Structures.h

#ifndef DEF_STRUCTURES
#define DEF_STRUCTURES
#include <vector>
#include <map>


struct Cell{
    std::vector<int> ref_counts; // number of ref reads for each variable locus
    std::vector<int> alt_counts; // number of alt reads for each variable locus
    std::vector<int> region_counts; // number of reads in each region
    std::vector<int> genotypes;
    std::vector<int> GQ;
    std::string name;
    int total_counts; // sum of the read counts in each region
};

struct Data{
    std::string sex; // "male" (1 chr X) or "female" (2 chr X)
    std::vector<std::string> locus_to_chromosome;
    std::vector<int> locus_to_position;
    std::vector<std::string> locus_to_reference;
    std::vector<std::string> locus_to_alternative;
    std::vector<bool> variant_is_SNV; // true if the variant is a SNV, false otherwise (indel)
    std::vector<double> locus_to_freq; // frequency of the variant in the population
    std::vector<std::string> locus_to_name; //name of the variant, which describes the effect on the protein (if any)
    std::vector<int> locus_to_region;
    
    std::vector<std::vector<int>> region_to_loci; //list of loci on this amplicon (possibly empty)
    std::vector<std::string> region_to_chromosome;
    std::vector<std::string> region_to_name;
    std::vector<bool> region_is_reliable; // true for the amplicons that are used for computing the CNV score.
    std::vector<double> predetermined_region_weights; // probability for a read to fall in each of the regions, when these values are given as input and not inferred (otherwise they will be inferred by using the cells attached at the root)
};

struct Params{
    double sequencing_error_rate;
    double omega_hom; //concentration parameter for beta-binomial in the SNV likelihood, when homozygous
    double omega_het; //concentration parameter for beta-binomial in the SNV likelihood, when heterozygous

    double sequencing_error_rate_indel; // more errors for indels than SNVs
    double omega_hom_indel;
    double omega_het_indel;

    double prior_dropoutrate_mean; // beta prior for the dropout rates. the mean is alpha/(alpha+beta)
    double prior_dropoutrate_omega; // beta prior for the dropout rates. the inverse overdispersion parameter omega is alpha+beta

    double theta; // inverse dispersion of the negative binomial for the CNV likelihood

    double doublet_rate;
    
    bool use_doublets;
    bool filter_regions;
    bool filter_regions_CNLOH;
    bool verbose;

    // Penalties in the tree prior
    double node_cost;
    double CNA_cost;
    double LOH_cost;
    double mut_notAtRoot_cost;
    double mut_notAtRoot_freq_cost;
};

#endif