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vis.R
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if (getRversion() >= "2.15.1") {
utils::globalVariables(c(
"value", "variable", "Clonotype", "Count", "Sample", "Clone",
"Value", "Value.mean", "Value.min", "Value.max", "quantile",
"Kmer", "Group", "Size", "Val", "Grouping.var", "Xrep", "Yrep",
"Clone.num", "Q", "Clonotypes", "Sample_subj", "Seq.count",
"Overlap", "head", "Mean", "MeanVal", "MinVal", "MaxVal",
"Q1", "Q2", "Type", "Length", "Gene", "Freq", "Sequence",
"AA", "Clones", "Source.gr", "Target.gr", "Samples", "Samples.y",
"CDR3.aa", "p.adj", "group1", "group2", "y.coord", ".SD",
"name", "label", "."
))
}
##### Utility functions #####
.rem_legend <- function(.p) {
.p + theme(legend.position = "none")
}
.rem_xlab <- function(.p) {
.p + theme(axis.title.x = element_blank())
}
.rem_ylab <- function(.p) {
.p + theme(axis.title.y = element_blank())
}
.colourblind_vector <- function() {
c("#FF4B20", "#FFB433", "#C6FDEC", "#7AC5FF", "#0348A6")
}
.colourblind_discrete <- function(.n, .colour = FALSE) {
cs <- .colourblind_vector()
if (.colour) {
scale_colour_manual(values = colorRampPalette(cs)(.n))
} else {
scale_fill_manual(values = colorRampPalette(cs)(.n))
}
}
.tweak_fill <- function(.n) {
if (.n == 1) {
palette_name <- "Set2"
} else if (.n == 2) {
palette_name <- "Set1"
} else if (.n < 12) {
palette_name <- "Spectral"
} else {
return(scale_fill_hue())
}
scale_fill_brewer(palette = palette_name)
}
.tweak_col <- function(.n) {
if (.n == 1) {
palette_name <- "Set2"
} else if (.n == 2) {
palette_name <- "Set1"
} else if (.n < 12) {
palette_name <- "Spectral"
} else {
return(scale_colour_hue())
}
scale_color_brewer(palette = palette_name)
}
theme_cleveland2 <- function(rotate = TRUE) {
if (rotate) {
theme(
panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_line(
colour = "grey70",
linetype = "dashed"
)
)
} else {
theme(
panel.grid.major.x = element_line(
colour = "grey70",
linetype = "dashed"
), panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank()
)
}
}
##### The one and only - the ultimate vis() function #####
#' One function to visualise them all
#'
#' @concept vis
#'
#' @name vis
#'
#' @import ggplot2
#' @importFrom factoextra fviz_cluster fviz_dend fviz_pca_ind
#' @importFrom grDevices colorRampPalette
#' @importFrom tidyr drop_na
#' @importFrom igraph graph_from_data_frame
#' @importFrom ggraph ggraph geom_edge_diagonal geom_node_point theme_graph
#'
#' @description Output from every function in immunarch can be visualised with a
#' single function - \code{vis}. The \code{vis} automatically detects
#' the type of the data and draws a proper visualisation. For example, output
#' from the \code{repOverlap} function will be identified as repertoire overlap values
#' and respective visualisation will be chosen without any additional arguments.
#' See "Details" for the list of available visualisations.
#'
#' @param .data Pass the output from any immunarch analysis tool to \code{vis()}.
#' @param ... Any other arguments, see the "Details" section for specific visualisation functions.
#'
#' @details
#' List of available visualisations for different kinds of data.
#'
#' Basic analysis:
#'
#' - Exploratory analysis results (from \link{repExplore}) - see \link{vis.immunr_exp_vol};
#'
#' - Clonality statistics (from \link{repClonality}) - see \link{vis.immunr_homeo}.
#'
#' Overlaps and public clonotypes:
#'
#' - Overlaps (from \link{repOverlap}) using heatmaps, circos plots, polar area plots - see \link{vis.immunr_ov_matrix};
#'
#' - Overlap clustering (from \link{repOverlapAnalysis}) - see \link{vis.immunr_hclust};
#'
#' - Repertoire incremental overlaps (from \link{repOverlap}) - see \link{vis.immunr_inc_overlap};
#'
#' - Public repertoire abundance (from \link{pubRep}) - vis \link{vis.immunr_public_repertoire}.
