#' Adjust and Export Pathway Analysis Results
#'
#' This function processes a dataframe containing fgsea results. It adjusts pathway names
#' by removing underscores, converting to lowercase, then capitalizing the first letter,
#' and joining the components with spaces. It selects and merges the top upregulated
#' and downregulated pathways based on enrichment score (ES) and p-value.
#'
#' @param fgseaRes Dataframe containing fgsea results with columns 'pathway', 'ES', and 'pval'.
#' @param nTop Integer, number of top pathways to select based on the p-value.
#' @return A vector containing combined top upregulated and downregulated pathways.
#' @importFrom Hmisc capitalize
#' @export
#' @examples
#' # Create a synthetic fgseaRes dataframe
#'fgseaRes <- data.frame(
#' pathway = c("KEGG_APOPTOSIS",
#' "GO_CELL_CYCLE",
#' "REACTOME_DNA_REPAIR",
#' "KEGG_METABOLISM",
#' "GO_TRANSPORT"),
#' ES = c(0.45, -0.22, 0.56, -0.35, 0.33),
#' pval = c(0.001, 0.02, 0.0003, 0.05, 0.01)
#')
#'
#' # Run the function to get top pathways
#'result <- adjust_export_pathway(fgseaRes = fgseaRes, nTop = 2)
#'
adjust_export_pathway <- function(fgseaRes, nTop = 10) {
# Adjust pathway names
fgseaRes$pathway <- as.character(fgseaRes$pathway)
for(i in 1:nrow(fgseaRes)){
message("Processing row ", i)
term = fgseaRes$pathway[i]
### 1. Split the string
term = unlist(strsplit(term, split="_", fixed=TRUE))[-1]
### 2. Convert to lowercase, then capitalize the first letter
term = Hmisc::capitalize(tolower(term))
### 3. Concatenate with spaces
term = paste(term, collapse=" ")
### 4. Data export
fgseaRes$pathway[i] = term
}
# Select top upregulated pathways
topPathwaysUp <- fgseaRes[fgseaRes$ES > 0,][order(fgseaRes$pval[fgseaRes$ES > 0]), 'pathway'][1:nTop]
# Select top downregulated pathways
topPathwaysDown <- fgseaRes[fgseaRes$ES < 0,][order(fgseaRes$pval[fgseaRes$ES < 0]), 'pathway'][1:nTop]
# Combine top pathways and convert any potential list to a vector
combinedPathways <- unlist(c(topPathwaysUp, rev(topPathwaysDown)), use.names = FALSE)
return(list(combinedPathways = combinedPathways, fgseaRes = fgseaRes))
}
#' Randomly Select Pathways with Limited Word Count
#'
#' This function randomly selects a specified number of pathways from a given list, ensuring that each selected pathway name does not exceed a specified number of words. It filters out pathways with names longer than the specified word limit before making the selection.
#'
#' @param pathways Character vector of pathways.
#' @param max_words Integer, maximum number of words allowed in the pathway name.
#' @param num_select Integer, number of pathways to randomly select.
#' @return A character vector of selected pathways.
#' @export
#' @examples
#' pathway_list <- c("pathway_one response to stimulus",
#' "pathway_two cell growth and death",
#' "pathway_three regulation of cellular process",
#' "pathway_four metabolic process")
#' selected_pathways <- selectPathways(pathway_list, max_words = 5, num_select = 2)
#'
selectPathways <- function(pathways, max_words = 10, num_select = 10) {
# Check input
if (!is.character(pathways)) {
stop("Please provide a character vector of pathways.")
}
# Filter pathways with word count not exceeding max_words
filtered_pathways <- pathways[sapply(pathways, function(x) length(strsplit(x, " ")[[1]]) <= max_words)]
# Randomly select num_select pathways from the filtered list
if (length(filtered_pathways) >= num_select) {
selected_pathways <- sample(filtered_pathways, num_select)
} else {
warning("Not enough pathways with <= ", max_words, " words. Returning as many as possible.")
selected_pathways <- sample(filtered_pathways, length(filtered_pathways))
}
return(selected_pathways)
}
#' Draw Dual-Sided Legends on a Plot
#'
#' This function creates two sets of legends, one on the left and one on the right side of a plot.
