R/relabu_boxplot.R

In compbiomed/animalcules: Interactive microbiome analysis toolkit

#' Plot boxplots comparing different organism prevalence across conditions
#'
#' @param MAE A multi-assay experiment object
#' @param tax_level The taxon level used for organisms
#' @param condition Compare groups by condition e.g. 'SEX'
#' @param organisms Include organisms for plotting.
#' @param datatype counts, relative abundance,logcpm
#' @return A plotly object
#'
#' @examples
#' data_dir <- system.file("extdata/MAE.rds", package = "animalcules")
#' toy_data <- readRDS(data_dir)
#' p <- relabu_boxplot(toy_data,
#'   tax_level = "genus",
#'   organisms = c("Escherichia", "Actinomyces"),
#'   condition = "SEX",
#'   datatype = "logcpm"
#' )
#' p
#'
#' @import plotly
#' @import magrittr
#' @import reshape2
#' @import MultiAssayExperiment
#'
#' @export
relabu_boxplot <- function(MAE,
    tax_level,
    condition,
    organisms = c(),
    datatype = c(
        "counts",
        "relative abundance",
        "logcpm"
    )) {
    . <- NULL
    
    # Default variables
    datatype <- match.arg(datatype)
    
    # Extract data
    microbe <- MAE[["MicrobeGenetics"]]
    # host <- MultiAssayExperiment::experiments(MAE)[[2]]
    tax_table <- as.data.frame(rowData(microbe)) # organism x taxlev
    sam_table <- as.data.frame(colData(microbe)) # sample x condition
    counts_table <-
        as.data.frame(assays(microbe))[, rownames(sam_table)] #organism x sample
    
    # Ensure conditions are all factored
    sam_table %<>% df_char_to_factor()
    
    # Sum counts by taxon level and return counts
    df <- counts_table %>% # Sum counts by taxon level
        upsample_counts(tax_table, tax_level) %>% # Choose data type
        {
            if (datatype == "relative abundance") {
                counts_to_relabu(.)
            } else if (datatype == "logcpm") {
                counts_to_logcpm(.)
            } else {
                .
            }
        } %>% # Subset on chosen organisms
        .[organisms, , drop = FALSE] %>% # Transpose
        t() %>% # Back to data frame
        as.data.frame() %>% # Merge in covariate information
        merge(sam_table[, condition, drop = FALSE],
            by = 0, all = TRUE
        ) %>% # Melt for plotly
        reshape2::melt(by = organisms, variable.name = "organisms")
    
    p <- plot_ly(df,
        x = ~organisms, y = ~value,
        color = ~ df[, condition], type = "box"
    ) %>%
        layout(
            boxmode = "group", xaxis = list(title = ""),
            yaxis = list(title = datatype)
        )
    p$p <- NULL # To suppress a shiny warning
    return(p)
}