#'
#' Gene usage:
#'
#' - Gene usage statistics (from \link{geneUsage}) using bar plots, box plots - see \link{vis.immunr_gene_usage};
#'
#' - Gene usage distances (from \link{geneUsageAnalysis}) using heatmaps, circos plots, polar area plots - see \link{vis.immunr_ov_matrix};
#'
#' - Gene usage clustering (from \link{geneUsageAnalysis}) - see \link{vis.immunr_hclust}.
#'
#' Diversity estimation:
#'
#' - Diversity estimations (from \link{repDiversity}) - see \link{vis.immunr_chao1}.
#'
#' BCR analysis:
#'
#' - Clonal tree (from \link{repClonalFamily}) - see \link{vis.clonal_family} and \link{vis.clonal_family_tree}.
#'
#' Advanced analysis:
#'
#' - Repertoire dynamics (from \link{trackClonotypes}) - see \link{vis.immunr_dynamics};
#'
#' - Sequence logo plots of amino acid distributions (from \link{kmer_profile}) - see \link{vis_seqlogo};
#'
#' - Kmers distributions (from \link{getKmers}) - see \link{vis.immunr_kmer_table};
#'
#' - Mutation networks (from mutationNetwork) - Work In Progress on vis.immunr_mutation_network;
#'
#' - CDR3 amino acid properties, e.g., biophysical (from cdrProp) - Work In Progress on vis.immunr_cdr_prop.
#'
#' Additionaly, we provide a wrapper functions for visualisations of common data types:
#'
#' - Any data frames or matrices using heatmaps - see \link{vis_heatmap} and \link{vis_heatmap2};
#'
#' - Any data frames or matrices using circos plots - see \link{vis_circos}.
#'
#' @return
#' A ggplot2, pheatmap or circlize object.
#'
#' @seealso \link{fixVis} for precise manipulation of plots.
#'
#' @examples
#' # Load the test data
#' data(immdata)
#'
#' # Compute and visualise:
#' ov <- repOverlap(immdata$data)
#' vis(ov)
#'
#' gu <- geneUsage(immdata$data)
#' vis(gu)
#'
#' dv <- repDiversity(immdata$data)
#' vis(dv)
#' @export vis
vis <- function(.data, ...) {
UseMethod("vis")
}
##### Overlap & heatmaps, circos plots and polar area plots #####
#' Repertoire overlap and gene usage visualisations
#'
#' @concept overlap
#'
#' @name vis.immunr_ov_matrix
#'
#' @aliases vis.immunr_ov_matrix vis.immunr_gu_matrix
#'
#' @description Visualises matrices with overlap values or gene usage distances among samples.
#' For details see the links below.
#'
#' @param .data Output from \link{repOverlap} or \link{geneUsageAnalysis}.
#'
#' @param .plot A string specifying the plot type:
#'
#' - "heatmap" for heatmaps using \link{vis_heatmap};
#'
#' - "heatmap2" for heatmaps using \link{vis_heatmap2};
#'
#' - "circos" for circos plots using \link{vis_circos};
#'
#' @param ... Other arguments are passed through to the underlying plotting function:
#'
#' - "heatmap" - passes arguments to \link{vis_heatmap};
#'
#' - "heatmap2" - passes arguments to \link{vis_heatmap2} and \link{heatmap} from the "pheatmap" package;
#'
#' - "circos" - passes arguments to \link{vis_circos} and \link{chordDiagram} from the "circlize" package;
#'
#' @return
#' A ggplot2, pheatmap or circlize object.
#'
#' @examples
#' data(immdata)
#' ov <- repOverlap(immdata$data)
#' vis(ov)
#' vis(ov, "heatmap")
#' vis(ov, "heatmap2")
#' vis(ov, "circos")
#' @export
vis.immunr_ov_matrix <- function(.data, .plot = c("heatmap", "heatmap2", "circos"), ...) {
args <- list(...)
if (!(".title" %in% names(args))) {
args$.title <- "Repertoire overlap"
}
.plot <- .plot[1]
heatfun <- vis_heatmap
if (.plot == "heatmap2") {
heatfun <- vis_heatmap2
} else if (.plot == "circos") {
heatfun <- vis_circos
}
do.call(heatfun, c(list(.data = .data), args))
}
#' @export
vis.immunr_gu_matrix <- function(.data, .plot = c("heatmap", "heatmap2", "circos"), ...) {
args <- list(...)
if (!(".title" %in% names(args))) {
args$.title <- "Gene usage"
}
.plot <- .plot[1]
heatfun <- vis_heatmap
if (.plot == "heatmap2") {
heatfun <- vis_heatmap2
} else if (.plot == "circos") {
heatfun <- vis_circos
}
do.call(heatfun, c(list(.data = .data), args))
}
#' Visualisation of matrices and data frames using ggplo2-based heatmaps
#'
#' @concept vis
#'
#' @name vis_heatmap
#'
#' @aliases vis_heatmap
#'
#' @description Fast and easy visualisations of matrices or data frames
#' with functions based on the ggplot2 package.
