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Comparative functions added to the vignette, vignette data generated
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| #' Plot the broken stick | ||
| #' | ||
| #' @description | ||
| #' Plot a broken stick to compare among a large set of species | ||
| #' the heterogeneity in the distribution of recombination rates along the genome | ||
| #' For a description of the method, see Brazier, T., & Glémin, S. (2022). | ||
| #' Diversity and determinants of recombination landscapes in flowering plants. | ||
| #' PLOS Genetics, 18(8), Article 8. https://doi.org/10.1371/journal.pgen.1010141 | ||
| #' | ||
| #' @param marey a `comparative_marey_map` object | ||
| #' @param k the number of segments (default = 10) | ||
| #' @param method the method to infer the breakpoint of segments, either `strict` to cut a marker position, or `segmented` to interpolate segments breakpoints | ||
| #' @param plot (logical) whether to plot the broken stick directly or return a data frame (default = `TRUE` will plot the figure) | ||
| #' | ||
| #' | ||
| #' @details | ||
| #' Broken Stick model (see White & Hill 2020 for details) | ||
| #' K segments of equal genomic size (Mb) and evaluate the genetic relative size | ||
| #' | ||
| #' @returns a `ggplot` of the broken stick or a data frame, depending on the value of `plot` | ||
| #' | ||
| #' @import segmented | ||
| #' @import ggplot2 | ||
| #' @import reshape2 | ||
| #' | ||
| #' @export | ||
| #' | ||
| brokenstick = function(marey, k = 10, method = "strict", plot = TRUE) { | ||
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| # the list of set and names to process | ||
| s = marey$set | ||
| n = marey$map | ||
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| list_bs = list() | ||
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| for (i in 1:length(s)) { | ||
| idx = (s == s[i] & n == n[i]) | ||
| cat(s[i], n[i], "\n") | ||
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| subs = subset_comparative_marey(marey, subset = idx) | ||
| subs = comparative_marey_to_dataframe(subs) | ||
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| bs = list(brokenstick_one_map(subs, k = k, method = method)) | ||
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| list_bs = c(list_bs, bs) | ||
| } | ||
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| df = data.frame(set = s, | ||
| name = n) | ||
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| res = as.data.frame(do.call("rbind", list_bs)) | ||
| df = cbind(df, res) | ||
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| # Format columns in proper way | ||
| colnames(df) = c('set', 'name', as.character(c(1:k))) | ||
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| # Tidy data frame | ||
| # brokenstick = brokenstick[!(is.na(df$p1) | is.na(df$p2) | is.na(df$p3) | is.na(df$p4) | is.na(df$p5) | df(brokenstick$p6) | is.na(df$p7) | is.na(df$p8) | is.na(df$p9) | is.na(df$p10)),] | ||
| brokenstick = melt(df) | ||
| brokenstick$sample = paste(brokenstick$set, brokenstick$chromosome, sep = "_") | ||
| colnames(brokenstick)=c("set","chromosome","segment","proportion.length","sample") | ||
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| # Set a vector of gradient color | ||
| # Value of color is simply expected - p, the departure from the expected proportion 1/k | ||
| brokenstick$color = (1/k) - brokenstick$proportion.length | ||
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| # Besides, estimates the ratio expected/observed (longer than expected will have lower relative recombination rate) | ||
| brokenstick$ratio = brokenstick$proportion.length/(1/k) | ||
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| p = ggplot(data = brokenstick, aes(x=sample, y=proportion.length, fill = log10(ratio)))+ | ||
| geom_bar(stat='identity', width = 1) + | ||
| # scale_fill_manual(values = color) + | ||
| scale_fill_viridis_c(breaks = c(-1, 0, 1), labels = c("-1", "0", "1"), direction = -1, limits = c(-1, 1), | ||
| values = c(0,0.7,0.8,1), option = "D") + | ||
| # scale_fill_gradient2() + | ||
| facet_grid(~set, scales = "free", space="free_x") + | ||
