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```
#' Calculate association scores between alpha and beta chain pairs.
#'
#' \code{chain_scores()} calculates association scores between every pair of
#' alpha and beta chains based on the number of concurrent well appearances
#' each alpha and beta pair makes, scaled inversely by the number of unique
#' chains in that well. See Lee et. al. for more information about this
#' procedure.
#'
#' @param data_a Matrix recording which alpha chains appear in each well of the
#' data. See \code{\link{create_clones}}.
#' @param data_b Matrix recording which beta chains appear in the each well of the
#' data. See \code{\link{create_clones}}.
#'
#' @return A list containing the alpha and beta association scores. Accessed
#' with \code{list$ascores} and \code{list$bscores} respectively.
#'
#' @examples
#' # see the help for create_clones() and create_data()
#' clones <- create_clones(numb_beta = 1000,
#' dual_alpha = .3,
#' dual_beta = .06,
#' alpha_sharing = c(0.80, 0.15, 0.05),
#' beta_sharing = c(0.75, 0.20, 0.05))
#' dat <- create_data(clones$TCR, plate = 5,
#' error_drop = c(.15, .01),
#' error_seq = c(.05, .001),
#' error_mode = c("lognormal", "lognormal"),
#' skewed = 10,
#' prop_top = 0.6,
#' dist = "linear",
#' numb_cells = matrix(c(50, 480), ncol = 2))
#'
#' #this is done internally in bagpipe()
#' scores <- chain_scores(data_a = dat$alpha, data_b = dat$beta)
#' scores <- scores$ascores + t(scores$bscores)
#'
#' @export
chain_scores <- function(data_a, data_b) {
# Determining the number of unique alpha and beta chains and number of wells
# Each column of data_a and data_b represents an alpha/beta chain respectively
# Each row is a well; entry (i,j) determines if chain j is present in well i
numb_alph <- ncol(data_a)
numb_beta <- ncol(data_b)
numb_well <- nrow(data_a)
if (numb_well != nrow(data_b)) stop()
# Creating the matrices that will record the association scores between
# each pair of alpha and beta chain. In scores_alph matrix, entry (i,j)
# represents how associated beta_j is associated with alpha_i (analogous
# situation for scores_beta matrix). These two matrices will be added to get
# a composite score (i.e. S_ij = scores_alph[i, j] + scores_beta[j, i])
scores_alph <- matrix(0, nrow = numb_alph, ncol = numb_beta)
scores_beta <- matrix(0, nrow = numb_beta, ncol = numb_alph)
# loop through each well, find which alphas and betas are present, and
# calculate association scores based on concurrent well appearances
for (well in 1:numb_well) {
# find which alphas and betas are in the well
well_alph <- which(data_a[well, ] == 1)
well_beta <- which(data_b[well, ] == 1)
# Each concurrent apperance is scaled by the number of unique partner chains
# found in the well (e.g. for a given beta, each concurrent alpha appearance
# is scaled inversely by the number of unique alphas found in that well)
scale_alph <- length(well_alph)
scale_beta <- length(well_beta)
scores_alph[well_alph, well_beta] <- scores_alph[well_alph, well_beta] +
1/scale_alph
scores_beta[well_beta, well_alph] <- scores_beta[well_beta, well_alph] +
1/scale_beta
} # end for - well
list(ascores = scores_alph, bscores = scores_beta)
} # end function
```

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