#' Get GeneticVsGenetic data
#'
#' This function creates a \code{data.frame} suitable for the GeneticVsGenetic plots and shiny app
#'
#' @param con A \code{SQLiteConnection} object to the database
#' @param cell_lines A vector of cell line identifiers
#' @param gene1 The gene symbol of the primary gene
#' @param gene2 The gene symbol of the secondary gene
#' @param data_type1 The data type required for gene1: should be one of \code{'affy', 'cn', 'hybcap', 'cosmicclp'}
#' @param data_type2 The data type required for gene1: should be one of \code{'affy', 'cn', 'hybcap', 'cosmicclp'}
#' @return A \code{data.frame} containing the combined genetic data for the requested cell lines
#' @export
makeGeneticVsGeneticDataFrame <- function(con, cell_lines, gene1, gene2, data_type1, data_type2) {
gene1_data <- makeTallDataFrame(con,
gene1,
cell_lines,
drugs=NULL,
data_type1) %>%
dplyr::transmute(unified_id, gene1=assayed_id, feature_type1=data_type, feature_name1=paste(assayed_id, data_type, sep="_"), feature_value1=value, feature_original1=original)
gene2_data <- makeTallDataFrame(con,
gene2,
cell_lines,
drugs=NULL,
data_type2) %>%
dplyr::transmute(unified_id, gene2=assayed_id, feature_type2=data_type, feature_name2=paste(assayed_id, data_type, sep="_"), feature_value2=value, feature_original2=original)
cls.df <- dplyr::src_sqlite(con@dbname) %>%
dplyr::tbl('ccle_sampleinfo') %>%
dplyr::transmute(unified_id=CCLE_name, tissue=Site_primary) %>%
dplyr::collect()
plot_data <- gene1_data %>%
dplyr::inner_join(gene2_data, by='unified_id') %>%
dplyr::left_join(cls.df, by='unified_id')
return(plot_data)
}
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