#'
#' @param .data Input object: a matrix or a data frame.
#'
#' If matrix: column names and row names (if presented) will be used as names for labs.
#'
#' If data frame: the first column will be used for row names and removed from the data.
#' Other columns will be used for values in the heatmap.
#'
#' @param .text If TRUE then plots values in the heatmap cells. If FALSE does not plot values,
#' just plot coloured cells instead.
#'
#' @param .scientific If TRUE then uses the scientific notation for numbers (e.g., "2.0e+2").
#'
#' @param .signif.digits Number of significant digits to display on plot.
#'
#' @param .text.size Size of text in the cells of heatmap.
#'
#' @param .axis.text.size Size of text on the axis labels.
#'
#' @param .labs A character vector of length two with names for x-axis and y-axis, respectively.
#'
#' @param .title The The text for the plot's title.
#'
#' @param .leg.title The The text for the plots's legend. Provide NULL to remove the legend's title completely.
#'
#' @param .legend If TRUE then displays a legend, otherwise removes the legend from the plot.
#'
#' @param .na.value Replace NA values with this value. By default they remain NA.
#'
#' @param .transpose Logical. If TRUE then switch rows and columns.
#'
#' @param ... Other passed arguments.
#'
#' @return
#' A ggplot2 object.
#'
#' @seealso \link{vis}, \link{repOverlap}.
#'
#' @examples
#' data(immdata)
#' ov <- repOverlap(immdata$data)
#' vis_heatmap(ov)
#' gu <- geneUsage(immdata$data, "hs.trbj")
#' vis_heatmap(gu)
#' @export
vis_heatmap <- function(.data, .text = TRUE, .scientific = FALSE, .signif.digits = 2,
.text.size = 4, .axis.text.size = NULL,
.labs = c("Sample", "Sample"), .title = "Overlap",
.leg.title = "Overlap values", .legend = TRUE,
.na.value = NA, .transpose = FALSE, ...) {
if (has_class(.data, "data.frame")) {
names <- .data[[1]]
.data <- as.matrix(.data[, -1])
row.names(.data) <- names
if (.transpose) {
.data <- t(.data)
}
} else if (is.null(dim(.data))) {
.data <- as.matrix(.data)
if (.transpose) {
.data <- t(.data)
}
}
if (is.null(colnames(.data))) {
colnames(.data) <- paste0("C", seq_len(ncol(.data)))
}
if (is.null(row.names(.data))) {
row.names(.data) <- paste0("C", seq_len(nrow(.data)))
}
.data[is.na(.data)] <- .na.value
tmp <- as.data.frame(.data)
tmp$name <- row.names(.data)
m <- reshape2::melt(tmp, id.var = c("name"))
m[, 1] <- factor(m[, 1], levels = rev(rownames(.data)))
m[, 2] <- factor(m[, 2], levels = colnames(.data))
format <- if (.scientific) "g" else "fg"
m$label <- formatC(m$value, format = format, digits = .signif.digits)
p <- ggplot(m, aes(x = variable, y = name, fill = value))
p <- p + geom_tile(aes(fill = value), colour = "white")
if (.text) {
p <- p + geom_text(aes(label = label), size = .text.size)
}
p <- p + scale_fill_distiller(palette = "RdBu")
p <- p + ggtitle(.title) +
guides(fill = guide_colourbar(title = .leg.title)) +
xlab(.labs[1]) + ylab(.labs[2]) + coord_fixed() +
theme_linedraw() + theme(
axis.text.x =
element_text(angle = 90, vjust = .5, size = .axis.text.size)
) + theme(
axis.text.y =
element_text(size = .axis.text.size)
) + scale_x_discrete(expand = c(0, 0)) + scale_y_discrete(expand = c(0, 0))
if (!.legend) {
p <- .rem_legend(p)
}
if (is.null(.labs)) {
p <- .rem_xlab(.rem_ylab(p))
}
p
}
#' Visualisation of matrices using pheatmap-based heatmaps
#'
#' @concept vis
#'
#' @importFrom pheatmap pheatmap
#'
#' @name vis_heatmap2
#'
#' @description Visualise matrices with the functions based on the \link[pheatmap]{pheatmap}
#' package with minimum amount of arguments.