#' It displays color-coded legends with labels corresponding to different data categories.
#' Each legend entry consists of a colored rectangle and a text label. The left side legend has
#' text aligned to the right of the color block, while the right side legend has text aligned
#' to the left of the color block.
#'
#' @param labels Vector of labels for the legends.
#' @param colors Vector of colors corresponding to the labels.
#' @param legend_width The width of each legend viewport expressed in grid units.
#' @param x_positions Numeric vector of length 2 specifying the x-positions of the left and right legends.
#' @param y_position The y-position common for both legends, expressed as a fraction of the plot height.
#' @param just_positions List of two vectors, each specifying the horizontal and vertical justification for the legends.
#' @param text_alignments List of two character strings specifying text alignments for the legends ('left' or 'right').
#' @param font_size Numeric value specifying the font size for the legend labels.
#' @return Invisible. This function is called for its side effects of drawing legends on a plot.
#' @importFrom grid pushViewport viewport grid.roundrect grid.text upViewport unit
#' @export
#' @examples
#' labels <- c("Label1", "Label2", "Label3", "Label4", "Label5", "Label6")
#' colors <- c("#ff0000", "#00ff00", "#0000ff", "#ffff00", "#ff00ff", "#00ffff")
#'
#' # Convert to 'unit' objects for grid
#' grid::grid.roundrect(
#' x = grid::unit(0.5, "npc"), # "npc" stands for normalized parent coordinates
#' y = grid::unit(0.5, "npc"),
#' width = grid::unit(0.1, "npc"),
#' height = grid::unit(0.05, "npc"),
#' gp = grid::gpar(fill = "red"),
#' r = grid::unit(0.1, "npc") # rounding radius
#' )
#'
#' # Example of drawing legends with specific labels and colors
#' drawLegends(labels, colors, grid::unit(2, "cm"), c(0.225, 0.75), 0.5,
#' list(c("left", "center"), c("right", "center")),
#' list("right", "left"), 10)
#'
drawLegends <- function(labels, colors, legend_width, x_positions, y_position, just_positions, text_alignments, font_size) {
half_length <- length(labels) / 2
legend_height <- grid::unit(1, "lines") * half_length
# Draw left-side legend
grid::pushViewport(grid::viewport(
width = legend_width,
height = legend_height,
x = x_positions[1],
y = y_position,
just = just_positions[[1]]
))
for (i in seq_len(half_length)) {
grid::grid.roundrect(
x = grid::unit(1, "npc") - grid::unit(0.5, "cm"),
y = grid::unit(1, "npc") - grid::unit(i / half_length, "npc") + grid::unit(0.5 / half_length, "npc"),
width = grid::unit(0.7, "cm"),
height = grid::unit(0.9 / half_length, "npc"),
gp = grid::gpar(fill = colors[i], col = NA),
r = grid::unit(0.3, "snpc")
)
grid::grid.text(
labels[i],
x = grid::unit(1, "npc") - grid::unit(1, "cm"),
y = grid::unit(1, "npc") - grid::unit(i / half_length, "npc") + grid::unit(0.5 / half_length, "npc"),
gp = grid::gpar(col = colors[i], fontsize = font_size),
just = text_alignments[[1]]
)
}
grid::upViewport()
# Draw right-side legend
grid::pushViewport(grid::viewport(
width = legend_width,
height = legend_height,
x = x_positions[2],
y = y_position,
just = just_positions[[2]]
))
for (i in (half_length + 1):length(labels)) {
grid::grid.roundrect(
x = grid::unit(1, "npc") - grid::unit(0.6, "cm"),
y = grid::unit(1, "npc") - grid::unit((i - half_length) / half_length, "npc") + grid::unit(0.5 / half_length, "npc"),
width = grid::unit(0.7, "cm"),
height = grid::unit(0.9 / half_length, "npc"),
gp = grid::gpar(fill = colors[i], col = NA),
r = grid::unit(0.3, "snpc")
)
grid::grid.text(
labels[i],
x = grid::unit(1, "npc") - grid::unit(0.1, "cm"),
y = grid::unit(1, "npc") - grid::unit((i - half_length) / half_length, "npc") + grid::unit(0.5 / half_length, "npc"),
gp = grid::gpar(col = colors[i], fontsize = font_size),
just = text_alignments[[2]]
)
}
grid::upViewport()
}
#' Draw Chord Diagram with Legends
#'
#' This function creates a chord diagram from a specified dataframe and draws two sets of legends for it.
