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```
#' Clonality
#'
#' Creates a data frame giving the total number of sequences, number of unique
#' productive sequences, number of genomes, entropy, clonality, Gini
#' coefficient, and the frequency (\%) of the top productive sequences in a list
#' of sample data frames.
#'
#' @param file.list A list of data frames consisting of antigen receptor
#' sequencing imported by the LymphoSeq function readImmunoSeq. "aminoAcid", "count",
#' and "frequencyCount" are required columns. "estimatedNumberGenomes" is optional.
#' Note that clonality is usually calculated from productive nucleotide sequences.
#' Therefore, it is not recommended to run this function using a productive sequence
#' list aggregated by amino acids.
#' @return Returns a data frame giving the total number of sequences, number of
#' unique productive sequences, number of genomes, clonality, Gini coefficient,
#' and the frequency (\%) of the top productive sequence in each sample.
#' @details Clonality is derived from the Shannon entropy, which is calculated
#' from the frequencies of all productive sequences divided by the logarithm of
#' the total number of unique productive sequences. This normalized entropy
#' value is then inverted (1 - normalized entropy) to produce the clonality
#' metric.
#'
#' The Gini coefficient is an alternative metric used to calculate repertoire
#' diversity and is derived from the Lorenz curve. The Lorenz curve is drawn
#' such that x-axis represents the cumulative percentage of unique sequences and
#' the y-axis represents the cumulative percentage of reads. A line passing
#' through the origin with a slope of 1 reflects equal frequencies of all clones.
#' The Gini coefficient is the ratio of the area between the line of equality
#' and the observed Lorenz curve over the total area under the line of equality.
#' Both Gini coefficient and clonality are reported on a scale from 0 to 1 where
#' 0 indicates all sequences have the same frequency and 1 indicates the
#' repertoire is dominated by a single sequence.
#' @examples
#' file.path <- system.file("extdata", "TCRB_sequencing", package = "LymphoSeq")
#'
#' file.list <- readImmunoSeq(path = file.path)
#'
#' clonality(file.list = file.list)
#' @seealso \code{\link{lorenzCurve}}
#' @export
#' @importFrom ineq Gini
clonality <- function(file.list) {
table <- data.frame(samples = names(file.list))
i <- 1
for (i in 1:length(file.list)) {
file <- file.list[[i]]
total.reads <- nrow(file)
total.count <- sum(file$count)
productive <- file[!grepl("\\*", file$aminoAcid) & file$aminoAcid != "", ]
frequency <- productive$count/sum(productive$count)
entropy <- -sum(frequency * log2(frequency), na.rm = TRUE)
unique.productive <- nrow(productive)
clonality <- 1 - round(entropy/log2(unique.productive), digits = 6)
table$totalSequences[i] <- total.reads
table$uniqueProductiveSequences[i] <- unique.productive
table$totalCount[i] <- total.count
table$clonality[i] <- clonality
table$giniCoefficient[i] <- ineq::Gini(frequency)
table$topProductiveSequence[i] <- max(frequency) * 100
if (any(grepl("estimatedNumberGenomes", colnames(file)))) {
file$estimatedNumberGenomes <- suppressWarnings(as.integer(file$estimatedNumberGenomes))
total.genomes <- sum(file$estimatedNumberGenomes)
table$totalGenomes[i] <- ifelse(total.genomes == 0, NA, total.genomes)
} else {
table$totalGenomes[i] <- NA
}
}
table <- table[order(table$topProductiveSequence, decreasing = TRUE), ]
rownames(table) <- NULL
return(table)
}
```

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