diff --git a/.github/.gitignore b/.github/.gitignore new file mode 100644 index 0000000..2d19fc7 --- /dev/null +++ b/.github/.gitignore @@ -0,0 +1 @@ +*.html diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml new file mode 100644 index 0000000..c7ed99d --- /dev/null +++ b/.github/workflows/R-CMD-check.yaml @@ -0,0 +1,107 @@ +on: + push: + branches: + - master + pull_request: + branches: + - master + +name: R-CMD-check + +jobs: + R-CMD-check: + runs-on: ${{ matrix.config.os }} + defaults: + run: + working-directory: elsasserlib + + name: ${{ matrix.config.os }} (${{ matrix.config.r }}) + + strategy: + fail-fast: false + matrix: + config: + - {os: ubuntu-latest, r: 'release'} + - {os: macOS-latest, r: 'release'} + - {os: windows-latest, r: 'release'} + + env: + R_REMOTES_NO_ERRORS_FROM_WARNINGS: true + RSPM: ${{ matrix.config.rspm }} + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + + steps: + - uses: actions/checkout@v2 + + - uses: r-lib/actions/setup-r@master + with: + r-version: ${{ matrix.config.r }} + + - uses: r-lib/actions/setup-pandoc@master + + - name: Query dependencies + run: | + install.packages('remotes') + saveRDS(remotes::dev_package_deps(dependencies = TRUE), "../.github/depends.Rds", version = 2) + writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), "../.github/R-version") + shell: Rscript {0} + + - name: Cache R packages + if: runner.os != 'Windows' + uses: actions/cache@v1 + with: + path: ${{ env.R_LIBS_USER }} + key: ${{ runner.os }}-${{ hashFiles('../.github/R-version') }}-1-${{ hashFiles('../.github/depends.Rds') }} + restore-keys: ${{ runner.os }}-${{ hashFiles('../.github/R-version') }}-1- + + - name: Install Linux system dependencies + if: runner.os == 'Linux' + env: + RHUB_PLATFORM: linux-x86_64-ubuntu-gcc + run: | + sudo apt-get install libcurl4-openssl-dev + Rscript -e "remotes::install_github('r-hub/sysreqs')" + sysreqs=$(Rscript -e "cat(sysreqs::sysreq_commands('DESCRIPTION'))") + sudo -s eval "$sysreqs" + + - name: Manually install BioConductor dependencies + run: | + install.packages("BiocManager") + BiocManager::install(c("rtracklayer", "GenomeInfoDb", "GenomicRanges", "BSgenome.Mmusculus.UCSC.mm9", "BSgenome.Hsapiens.UCSC.hg38")) + shell: Rscript {0} + working-directory: elsasserlib + + - name: Install dependencies + run: | + remotes::install_deps(dependencies = TRUE) + remotes::install_cran("rcmdcheck") + shell: Rscript {0} + working-directory: elsasserlib + + - name: Session info + run: | + options(width = 100) + pkgs <- installed.packages()[, "Package"] + sessioninfo::session_info(pkgs, include_base = TRUE) + shell: Rscript {0} + working-directory: elsasserlib + + - name: Check + env: + _R_CHECK_CRAN_INCOMING_: false + run: rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), error_on = "warning", check_dir = "check") + shell: Rscript {0} + working-directory: elsasserlib + + - name: Show testthat output + if: always() + run: find check -name 'testthat.Rout*' -exec cat '{}' \; || true + shell: bash + working-directory: elsasserlib + + - name: Upload check results + if: failure() + uses: actions/upload-artifact@master + with: + name: ${{ runner.os }}-r${{ matrix.config.r }}-results + path: check diff --git a/.gitignore b/.gitignore index fae8299..33f57e3 100644 --- a/.gitignore +++ b/.gitignore @@ -37,3 +37,5 @@ vignettes/*.pdf # R Environment Variables .Renviron + +Rplots.pdf diff --git a/COPYING b/COPYING new file mode 100644 index 0000000..f288702 --- /dev/null +++ b/COPYING @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md index 