#'
#' @param .data Input matrix. Column names and row names (if presented) will be used as names for labs.
#'
#' @param .meta A metadata object. An R dataframe with sample names and their properties,
#' such as age, serostatus or hla.
#'
#' @param .by Set NA if you want to plot samples without grouping.
#'
#' @param .title The text for the plot's title (same as the "main" argument in \link[pheatmap]{pheatmap}).
#'
#' @param .color A vector specifying the colors (same as the "color" argument in \link[pheatmap]{pheatmap}).
#' Pass NA to use the default pheatmap colors.
#'
#' @param ... Other arguments for the \link[pheatmap]{pheatmap} function.
#'
#' @return
#' A pheatmap object.
#'
#' @seealso \link{vis}, \link{repOverlap}
#'
#' @examples
#' data(immdata)
#' ov <- repOverlap(immdata$data)
#' vis_heatmap2(ov)
#' @export
vis_heatmap2 <- function(.data, .meta = NA, .by = NA, .title = NA, .color = colorRampPalette(c("#67001f", "#d6604d", "#f7f7f7", "#4393c3", "#053061"))(1024), ...) {
args <- list(...)
if (!is.na(.by)[1]) {
if (!is.na(.meta)[1]) {
args[["annotation_col"]] <- .meta %>%
tibble::column_to_rownames(var = "Sample") %>%
dplyr::select(tidyselect::any_of(.by))
}
}
args[["mat"]] <- .data
args[["main"]] <- .title
if (!is.na(.color)[1]) {
args[["color"]] <- .color
}
do.call(pheatmap, args)
}
#' Visualisation of matrices using circos plots
#'
#' @concept vis
#'
#' @importFrom circlize chordDiagram
#'
#' @name vis_circos
#'
#' @description Visualise matrices with the \link{chordDiagram} function
#' from the circlize package.
#'
#' @param .data Input matrix.
#'
#' @param .title The The text for the title of the plot.
#'
#' @param ... Other arguments passed to \link{chordDiagram} from the 'circlize' package.
#'
#' @return
#' A circlize object.
#'
#' @seealso \link{vis}, \link{repOverlap}.
#'
#' @examples
#' data(immdata)
#' ov <- repOverlap(immdata$data)
#' vis(ov, .plot = "circos")
#' @export
vis_circos <- function(.data, .title = NULL, ...) {
if (has_class(.data, "tibble")) {
.data <- as.data.frame(.data)
}
chordDiagram(.data, ...)
}
# vis_radar <- function(.data, .by = NA, .meta = NA,
# .ncol = NA, .which = NA,
# .errorbars = c(0.025, 0.975), .errorbars.off = FALSE, .expand = c(.25, 0),
# .title = NA, .subtitle = NULL,
# .legend = FALSE, .leg.title = NULL, ...) {
# stop("vis_radar() is under complety re-working. Please use other functions such as vis_heatmap.
# Sorry for the inconvenience! Please contact us if you need vis_radar() ASAP.")
#
# .data <- melt(.data)
# colnames(.data) <- c("Source", "Target", "Value")
# .data$Value[is.na(.data$Value)] <- 0
#
# group_res <- process_metadata_arguments(.data, .by, .meta, .data.sample.col = "Source")
# group_res2 <- process_metadata_arguments(.data, .by, .meta, .data.sample.col = "Target")
# .data$Source.gr <- group_res$group_column
# .data$Target.gr <- group_res2$group_column
#
# .data_proc <- .data %>%
# dplyr::group_by(Source.gr, Target.gr) %>%
# dplyr::summarise(
# Value = mean(Value, na.rm = TRUE),
# Value.min = quantile(Value, .errorbars[1], na.rm = TRUE),
# Value.max = quantile(Value, .errorbars[2], na.rm = TRUE)
# )
# .data_proc <- .data_proc[!is.na(.data_proc$Source.gr), ]
#
# step <- length(unique(.data_proc$Source.gr))
#
# if (!is.na(.which[1])) {
# .data_proc <- .data_proc[.data_proc$Source.gr %in% .which, ]
# }
#
# if (is.na(.leg.title)) {
# if (group_res$is_grouped) {
# .leg.title <- "Group"
# } else {
# .leg.title <- "Sample"
# }
# }
#
# if (!group_res$is_grouped) {
# .errorbars.off <- TRUE
# }
#
# if (is.na(.ncol)) {
# .ncol <- round(length(unique(.data_proc$Source.gr))**.5)
# }
#
# .data_proc$Value[.data_proc$Source.gr == .data_proc$Target.gr] <- 0
# .data_proc$Value.min[.data_proc$Source.gr == .data_proc$Target.gr] <- NA
# .data_proc$Value.max[.data_proc$Source.gr == .data_proc$Target.gr] <- NA
#
# ps <- lapply(seq(1, nrow(.data_proc), step), function(l) {
# p <- ggplot(.data_proc[l:(l + step - 1), ], aes(x = Target.gr, y = Value, fill = Target.gr)) +
# geom_bar(colour = "black", stat = "identity")
#
# if (!.errorbars.off) {
# p <- p +
# geom_errorbar(aes(ymin = Value.min, ymax = Value.max), width = 0.2, position = position_dodge(.9))
# }
#
# p <- p +
# coord_polar() +
# scale_y_continuous(expand = .expand) +
# theme_linedraw() +
# .colourblind_discrete(step)
#
# if (!.legend) {
# p <- p + guides(fill = "none")
# } else {
# p <- p + guides(fill = guide_legend(title = .leg.title))
# }
#
# p <- p +
# labs(title = .data_proc$Source.gr[l], subtitle = .subtitle)
#
# p
# })
# do.call(grid.arrange, c(ps, ncol = .ncol, top = .title))
# }
##### Complex overlaps - top overlap & public repertoires #####
#' Visualise incremental overlaps
#'
#' @concept overlap
#'
#' @importFrom dplyr rename
#'
#' @name vis.immunr_inc_overlap
#'
#' @param .data Output from the \link{repOverlap} function that uses "top" methods.
#'
#' @param .target Index of a repertoire to plot. Omitted if .grid is TRUE.
#'
#' @param .grid Logical. If TRUE then plot all similarities in a grid.
#'
#' @param .ncol Numeric. Number of columns in the resulting grid.
#'
#' @param ... Not used here.
#'
#' @return
#' A ggplot2 object.
#'
#' @seealso \link{repOverlap}
#'
#' @examples
#' data(immdata)
#' tmp <- repOverlap(immdata$data[1:4], "inc+overlap", .verbose.inc = FALSE, .verbose = FALSE)
#' vis(tmp, .target = 1)
#' vis(tmp, .grid = TRUE)
#' @export
vis.immunr_inc_overlap <- function(.data, .target = 1, .grid = FALSE, .ncol = 2, ...) {
data_is_bootstrapped <- !is.null(attr(.data, "bootstrap"))
if (.grid) {
if (data_is_bootstrapped) {
sample_names <- colnames(.data[[1]][[1]])
} else {
sample_names <- colnames(.data[[1]])
}
p_list <- lapply(seq_along(sample_names), function(i_name) {
p <- vis(.data, .target = i_name) + ggtitle(sample_names[i_name]) + theme_pubr(legend = "right") + theme_cleveland2()
p
})
return(do.call(wrap_plots, c(p_list, list(ncol = .ncol))))
} else {
if (data_is_bootstrapped) {
sample_names <- colnames(.data[[1]][[1]])
.data <- lapply(.data, function(mat_list) {
lapply(mat_list, function(mat) {
replace(mat, lower.tri(mat, TRUE), NA)
})
})
.data <- reshape2::melt(.data, na.rm = TRUE)
names(.data) <- c("Sample_subj", "Sample", "Overlap", "N", "Seq.count")
filtered_data <- .data[.data$Sample_subj == sample_names[.target], ]
filtered_data$Seq.count <- as.numeric(filtered_data$Seq.count)
filtered_data <- filtered_data %>%
dplyr::group_by(Sample_subj, Sample, Seq.count, Overlap) %>%
dplyr::summarise(
Value = mean(Overlap, na.rm = TRUE),
Value.min = quantile(Overlap, 0.025, na.rm = TRUE),
Value.max = quantile(Overlap, 0.975, na.rm = TRUE)
)
} else {
sample_names <- colnames(.data[[1]])
.data <- lapply(.data, function(mat) replace(mat, lower.tri(mat, TRUE), NA))
.data <- reshape2::melt(.data, na.rm = TRUE)
names(.data) <- c("Sample_subj", "Sample", "Overlap", "Seq.count")
filtered_data <- .data[.data$Sample_subj == sample_names[.target], ]
filtered_data$Seq.count <- as.numeric(filtered_data$Seq.count)
filtered_data <- filtered_data %>%
dplyr::rename(Value = Overlap)
}
p <- ggplot(aes(x = Seq.count, y = Value, col = Sample, group = Sample), data = filtered_data) +
geom_line()
if (data_is_bootstrapped) {
p <- p +
geom_ribbon(aes(group = Sample, ymin = Value.min, ymax = Value.max, fill = Sample), alpha = 0.15)
}
p <- p +
theme_pubr(legend = "right") + theme_cleveland2() +
ggtitle("Similarity of repertoires") +
xlab("Sequence count") + ylab("Similarity")
return(p)
}
}
#' Public repertoire visualisation
#'
#' @concept pubrep
#'
#' @name vis.immunr_public_repertoire
#'
#' @param .data Public repertoire, an output from \link{pubRep}.
