# Uses read counts for biallelic SNPs to determine if a sample is diploid or triploid `tripsAndDip` calculates log-likelihood ratios comparing whether a sample is likely diploid or triploid based on the read counts for biallelic SNPs. ```r tripsAndDip(counts, counts_alt = NA, h, eps, min_reads = 30, min_loci = 15, binom_p_value = 0.05) ``` ## Arguments - `counts`: Either a numeric matrix or a dataframe with each row corresponding to a different sample. There are two options for formatting the input. Either the columns correspond to the read counts for each locus, in a two column per locus format: column 1 is the read counts for locus1ReferenceAllele, column two is the read counts for locus1AlternateAllele2, locus2Reference, locus2Alternate, ... OR this contains read counts for the reference allele, and `counts_alt` contains read counts for the alternate allele The rownames should be the sample names. - `counts_alt`: This is a numeric matrix or a dataframe with each row corresponding to a different sample. The matrix contains counts for the alternate allele, with samples and loci having the same order as in `counts` If this parameter is NA or NULL, `counts` is assumed to have both the reference and alternate allele counts. - `h`: A numeric vector of h values for each locus in the same order that the loci are ordered in counts. These h values are as defined by Gerard et al. (2018) "Genotyping polyploids from messy sequencing data" Genetics 210:789-807. with h expressed as alternate / reference. These values can be estimated using the R package "updog". - `eps`: A numeric vector of values for the error rate per read for each locus in the same order that the loci are ordered in counts. These are expressed as proportions, so a rate of 1% should be given as 0.01. These values can be estimated using the R package "updog". - `min_reads`: The minimum number of reads to consider a locus. - `min_loci`: The minimum number of usable loci in a sample to calculate a log-likelihood ratio. - `binom_p_value`: The alpha value to use when applying a binomial test to determine whether to include a locus in the calculation. ## Returns a dataframe with column 1 containing sample names, column 2 containing calculated LLRs (larger means more likely given triploidy) and column 3 containing the number of loci used to calculate the LLR ## Details `tripsAndDip` calculates log-likelihood ratios comparing the likelihoods of the read counts under diploidy or triploidy for a sample using biallelic SNPs.This function was designed with amplicon sequencing data in mind, but may be useful for other genotyping techniques that also yield read counts for each allele in a given locus. Full details of the calculations can be found in Delomas (2019) Differentiating diploid and triploid individuals using single nucleotide polymorphisms genotyped by amplicon-sequencing. Molecular Ecology Resources. ## Examples ```r # make up some data triploid_allele1 <- rbinom(60, 75, 2/3) triploid_allele2 <- 75 - triploid_allele1 diploid_allele1 <- rbinom(60, 75, 1/2) diploid_allele2 <- 75 - diploid_allele1 # interleave allele counts triploid <- c(rbind(triploid_allele1, triploid_allele2)) diploid <- c(rbind(diploid_allele1, diploid_allele2)) # create counts matrix allele_counts <- matrix(data = c(triploid, diploid), byrow = TRUE, nrow = 2, ncol = 120) rownames(allele_counts) <- c("triploid", "diploid") #create h and eps vectors h_constant <- rep(1, 60) eps_constant <- rep(.01, 60) #run function ploidy <- tripsAndDip(allele_counts, h = h_constant, eps = eps_constant) ```

tripsAndDip function