7b2c270..2ea8ed2 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,41 @@ +![](https://github.com/elsasserlab/labcode/workflows/R-CMD-check/badge.svg) + # Lab code repository -The aim of this repository is to have a centralized place to share and develop utilities that could be of use to other lab members. In this sense, it is a mixed code repository that does not have a specific functionality. If, within it, some tool grows enough to have an entity of its own, it may be eventually moved to its own repository. +The aim of this repository is to have a centralized place to share and develop +utilities that could be of use to other lab members. In this sense, it is a +mixed code repository that does not have a specific functionality. If, within +it, some tool grows enough to have an entity of its own, it may be eventually +moved to its own repository. ## Structure of the repository The repository is (will be) structured as follows: -- `notebooks`: R and possibly python notebooks that either explain functionality available on this repository or useful analyses that can be done within the context of our lab, even if they do not represent additional code development. -- `snippets`: Pieces of code that are useful to share but do not fit (yet) in a library/R package/tool. These can come or have a GitHub gist attached to them. +- `notebooks`: R and possibly python notebooks that either explain functionality + available on this repository or useful analyses that can be done within + the context of our lab, even if they do not represent additional code + development. +- `snippets`: Pieces of code that are useful to share but do not fit (yet) in a + library/R package/tool. These can come or have a GitHub gist attached to + them. - `doc`: Documentation. -- `elsasserlib`: Mixed utils R package that contains code that can be useful to perform usual analyses in the lab. +- `elsasserlib`: Mixed utils R package that contains code that can be useful to + perform usual analyses in the lab. + +## Including code in the repo +If you have some code you think it may be useful for the lab, you're welcome to +include it! If it does not match any category in the proposed structure above, +we can of course include new things. + +If you are familiar with how GitHub works, you are welcome to add a pull request +with your code! + +In order to be somewhat consistent, I ask you to follow the same style as the +code that is already there. Mostly, I follow the conventions stated in the +tidyverse style guide: https://style.tidyverse.org/ which seemed reasonable +enough. -## Everybody's code welcome! -If you have some code you think it may be useful for the lab, you're welcome to include it! If it does not match any category in the proposed structure above, we can of course include new things. ## Wishlists, suggestions and bug reports also welcome -Please, add issues to the repository if you encounter any problems or you have suggestions or wishes about functionalities to add. If you are unsure about how to do any of this, feel free to talk to me :) +Please, add issues to the repository if you encounter any problems or you have +suggestions or wishes about functionalities to add. If you are unsure about how +to do any of this, feel free to talk to me :) diff --git a/elsasserlib/.Rbuildignore b/elsasserlib/.Rbuildignore index a32bacb..6216e7f 100644 --- a/elsasserlib/.Rbuildignore +++ b/elsasserlib/.Rbuildignore @@ -2,3 +2,5 @@ ^\.Rproj\.user$ ^doc$ ^Meta$ +^\.github$ +^README.Md diff --git a/elsasserlib/.gitignore b/elsasserlib/.gitignore