#' @param .plot A string specifying the plot type:
#'
#' - "freq" for visualisation of the distribution of occurrences of clonotypes
#' and their frequencies using \link{vis_public_frequencies}.
#'
#' - "clonotypes" for visualisation of public clonotype frequenciy correlations between pairs of
#' samples using \link{vis_public_clonotypes}
#' @param ... Further arguments passed \link{vis_public_frequencies} or \link{vis_public_clonotypes},
#' depending on the ".plot" argument.
#'
#' @return
#' A ggplot2 object.
#'
#' @examples
#' data(immdata)
#' immdata$data <- lapply(immdata$data, head, 300)
#' pr <- pubRep(immdata$data, .verbose = FALSE)
#' vis(pr, "freq")
#' vis(pr, "freq", .type = "none")
#'
#' vis(pr, "clonotypes", 1, 2)
#' @export
vis.immunr_public_repertoire <- function(.data, .plot = c("freq", "clonotypes"), ...) {
.plot <- .plot[1]
if (.plot == "freq") {
vis_public_frequencies(.data, ...)
} else if (.plot == "clonotypes") {
vis_public_clonotypes(.data, ...)
} else {
stop('Error: unknown argument for .plot, please provide one of the following: "freq", "clonotypes" or "???"')
}
}
#' Visualise sharing of clonotypes among samples
#'
#' @concept pubrep
#'
#' @importFrom UpSetR upset fromExpression
#'
#' @name vis.immunr_public_statistics
#'
#' @description Visualise public clonotype frequencies.
#'
#' @param .data Public repertoire - an output from the \link{pubRep} function.
#'
#' @param ... Other arguments passsed directly to \link{upset}.
#'
#' @return
#' A ggplot2 object.
#'
#' @examples
#' data(immdata)
#' immdata$data <- lapply(immdata$data, head, 2000)
#' pr <- pubRep(immdata$data, .verbose = FALSE)
#' pubRepStatistics(pr) %>% vis()
#' @export
vis.immunr_public_statistics <- function(.data, ...) {
upsetr_data <- as.list(.data$Count)
names(upsetr_data) <- .data$Group
upset(fromExpression(upsetr_data), ...)
}
#' Public repertoire visualisation
#'
#' @concept pubrep
#'
#' @name vis_public_frequencies
#'
#' @description Visualise public clonotype frequencies.
#'
#' @param .data Public repertoire - an output from the \link{pubRep} function.
#' @param .by Pass NA if you want to plot samples without grouping.
#'
#' You can pass a character vector with one or several column names from ".meta"
#' to group your data before plotting. In this case you should provide ".meta".
#'
#' You can pass a character vector that exactly matches the number of samples in
#' your data, each value should correspond to a sample's property. It will be used
#' to group data based on the values provided. Note that in this case you should
#' pass NA to ".meta".
#'
#' @param .meta A metadata object. An R dataframe with sample names and their properties,
#' such as age, serostatus or hla.
#' @param .type Character. Either "boxplot" for plotting distributions of frequencies,
#' "none" for plotting everything, or "mean" for plotting average values only.
#'
#' @return
#' A ggplot2 object.