| labs(x="Chromosome", y="Proportion of\ntotal physical length", fill="Segment") + | ||
| theme(axis.line = element_blank(), | ||
| # axis.line.x = element_blank(), # No x axis | ||
| panel.grid.major = element_blank(), | ||
| panel.grid.minor = element_blank(), | ||
| panel.border = element_blank(), | ||
| panel.background = element_blank(), | ||
| plot.title = element_text(color="black", size=24, face="bold.italic",hjust = 0.5), | ||
| plot.subtitle = element_text(color="black",size=24,hjust = 0.5), | ||
| axis.title.x = element_text(color="black", size=24), | ||
| axis.title.y = element_text(color="black", size=24), | ||
| axis.text=element_text(size=24, colour="black"), | ||
| axis.text.x=element_blank(), # No samples names | ||
| axis.ticks.x=element_blank(), # No x axis | ||
| strip.text=element_text(size=18, colour="black", angle = 90), | ||
| legend.key = element_rect(fill = "white", linewidth = 1), | ||
| legend.text=element_text(size=24), | ||
| legend.title=element_text(size=24)) | ||
| p | ||
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| if (plot) { | ||
| p | ||
| } else { | ||
| return(brokenstick) | ||
| } | ||
| } | ||
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| #' Compute the broken stick model for a single Marey map | ||
| #' | ||
| #' @description | ||
| #' Estimate the proportions of a broken stick model for a `comparative_marey_map` object | ||
| #' i.e. proportion of relative genetic length (cM) in k segments of equal genomic size (bp) along the chromosome | ||
| brokenstick_one_map = function(marey, k = 10, method = "strict") { | ||
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| marey$map = as.character(marey$map) | ||
| marey$mkr = as.character(marey$mkr) | ||
| marey$phys = as.numeric(as.character(marey$phys)) | ||
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| # Create empty dataset | ||
| stick_proportions = as.data.frame(matrix(NA, nrow = 1, ncol = k)) | ||
| colnames(stick_proportions) = paste("p", c(1:k), sep ="") | ||
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| if (method == "strict") { | ||
| # Segment in k segments of equal genomic size (Mb) | ||
| # Size of segment is total genomic size divided by k | ||
| segment_size = round(max(marey$phys, na.rm = TRUE)/k) | ||
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| # A vector of segments genetic size pi (proportion of total length) | ||
| p = numeric(k) | ||
| for (i in 1:k) { | ||
| # print(max(which(marey$gen < segment_size*i))) | ||
| # p[i] = marey$phys[max(which(marey$gen < segment_size*i))] | ||
| p[i] = max(marey$gen[which(marey$phys < segment_size*i)], na.rm = TRUE) | ||
| # p[i] = max(marey$gen[marey$gen < segment_size*i]) | ||
| # p[i] = marey$phys[max(which(marey$gen < quantile(marey$gen, i/k)))] | ||
| # p[i] = quantile(marey$gen, i/k) | ||
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| if (i > 1) { | ||
| p[i] = p[i] - sum(p[i-1:(i-1)]) | ||
| } | ||
| } | ||
| # END OF METHOD STRICT | ||
| } | ||
| if (method == "segmented") { | ||
| res = c() | ||
| lin.mod <- lm(phys~gen, data = marey) | ||
| # Estimated breakpoints in genomic distances | ||
| res = try(segmented.mod <- segmented(lin.mod, seg.Z = ~phys, npsi=(k-1), control = seg.control(n.boot = 100, fix.npsi=TRUE))) | ||
| # In cases of segmentation failure | ||
| if (class(res)[1] == "try-error") { | ||
| # Failure because not enough data | ||
| breakpoints = c(NA, NA, NA) | ||
| } else { | ||
| breakpoints = c(summary(segmented.mod)$psi[,2], max(marey$phys, na.rm = TRUE)) | ||
| } | ||
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| for (i in 1:k) { | ||
| p[i] = max(marey$gen[marey$phys < breakpoints[i]]) | ||
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| if (i > 1) { | ||
| p[i] = p[i] - sum(p[i-1:(i-1)]) | ||
| } | ||
| } | ||
| # END OF METHOD SEGMENTED | ||
| } | ||
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| # stick_proportions is a proportion of total physical length | ||
| stick_proportions = p/sum(p) | ||
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| return(stick_proportions) | ||
| } |
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