index 73bae41..ef86ad0 100644 --- a/elsasserlib/.gitignore +++ b/elsasserlib/.gitignore @@ -1,3 +1,4 @@ inst/doc Meta doc +Rplots.pdf diff --git a/elsasserlib/DESCRIPTION b/elsasserlib/DESCRIPTION index 3a40096..7512aa8 100644 --- a/elsasserlib/DESCRIPTION +++ b/elsasserlib/DESCRIPTION @@ -1,31 +1,53 @@ Package: elsasserlib Type: Package Title: General utilities used within Elsasser lab -Version: 0.1.0 -Authors@R: person("Carmen", "Navarro", email = "carmen.navarro@scilifelab.se", - role = c("aut", "cre")) +Version: 1.0.7 +Authors@R: c( + person("Carmen", "Navarro", + email = "carmen.navarro@scilifelab.se", + role = c("aut", "cre") + ), + person("Rui", "Shao", + email = "rui.shao@scilifelab.se", + role = c("aut") + ) + ) Maintainer: Carmen Navarro Description: Set of mixed utilities of use in the context of Simon Elsasser's lab at SciLifeLab. This includes genomics-related plots, analyses of outputs of experiments and methods developed within the lab. -License: MIT -Depends: R (>= 3.6.1) +License: GPL-3 + file LICENSE +Depends: R (>= 3.6) biocViews: Imports: rtracklayer, + rmarkdown, dplyr, + tidyselect, + grDevices, + stats, + methods, ggplot2, + stringr, reshape2, GenomeInfoDb, + GenomeInfoDbData, GenomicRanges, + IRanges, purrr, + prettydoc, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Hsapiens.UCSC.hg38, - scales + scales, + RColorBrewer, + pheatmap Encoding: UTF-8 LazyData: true -RoxygenNote: 7.1.0 +RoxygenNote: 7.1.1 Suggests: knitr, - rmarkdown + testthat, + mockery VignetteBuilder: knitr +URL: https://github.com/elsasserlab/labcode +BugReports: https://github.com/elsasserlab/labcode/issues diff --git a/elsasserlib/LICENSE b/elsasserlib/LICENSE index 6a9a0b2..f288702 100644 --- a/elsasserlib/LICENSE +++ b/elsasserlib/LICENSE @@ -1,21 +1,674 @@ -MIT License - -Copyright (c) 2020 Carmen Navarro @ SciLifeLab - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/elsasserlib/NAMESPACE b/elsasserlib/NAMESPACE index 2301739..6f328e5 100644 --- a/elsasserlib/NAMESPACE +++ b/elsasserlib/NAMESPACE @@ -3,13 +3,27 @@ export(build_bins) export(bw_bed) export(bw_bins) +export(bw_profile) +export(mean_ratio_norm) export(palette_categorical) +export(plot_bw_bed_summary_heatmap) +export(plot_bw_bins_scatter) +export(plot_bw_bins_violin) +export(plot_bw_profile) +export(quantile_norm) +export(render_bw_report) +export(size_factor_norm) export(theme_elsasserlab_print) export(theme_elsasserlab_screen) +import(ggplot2) +importFrom(BSgenome.Hsapiens.UCSC.hg38,BSgenome.Hsapiens.UCSC.hg38) importFrom(BSgenome.Mmusculus.UCSC.mm9,BSgenome.Mmusculus.UCSC.mm9) importFrom(GenomeInfoDb,seqinfo) +importFrom(GenomeInfoDb,sortSeqlevels) importFrom(GenomicRanges,makeGRangesFromDataFrame) importFrom(GenomicRanges,tileGenome) +importFrom(IRanges,subsetByOverlaps) +importFrom(RColorBrewer,brewer.pal) importFrom(dplyr,`%>%`) importFrom(dplyr,across) importFrom(dplyr,group_by_at) @@ -23,5 +37,16 @@ importFrom(ggplot2,rel) importFrom(ggplot2,theme) importFrom(ggplot2,theme_minimal) importFrom(ggplot2,unit) +importFrom(grDevices,colorRampPalette) +importFrom(grDevices,hcl) +importFrom(grDevices,rgb) +importFrom(methods,getMethod) +importFrom(pheatmap,pheatmap) +importFrom(reshape2,melt) +importFrom(rmarkdown,render) importFrom(rtracklayer,BigWigFile) importFrom(rtracklayer,import) +importFrom(rtracklayer,mcols) +importFrom(stats,median) +importFrom(stats,sd) +importFrom(stringr,str_sort) diff --git a/elsasserlib/R/bwplot.R b/elsasserlib/R/bwplot.R new file mode 100644 index 0000000..197ce34 --- /dev/null +++ b/elsasserlib/R/bwplot.R @@ -0,0 +1,418 @@ +#' Summary heatmap of a categorized BED file +#' +#' Make a summary heatmap where each cell contains an aggregated value of a +#' bigWig file from bwfiles and a category of a BED file (bedfile). The +#' provided BED file must have a name field that is valid (i.e. can be grouped, +#' representing some type of category, not a per-locus unique ID). +#' +#' @param labels Labels to use for in the plot for the bw files. +#' @param file_out Output the plot to a file. +#' @inheritParams bw_bed +#' @return A pheatmap object +#' @export +plot_bw_bed_summary_heatmap <- function(bwfiles, + bedfile, + bg_bwfiles = NULL, + labels = NULL, + aggregate_by = "true_mean", + norm_func = identity, + file_out = NA) { + + summary_values <- bw_bed(bwfiles, bedfile, + bg_bwfiles = bg_bwfiles, + aggregate_by = aggregate_by, + norm_func = norm_func + ) + + if (sum(summary_values) == 0) { + warning("All zero-values matrix. Using same background as bw input?") + } + + title <- paste("Coverage per region (", aggregate_by, ")") + title <- paste(title, "-", make_norm_label(substitute(norm_func), bg_bwfiles)) + summary_heatmap(t(summary_values), title = title, file_out = file_out) +} + +#' Bin-based scatterplot of a pair of bigWig files +#' +#' Plots a scatter plot from two given bigWig files and an optional set of BED +#' files as highlighted annotations. Bins are highlighted if there is at least +#' minoverlap base pairs overlap with any loci in BED file. +#' +#' If specifying minoverlap, you must take into account the bin_size parameter +#' and the size of the loci you are providing as BED file. +#' +#' Values in x and y axis can be normalized using background bigWig files +#' (usually input files). By default, the value shown will be x / bg_x per bin. +#' If norm_func_x or norm_func_y are provided, this can be changed to any given +#' function, for instance, if norm_func_x = log2, values on the x axis will +#' represent log2(x / bg_x) for each bin. +#' +#' Values that are invalid (NaN, Inf, -Inf) in doing such normalization will +#' be ignored and shown as warnings, as this is ggplot default behavior. +#' +#' @param x BigWig file corresponding to the x axis. +#' @param y BigWig file corresponding to the y axis. +#' @param bg_x BigWig file to be used as x axis background (us. input). +#' @param bg_y BigWig file to be used as y axis background (us. input). +#' @param highlight List of bed files to use as highlight for subgroups. +#' @param minoverlap Minimum overlap required for a bin to be highlighted +#' @param highlight_label Labels for the highlight groups. +#' If not provided, filenames are used. +#' @param norm_func_x Function to use after x / x_bg. +#' @param norm_func_y Function to use after y / y_bg. +#' @import ggplot2 +#' @inheritParams bw_bins +#' @return A ggplot object. +#' @export +plot_bw_bins_scatter <- function(x, + y, + bg_x = NULL, + bg_y = NULL, + bin_size = 10000, + per_locus_stat = "mean", + genome = "mm9", + highlight = NULL, + minoverlap = 0L, + highlight_label = NULL, + norm_func_x = identity, + norm_func_y = identity) { + + label_df <- function(df, name) { + data.frame(df, group = name) + } + + bins_values_x <- bw_bins(x, + bg_bwfiles = bg_x, + bin_size = bin_size, + genome = genome, + per_locus_stat = per_locus_stat, + norm_func = norm_func_x, + labels = "x" + ) + + bins_values_y <- bw_bins(y, + bg_bwfiles = bg_y, + bin_size = bin_size, + genome = genome, + per_locus_stat = per_locus_stat, + norm_func = norm_func_y, + labels = "y" + ) + + + bins_df <- cbind(data.frame(bins_values_x), + data.frame(bins_values_y)[, "y"] + ) + + colnames(bins_df) <- c(colnames(bins_df)[seq_len(ncol(bins_df) - 1)], "y") + bins_values <- makeGRangesFromDataFrame(bins_df, keep.extra.columns = TRUE) + + extra_plot <- NULL + + if (!is.null(highlight)) { + gr_list <- lapply(highlight, rtracklayer::import, format = "BED") + + subset_func <- purrr::partial( + IRanges::subsetByOverlaps, + x = bins_values, + minoverlap = minoverlap + ) + + bins_subset <- lapply(gr_list, subset_func) + subset_df <- lapply(bins_subset, data.frame) + + if (is.null(highlight_label)) { + highlight_label <- basename(highlight) + } + + df_values_labeled <- purrr::map2(subset_df, highlight_label, label_df) + highlight_values <- do.call(rbind, df_values_labeled) + + extra_plot <- geom_point( + data = highlight_values, + aes_string(x = "x", y = "y", color = "group"), + alpha = 0.8 + ) + } + + x_label <- paste(make.names(basename(x)), "-", + make_norm_label(substitute(norm_func_x), bg_x)) + + y_label <- paste(make.names(basename(y)), "-", + make_norm_label(substitute(norm_func_y), bg_y)) + + ggplot(bins_df, aes_string(x = "x", y = "y")) + + geom_point(color = "#cccccc", alpha = 0.8) + + theme_elsasserlab_screen(base_size = 18) + + xlab(x_label) + + ylab(y_label) + + ggtitle(paste("Bin coverage (bin_size = ", bin_size, ")", sep = "")) + + extra_plot +} + + +#' Bin-based violin plot of a set of bigWig files +#' +#' Plots a violin plot of bin distribution of a set of bigWig files optionally +#' overlaid with annotated bins. Bins overlapping loci of the provided BED +#' file will be shown as a jitter plot on top of the violin plot. +#' +#' @param highlight BED file to use as highlight for subgroups. +#' @param minoverlap Minimum overlap required for a bin to be highlighted. +#' @inheritParams bw_bins +#' @import ggplot2 +#' @importFrom reshape2 melt +#' @return A ggplot object. +#' @export +plot_bw_bins_violin <- function(bwfiles, + bg_bwfiles = NULL, + labels = NULL, + bin_size = 10000, + per_locus_stat = "mean", + genome = "mm9", + highlight = NULL, + minoverlap = 0L, + norm_func = identity) { + + bins_values <- bw_bins(bwfiles, + bg_bwfiles = bg_bwfiles, + labels = labels, + bin_size = bin_size, + genome = genome, + per_locus_stat = per_locus_stat, + norm_func = norm_func) + + bins_df <- data.frame(bins_values) + bwnames <- colnames(mcols(bins_values)) + bin_id <- c("seqnames", "start", "end") + + melted_bins <- melt(bins_df[, c(bin_id, bwnames)], id.vars = bin_id) + title <- paste("Global bin distribution (binsize=", bin_size, ")", sep = "") + extra_plot <- NULL + + if (!is.null(highlight)) { + title <- paste(title, "vs", basename(highlight)) + gr_highlight <- rtracklayer::import(highlight, format = "BED") + + overlapping_values <- IRanges::subsetByOverlaps( + bins_values, + gr_highlight, + minoverlap = minoverlap + ) + + overlap_df <- data.frame(overlapping_values) + + melted_highlight <- melt(overlap_df[, c(bin_id, bwnames)], id.vars = bin_id) + + extra_plot <- geom_jitter(data = melted_highlight, + aes_string(x = "variable", y = "value", color = "variable"), + alpha = 0.7 + ) + } + + y_label <- make_norm_label(substitute(norm_func), bg_bwfiles) + + # Avoid R_CMD_CHECK note on variable / value with aes + ggplot(melted_bins, aes_string(x = "variable", y = "value")) + + geom_violin(fill = "#cccccc") + + theme_elsasserlab_screen(base_size = 18) + + xlab("") + + ylab(y_label) + + ggtitle(title) + + theme(legend.position = "none", + axis.text.x = element_text(angle = 45, hjust = 1)) + + extra_plot +} + +#' Profile plot of a set