#'
#' @examples
#' data(immdata)
#' immdata$data <- lapply(immdata$data, head, 500)
#' pr <- pubRep(immdata$data, .verbose = FALSE)
#' vis(pr, "freq", .type = "boxplot")
#' vis(pr, "freq", .type = "none")
#' vis(pr, "freq", .type = "mean")
#' vis(pr, "freq", .by = "Status", .meta = immdata$meta)
vis_public_frequencies <- function(.data, .by = NA, .meta = NA,
.type = c("boxplot", "none", "mean")) {
.type <- .type[1]
# ToDo: make it work with V genes
melted_pr <- reshape2::melt(.data, id.vars = colnames(.data)[1:(match("Samples", colnames(.data)))])
colnames(melted_pr)[1] <- "Sequence"
colnames(melted_pr)[ncol(melted_pr) - 1] <- "Sample"
group_res <- process_metadata_arguments(melted_pr, .by, .meta)
group_column <- group_res$name
melted_pr$Group <- group_res$group_column
if (.type == "boxplot") {
p <- ggplot() +
geom_boxplot(aes(y = value, x = as.factor(Samples), col = Group), data = melted_pr) +
scale_y_log10() +
ggtitle("Distribution of public clonotype frequencies") +
xlab("Number of shared samples") +
ylab("Public clonotype frequencies")
} else if (.type == "mean") {
melted_pr <- melted_pr %>%
group_by(Sequence, Samples) %>%
summarise(Value = mean(value, na.rm = TRUE)) %>%
as.data.table()
p <- ggplot() +
geom_jitter(aes(x = Value, y = Samples), data = melted_pr) +
scale_x_log10() +
ggtitle("Mean frequencies of public clonotypes") +
ylab("Number of shared samples") +
xlab("Public clonotype frequencies")
} else {
# ToDo: add the .verbose argument for this
message("Warning! Visualising ", sum(!is.na(melted_pr$value)), ' points. Too many points may take a while to visualise depending on your hardware. We highly recommend you to use other types: "mean" or "boxplot"')
p <- ggplot() +
geom_jitter(aes(x = value, y = Samples, col = Group), data = melted_pr) +
scale_x_log10() +
ggtitle("Distribution of public clonotype frequencies") +
ylab("Number of shared samples") +
xlab("Public clonotype frequencies")
}
# tmp = as_tibble(data.frame(N_samples = .data$Samples,
# Value = rowMeans(public_matrix(.data), na.rm = TRUE)))
# ggplot(aes(x=Value, y=N_samples, color=N_samples), data=tmp) + geom_jitter() +
# scale_x_log10() +
# xlab("Public clonotype frequency") + ylab("Number of samples") +
# ggtitle("Public clonotype frequencies") +
# theme_bw()
p + theme_pubr(legend = "right") + theme_cleveland2()
}
#' Visualisation of public clonotypes
#'
#' @concept pubrep
#'
#' @name vis_public_clonotypes
#'
#' @importFrom grid rectGrob gpar
#' @importFrom stats lm
#' @importFrom patchwork wrap_plots plot_annotation
#'
#' @description Visualise correlation of public clonotype frequencies in pairs of repertoires.
#'
#' @param .data Public repertoire data - an output from the \link{pubRep} function.
#'
#' @param .x.rep Either indices of samples or character vector of sample names
#' for the x-axis. Must be of the same length as ".y.rep".
#'
#' @param .y.rep Either indices of samples or character vector of sample names
#' for the y-axis. Must be of the same length as ".x.rep".
#'
#' @param .title The text for the title of the plot.
#'
#' @param .ncol An integer number of columns to print in the grid of pairs of repertoires.
#'
#' @param .point.size.modif An integer value that is a modifier of the point size.
#' The larger the number, the larger the points.
#'
#' @param .cut.axes If TRUE then axes limits become shorter.
#'
#' @param .density If TRUE then displays density plot for distributions of clonotypes
#' for each sample. If FALSE then removes density plot from the visualisation.
#'
#' @param .lm If TRUE then fit a linear model and displays an R adjusted coefficient
#' that shows how similar samples are in terms of shared clonotypes.
#'
#' @param .radj.size An integer value, that defines the size of the The text
#' for the R adjusted coefficient.
#'
#' @return
#' A ggplot2 object.