#' It adjusts the track height of the chord diagram to optimize space and uses specified colors for the grid.
#' Legends are drawn at specified positions with configurable text alignments and font sizes.
#'
#' @param all_combined_df A dataframe containing the matrix for the chord diagram.
#' @param original_colors A vector of colors for the grid columns of the chord diagram.
#' @param labels A vector of labels for the first legend.
#' @param colors A vector of colors corresponding to the first legend's labels.
#' @param labels2 A vector of labels for the second legend.
#' @param colors2 A vector of colors corresponding to the second legend's labels.
#' @param font_size The font size used for legend texts, defaults to 10.
#' @return Invisible, primarily used for its side effects of drawing on a graphics device.
#' @importFrom circlize chordDiagram
#' @importFrom grid unit
#' @importFrom graphics strwidth
#' @export
#' @examples
#' # Sample Chord Diagram Matrix
#' all_combined_df <- data.frame(
#' A = c(10, 20, 30),
#' B = c(15, 25, 35),
#' C = c(5, 10, 15)
#' )
#' rownames(all_combined_df) <- c("A", "B", "C")
#'
#' # Colors for the grid of the chord diagram (corresponding to columns of the matrix)
#' original_colors <- c("red", "green", "blue")
#'
#' # Name the colors according to the sectors (A, B, C)
#' names(original_colors) <- colnames(all_combined_df)
#'
#' # Labels and Colors for the First Legend
#' labels <- c("Label 1", "Label 2", "Label 3")
#' colors <- c("yellow", "purple", "cyan")
#'
#' # Labels and Colors for the Second Legend
#' labels2 <- c("Label A", "Label B", "Label C")
#' colors2 <- c("orange", "pink", "brown")
#'
#' # Font size for the legend texts (optional, default is 10)
#' font_size <- 10
#'
#' # Call the enrichment_circlize function with the sample data
#' # This is just an example; the plot will be rendered in an appropriate graphics context
#' # such as RStudio's plot pane or an external plotting window.
#' plot1 <- enrichment_circlize(all_combined_df,
#' original_colors,
#' labels,
#' colors,
#' labels2,
#' colors2,
#' font_size
#' )
#'
enrichment_circlize <- function(all_combined_df, original_colors, labels, colors,
labels2, colors2, font_size = 10) {
# Calculate adjusted height for the chord diagram
max_height <- max(graphics::strwidth(unlist(dimnames(all_combined_df)), "inches")) * 1.2
# Draw the chord diagram
circlize::chordDiagram(all_combined_df, grid.col = original_colors, annotationTrack = "grid",
directional = -1, direction.type = c("diffHeight", "arrows"),
link.arr.type = "big.arrow", preAllocateTracks = list(track.height = max_height))
# Draw the first set of legends
legend_width <- grid::unit(2, "cm")
x_positions <- c(0.225, 0.75)
y_position <- 0.5
just_positions <- list(c("left", "center"), c("right", "center"))
text_alignments <- list("right", "left")
drawLegends(labels, colors, legend_width, x_positions, y_position, just_positions, text_alignments, font_size)
# Draw the second set of legends
x_positions2 <- c(0.3, 0.68)
y_position2 <- 0.7
drawLegends(labels2, colors2, legend_width, x_positions2, y_position2, just_positions, text_alignments, font_size)
}
Datasets
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