_sizes,
        options,
        output
    );
}
 
template<typename Value_, typename Index_, typename Group_, typename Block_, typename Stat_, typename Rank_>
void score_markers_summary_blocked(
    const tatami::Matrix<Value_, Index_>& matrix, 
    const Group_* const group, 
    const std::size_t num_groups,
    const Block_* const block,
    const std::size_t num_blocks,
    const ScoreMarkersSummaryOptions& options,
    const ScoreMarkersSummaryBuffers<Stat_, Rank_>& output
) {
    const auto combo_out = create_combinations(matrix.ncol(), group, num_groups, block, num_blocks);
    internal::score_markers_summary<false>(
        matrix,
        group,
        num_groups,
        block,
        num_blocks,
        combo_out.combinations.data(),
        combo_out.num_combinations,
        combo_out.frequencies,
        options,
        output
    );
}
 
template<typename Stat_, typename Rank_>
struct ScoreMarkersSummaryResults {
    std::vector<std::vector<Stat_> > mean;
 
    std::vector<std::vector<Stat_> > detected;
 
    std::vector<SummaryResults<Stat_, Rank_> > cohens_d;
 
    std::vector<SummaryResults<Stat_, Rank_> > auc;
 
    std::vector<SummaryResults<Stat_, Rank_> > delta_mean;
 
    std::vector<SummaryResults<Stat_, Rank_> > delta_detected;
};
 
template<typename Index_, typename Stat_, typename Rank_>
ScoreMarkersSummaryBuffers<Stat_, Rank_> preallocate_summary_results(
    const Index_ num_genes,
    const std::size_t num_groups,
    ScoreMarkersSummaryResults<Stat_, Rank_>& store,
    const ScoreMarkersSummaryOptions& options
) {
    ScoreMarkersSummaryBuffers<Stat_, Rank_> output;
 
    if (options.compute_group_mean) { 
        internal::preallocate_average_results(num_genes, num_groups, store.mean, output.mean);
    }
 
    if (options.compute_group_detected) { 
        internal::preallocate_average_results(num_genes, num_groups, store.detected, output.detected);
    }
 
    if (options.compute_cohens_d) {
        output.cohens_d = internal::fill_summary_results(
            num_genes,
            num_groups,
            store.cohens_d,
            options.compute_summary_min,
            options.compute_summary_mean,
            options.compute_summary_median,
            options.compute_summary_max,
            options.compute_summary_quantiles,
            options.compute_summary_min_rank
        );
    }
 
    if (options.compute_auc) {
        output.auc = internal::fill_summary_results(
            num_genes,
            num_groups,
            store.auc,
            options.compute_summary_min,
            options.compute_summary_mean,
            options.compute_summary_median,
            options.compute_summary_max,
            options.compute_summary_quantiles,
            options.compute_summary_min_rank
        );
    }
 
    if (options.compute_delta_mean) {
        output.delta_mean = internal::fill_summary_results(
            num_genes,
            num_groups,
            store.delta_mean,
            options.compute_summary_min,
            options.compute_summary_mean,
            options.compute_summary_median,
            options.compute_summary_max,
            options.compute_summary_quantiles,
            options.compute_summary_min_rank
        );
    }
 
    if (options.compute_delta_detected) {
        output.delta_detected = internal::fill_summary_results(
            num_genes,
            num_groups,
            store.delta_detected,
            options.compute_summary_min,
            options.compute_summary_mean,
            options.compute_summary_median,
            options.compute_summary_max,
            options.compute_summary_quantiles,
            options.compute_summary_min_rank
        );
    }
 
    return output;
}
template<typename Stat_ = double, typename Rank_ = int, typename Value_, typename Index_, typename Group_>
ScoreMarkersSummaryResults<Stat_, Rank_> score_markers_summary(
    const tatami::Matrix<Value_, Index_>& matrix,
    const Group_* const group,
    const std::size_t num_groups,
    const ScoreMarkersSummaryOptions& options
) {
    ScoreMarkersSummaryResults<Stat_, Rank_> output;
    const auto buffers = preallocate_summary_results(matrix.nrow(), num_groups, output, options);
    score_markers_summary(matrix, group, num_groups, options, buffers);
    return output;
}
 
template<typename Stat_ = double, typename Rank_ = int, typename Value_, typename Index_, typename Group_, typename Block_>
ScoreMarkersSummaryResults<Stat_, Rank_> score_markers_summary_blocked(
    const tatami::Matrix<Value_, Index_>& matrix,
    const Group_* const group,
    const std::size_t num_groups,
    const Block_* const block,
    const std::size_t num_blocks,
    const ScoreMarkersSummaryOptions& options
) {
    ScoreMarkersSummaryResults<Stat_, Rank_> output;
    const auto buffers = preallocate_summary_results(matrix.nrow(), num_groups, output, options);
    score_markers_summary_blocked(matrix, group, num_groups, block, num_blocks, options, buffers);
    return output;
}
 
}
 
#endif