#'
#' @seealso \link{pubRep}, \link{vis.immunr_public_repertoire}
#'
#' @examples
#' data(immdata)
#' pr <- pubRep(immdata$data, .verbose = FALSE)
#' vis(pr, "clonotypes", 1, 2)
vis_public_clonotypes <- function(.data, .x.rep = NA, .y.rep = NA,
.title = NA, .ncol = 3,
.point.size.modif = 1, .cut.axes = TRUE,
.density = TRUE, .lm = TRUE, .radj.size = 3.5) {
.shared.rep <- .data
mat <- public_matrix(.shared.rep)
if (is.na(.x.rep) && is.na(.y.rep)) {
ps <- list()
for (i in 1:ncol(mat)) {
for (j in 1:ncol(mat)) {
ps <- c(ps, list(vis_public_clonotypes(.shared.rep, i, j, "",
.point.size.modif = .point.size.modif,
.cut.axes = .cut.axes, .density = .density, .lm = .lm, .radj.size = .radj.size
)))
}
}
do.call(wrap_plots, c(ps, ncol = .ncol)) + plot_annotation(title = .title)
} else if (is.na(.x.rep)) {
ps <- lapply(1:ncol(mat), function(i) {
vis_public_clonotypes(.shared.rep, i, .y.rep, "",
.point.size.modif = .point.size.modif,
.cut.axes = .cut.axes, .density = .density, .lm = .lm
)
})
do.call(wrap_plots, c(ps, ncol = .ncol)) + plot_annotation(title = .title)
} else if (is.na(.y.rep)) {
ps <- lapply(1:ncol(mat), function(j) {
vis_public_clonotypes(.shared.rep, .x.rep, j, "",
.point.size.modif = .point.size.modif,
.cut.axes = .cut.axes, .density = .density, .lm = .lm
)
})
do.call(wrap_plots, c(ps, ncol = .ncol)) + plot_annotation(title = .title)
} else {
if (!is.character(.x.rep)) {
.x.rep <- colnames(mat)[.x.rep]
}
if (!is.character(.y.rep)) {
.y.rep <- colnames(mat)[.y.rep]
}
if (.x.rep == .y.rep) {
return(rectGrob(gp = gpar(col = "white")))
}
df <- data.frame(cbind(mat[, .x.rep], mat[, .y.rep]))
df[, 1] <- df[, 1] / sum(df[, 1], na.rm = TRUE)
df[, 2] <- df[, 2] / sum(df[, 2], na.rm = TRUE)
df_full <- df
df <- df[!is.na(df[, 1]) & !is.na(df[, 2]), ]
freq <- log10(sqrt(as.numeric(df[, 1]) * df[, 2])) / 2
names(df) <- c("Xrep", "Yrep")
names(df_full) <- c("Xrep", "Yrep")
pnt.cols <- log(df[, 1] / df[, 2])
suppressWarnings(pnt.cols[pnt.cols > 0] <- pnt.cols[pnt.cols > 0] / max(pnt.cols[pnt.cols > 0]))
suppressWarnings(pnt.cols[pnt.cols < 0] <- -pnt.cols[pnt.cols < 0] / min(pnt.cols[pnt.cols < 0]))
if (.cut.axes) {
mat.lims <- c(min(as.matrix(df_full), na.rm = TRUE), max(as.matrix(df_full), na.rm = TRUE))
} else {
mat.lims <- c(min(as.matrix(df_full), na.rm = TRUE), 1)
}
empty <- ggplot() +
geom_point(aes(1, 1), colour = "white") +
theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank()
)
min_df <- min(floor(log10(min(df_full[, 1], na.rm = TRUE))), floor(log10(min(df_full[, 2], na.rm = TRUE))))
max_df <- max(trunc(log10(max(df_full[, 1], na.rm = TRUE))), trunc(log10(max(df_full[, 2], na.rm = TRUE))))
breaks_values <- 10^seq(min_df, 1)
breaks_labels <- format(log10(breaks_values), scientific = FALSE)
grey_col <- "#CCCCCC"
points <- ggplot() +
geom_point(aes(x = Xrep, y = Yrep, size = freq, fill = pnt.cols), data = df, shape = 21) +
scale_radius(range = c(.point.size.modif, .point.size.modif * 6)) +
geom_abline(intercept = 0, slope = 1, linetype = "dashed") +
theme_linedraw() +
scale_fill_distiller(palette = "RdBu") +
scale_x_log10(breaks = breaks_values, labels = breaks_labels, lim = mat.lims, expand = c(.015, .015)) +
scale_y_log10(breaks = breaks_values, labels = breaks_labels, lim = mat.lims, expand = c(.015, .015)) +
theme(legend.position = "none") +
xlab(.x.rep) +
ylab(.y.rep)
if (!is.na(.title)) {
points <- points + ggtitle(.title)
}
if (.lm) {
adj.R.sq <- summary(lm(Yrep ~ Xrep, df))$adj.
points <- points +
geom_smooth(aes(x = Xrep, y = Yrep), method = "lm", data = df, fullrange = TRUE, colour = "grey20", size = .5) +
geom_text(aes(
x = max(df_full, na.rm = TRUE) / 4,
y = min(df_full, na.rm = TRUE),
label = paste0("R^2(adj.) = ", as.character(round(adj.R.sq, 2)))
), size = .radj.size)
# ggtitle(paste0("R^2(adj.) = ", as.character(round(adj.R.sq, 2))))