R/cosine_pairs.R

In Swarchal/phenoDist: Distance Measurements in Phenotypic Screening

#' Calculate pairs of cosine similarities between replicates
#'
#' Given a list, with an element per compound, \code{cols}
#' are the elements of the vector with which to calculate the cosine
#' similarity
#' @param x list
#' @param cols integer, column indices
#'
#' @return dataframe of compound combinations across replicates with a column 
#'    of cosine similarity values
#'
#' @export
#'
#' @examples
#' cmpds <- c(rep('a', 100), rep('b', 100), rep('c', 100))
#' replicate <- rep(1:100, 3)
#' PC1 <- rnorm(300)
#' PC2 <- rnorm(300)
#' 
#' df <- data.frame(cmpds, replicate, PC1, PC2)
#' 
#' df_split <- split(df, df$cmpds)
#' 
#' # works with unequal replicate sizes
#' df_split$a <- df_split$a[-c(1:10), ]
#' 
#' cosine_pairs(df_split, 3:4)


cosine_pairs <- function(x, cols){
          
    if (!is.list(x) || is.data.frame(x)){
	stop("Expecting a list", call. = FALSE)
    }
  
    # initialise empty vectors
    vals <- numeric()
    A <- character()
    B <- character()
  
    # get pairs of compounds
    name <- names(x)
    pairs_names <- t(combn(name, 2))
  
    for (i in 1:nrow(pairs_names)){
	tmp1 <- x[[pairs_names[i, 1]]]
	tmp2 <- x[[pairs_names[i, 2]]]
    
    # loop through rows in cmpd A and cmpd B
    # calculate the cosine similarity between the two vectors
    for (j in 1:nrow(tmp1)){
	for (k in 1:nrow(tmp2)){
	    vals <- c(vals,
	              cosine_sim_vector(tmp1[j, cols], tmp2[k, cols]))
	
	    A <- c(A, pairs_names[i, 1])
	    B <- c(B, pairs_names[i, 2])
      }
    }
  }
    data.frame(A, B, vals)
}