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#' Plot grouped bar charts of cell type proportions
#'
#' This function allows you to visualize the relative abundance of sub-populations after running fit_maple()
#' @param fit A list returned by fit_maple()
#' @param group A column name of fit$W or a grouping vector of length N (nrow(fit$W))
#'
#' @keywords spatial transcriptomics Bayesian
#' @import dplyr
#' @import ggplot2
#' @importFrom rlang .data
#' @export
#' @return A ggplot object
#' @examples
#' \dontrun{
#' brain1 <- LoadData("stxBrain", type = "anterior1")
#' brain2 <- LoadData("stxBrain", type = "anterior2")
#' brain1 <- SCTransform(brain1, assay = "Spatial", verbose = FALSE)
#' brain2 <- SCTransform(brain2, assay = "Spatial", verbose = FALSE)
#' brain <- merge(brain1,brain2)
#' DefaultAssay(brain) <- "SCT"
#' VariableFeatures(brain) <- c(VariableFeatures(brain1),VariableFeatures(brain2))
#' brain <- RunPCA(brain)
#' brain_fit_PCs <- fit_maple(brain,K = 6,emb = "PCs")
#' plot_props_grouped(brain_fit_PCs, group = brain$orig.ident)
#' }
plot_props_grouped <- function(fit, group)
{
if(length(group) == 1)
{
al_df = data.frame(z = fit$z,group = fit$W[,group])
}
else if(length(group) == nrow(fit$W))
{
group = as.factor(group)
al_df = data.frame(z = fit$z,group = group)
}
else
{
message("Error: group should be either a column name of fit$W, a column index of fit$W, or a length N categorical vector coercible to a factor.")
return()
}
al_df_sum = al_df %>%
group_by(.data$z,.data$group) %>%
summarize(Freq = n())
al_df_group = al_df %>%
group_by(.data$group) %>%
summarize(n_group = n())
al_df_sum = inner_join(al_df_sum,al_df_group,by = "group") %>%
mutate(prop = .data$Freq/.data$n_group) %>%
mutate(z = as.factor(.data$z), group = as.factor(.data$group))
g <- ggplot(al_df_sum, aes(x = .data$z, y = .data$prop, fill = .data$group)) +
geom_bar(position = "dodge", stat = "identity", color = "black") +
theme_classic() +
scale_y_continuous(expand = c(0,0)) +
scale_x_discrete(expand = c(0,0)) +
xlab("Sub-Population") +
ylab("Proportion of Group")
return(g)
}
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