of bigWig files +#' +#' Plots a profile of a set of bigWig files over a set of loci in a BED file. +#' +#' @param show_error Show standard error. +#' @inheritParams bw_profile +#' @import ggplot2 +#' @return A ggplot object. +#' @export +plot_bw_profile <- function(bwfiles, + bedfile, + bg_bwfiles = NULL, + mode = "stretch", + bin_size = 100, + upstream = 2500, + downstream = 2500, + ignore_strand = FALSE, + show_error = FALSE, + norm_func = identity) { + + values <- bw_profile(bwfiles, bedfile, + bg_bwfiles = bg_bwfiles, + mode = mode, + bin_size = bin_size, + upstream = upstream, + downstream = downstream, + ignore_strand = ignore_strand, + norm_func = norm_func + ) + + y_label <- make_norm_label(substitute(norm_func), bg_bwfiles) + + nrows <- max(values$index) + upstream_nbins <- floor(upstream / bin_size) + downstream_nbins <- floor(downstream / bin_size) + + # index value starts at 1 + axis_breaks <- c(1, upstream_nbins, nrows) + axis_labels <- c(paste("-", upstream / 1000, "kb", sep = ""), + mode, + paste("+", downstream / 1000, "kb", sep = "")) + + lines <- axis_breaks[2] + + if (mode == "stretch") { + axis_breaks <- c(1, upstream_nbins, nrows - downstream_nbins, nrows) + + axis_labels <- c(paste("-", upstream / 1000, "kb", sep = ""), + "start", "end", + paste("+", downstream / 1000, "kb", sep = "")) + + lines <- axis_breaks[2:3] + + } + + loci <- length(import(bedfile, format = "BED")) + x_title <- paste(basename(bedfile), "-", loci, "loci", sep = " ") + + p <- ggplot(values, + aes_string( + x = "index", + y = "mean", + color = "sample", + fill = "sample" + )) + + geom_line(size = 0.8) + + geom_vline( + xintercept = lines, + linetype = "dashed", + color = "#cccccc", + alpha = 0.8 + ) + + scale_x_continuous(breaks = axis_breaks, labels = axis_labels) + + xlab(x_title) + + ylab(y_label) + + ggtitle("Profile plot") + + theme_elsasserlab_screen(base_size = 18) + + theme( + legend.position = c(0.80, 0.90), + legend.direction = "vertical", + legend.title = element_blank() + ) + + if (show_error) { + p <- + p + geom_ribbon( + aes( + x = index, + ymin = mean - sderror, + ymax = mean + sderror + ), + color = NA, + alpha = 0.3 + ) + } + + p +} + + +#' Plot a pretty heatmap using pheatmap library +#' +#' This function ignores NA values to calculate min and max values. +#' +#' @param values Value matrix. +#' @param title Plot title. +#' @param size Square size in points. +#' @param file_out Optional file output. +#' @importFrom RColorBrewer brewer.pal +#' @importFrom pheatmap pheatmap +#' @importFrom grDevices colorRampPalette +#' @return pheatmap object +summary_heatmap <- function(values, title, size = 35, file_out = NA) { + bcolor <- "white" + + breakslist <- calculate_breakslist(values) + html_colors <- rev(RColorBrewer::brewer.pal(n = 7, name = "RdBu")) + palette <- colorRampPalette(html_colors)(length(breakslist)) + + cellsize_inches <- size / 72 + + margin <- 4 + plot_width_inches <- cellsize_inches * ncol(values) + margin + plot_height_inches <- cellsize_inches * nrow(values) + margin + + pheatmap( + values, + main = title, + cellwidth = size, + cluster_rows = FALSE, + cluster_cols = FALSE, + cellheight = size, + border_color = bcolor, + breaks = breakslist, + color = palette, + width = plot_width_inches, + height = plot_height_inches, + display_numbers = TRUE, + filename = file_out + ) +} + + +#' Calculate breaks list for the values in a matrix +#' +#' Given a numerical matrix, calculate breakslist to use in the summarized +#' heatmap plot. This is done in order to unify color scale regardless of +#' normalization used (mostly log vs linear), keeping white as the zero value. +#' +#' This function ignores NA values to calculate min and max values. +#' +#' @param values Value matrix. +#' @return Sequence of breaks used by heatmap function +calculate_breakslist <- function(values) { + # Compute the largest deviation from zero ignoring NAs + maxmat <- values + maxmat[is.na(maxmat)] <- -Inf + maxval <- max(maxmat) + + minmat <- values + minmat[is.na(minmat)] <- Inf + minval <- min(minmat) + + breaklim <- ceiling(abs(max(abs(c(minval, maxval))))) + + # Compute a reasonable amount of steps + nsteps <- 21.0 + stepsize <- 2 * breaklim / nsteps + + breakslist <- seq(-breaklim, +breaklim, by = stepsize) + breakslist +} + + +#' Generate a human-readable normalization function string +#' +#' @param f String representing normalization function. +#' @param bg Background file. +#' +#' @return A string describing normalization. +make_norm_label <- function(f, bg) { + label <- "RPGC" + if (!is.null(bg)) { + if (f != "identity") { + label <- paste(f, "(", label, " / background)", sep = "") + } else { + label <- paste(label, " / background", sep = "") + } + } + label +} diff --git a/elsasserlib/R/bwtools.R b/elsasserlib/R/bwtools.R index 302afe1..d7f97a1 100644 --- a/elsasserlib/R/bwtools.R +++ b/elsasserlib/R/bwtools.R @@ -1,161 +1,789 @@ +#' Build a bed-scored GRanges object from a bigWig file list and a BED file. +#' +#' Build a scored GRanges object from a bigWig file list and a BED file. +#' The aggregating function (per locus) can be min, max, sd, mean. +#' +#' bwfiles and bg_bwfiles must have the same length. If you are using same +#' background for several files, then file paths must be repeated accordingly. +#' +#' Values can be normalized using background bigWig files (usually input +#' files). By default, the value obtained will be bigwig / bg_bigwig per bin, +#' per bigWig. +#' +#' If norm_func is specified, this can be changed to any given function, for +#' instance, if norm_func = log2, values will represent log2(bigwig / bg_bigwig) +#' per bin. +#' +#' @param bedfile BED file to intersect with the BigWig file. +#' @param aggregate_by Statistic to aggregate per group. If NULL, values are +#' not aggregated. This is the behavior by default. +#' @export +#' @inheritParams bw_bins +#' @importFrom rtracklayer import BigWigFile +#' @importFrom GenomeInfoDb sortSeqlevels +bw_bed <- function(bwfiles, + bedfile, + bg_bwfiles = NULL, + labels = NULL, + per_locus_stat = "mean", + aggregate_by = NULL, + norm_func = identity) { + + validate_filelist(bwfiles) + validate_filelist(bedfile) + + if (is.null(labels)) { + labels <- basename(bwfiles) + } + + bed <- import(bedfile) + bed <- sortSeqlevels(bed) + bed <- sort(bed, ignore.strand = FALSE) + result <- NULL + if (is.null(aggregate_by)) { + if (is.null(bg_bwfiles)) { + result <- multi_bw_ranges(bwfiles, labels, + granges = bed, + per_locus_stat = per_locus_stat + ) + } + else { + # Only want to normalize per-locus if not aggregating + result <- multi_bw_ranges_norm( + bwfiles, + bg_bwfilelist = bg_bwfiles, + labels = labels, + granges = bed, + per_locus_stat = per_locus_stat, + norm_func = norm_func + ) + } + } else { + result <- multi_bw_ranges_aggregated(bwfiles, + labels = labels, + granges = bed, + per_locus_stat = per_locus_stat, + aggregate_by = aggregate_by + ) + + if (!is.null(bg_bwfiles)) { + bg <- multi_bw_ranges_aggregated(bg_bwfiles, + labels = labels, + granges = bed, + per_locus_stat = per_locus_stat, + aggregate_by = aggregate_by + ) + + rows <- rownames(result) + result <- data.frame(norm_func(result[rows, labels] / bg[rows, labels])) + rownames(result) <- rows + colnames(result) <- labels + } + } + + result +} + #' Build a binned-scored GRanges object from a bigWig file #' #' Build a binned-scored GRanges object from a bigWig file. The aggregating -#' function can be min, max, sd, mean. +#' function per bin can be min, max, sd, mean. +#' +#' bwfiles and bg_bwfiles must have the same length. If you are using same +#' background for several files, then file paths must be repeated accordingly. +#' +#' Values can be normalized using background bigWig files (usually input +#' files). By default, the value obtained will be bigwig / bg_bigwig per bin, +#' per bigWig. +#' +#' If norm_func is specified, this can be changed to any given function, for +#' instance, if norm_func = log2, values will represent log2(bigwig / bg_bigwig) +#' per bin. #' -#' @param bwfile BigWig file to be summarized (or list). -#' @param colnames List of names to give to the mcols of the returned GRanges -#' object. If NULL, filenames are used (default). -#' @param stat Aggregating function (per locus). Mean by default. +#' @param bwfiles Path or array of paths to the bigWig files to be summarized. +#' @param bg_bwfiles Path or array of paths to the bigWig files to be used as +#' background. +#' @param labels List of names to give to the mcols of the returned GRanges +#' object. If NULL, file names are used. +#' @param per_locus_stat Aggregating function (per locus). Mean by default. #' Choices: min, max, sd, mean. -#' @param bsize Bin size. Default 10000. +#' @param bin_size Bin size. #' @param genome Genome. Available choices are mm9, hg38. -#' @return A GenomicRanges object with each bwfile as a metadata column named -#' after colnames. +#' @param selection A GRanges object to restrict binning to a certain set of +#' intervals. This is useful for debugging and improving performance of +#' locus specific analyses. +#' @param norm_func Function to apply to normalize bin values f(bw / bw_bg). +#' @return A GRanges object with each bwfile as a metadata column named +#' after labels, if provided, or after filenames otherwise. #' @export -bw_bins <- function(bwfiles, colnames=NULL, stat='mean', bsize=10000, genome='mm9') { - if (is.null(colnames)) { - colnames <- basename(bwfiles) - } - if (length(bwfiles) != length(colnames)) { - stop("BigWig file list and column names must have the same length.") +bw_bins <- function(bwfiles, + bg_bwfiles = NULL, + labels = NULL, + per_locus_stat = "mean", + bin_size = 10000, + genome = "mm9", + selection = NULL, + norm_func = identity) { + + validate_filelist(bwfiles) + + if (is.null(labels)) { + labels <- make.names(basename(bwfiles)) } - tiles <- build_bins(bsize=bsize, genome=genome) - multi_bw_ranges(bwfiles, colnames, tiles, per.locus.stat=stat) -} -#' Intersect a list of bw files with a GRanges object -#' -#' Build a binned-scored GRanges object from a list of bigWig files. The -#' aggregating function per locus can be min, max, sd, mean. -#' -#' @param bwfilelist BigWig file to be summarized. -#' @param col.names Names to be assigned to the columns -#' @param gr GRanges object to intersect -#' @param per.locus.stat Aggregating function per stat -#' @importFrom GenomicRanges makeGRangesFromDataFrame -multi_bw_ranges <- function(bwfilelist, colnames, gr, per.locus.stat='mean') { - summaries <- purrr::map(bwfilelist, bw_ranges, gr=gr, per.locus.stat=per.locus.stat) - with.names <- purrr::map2(summaries, colnames, rename_score) - df.fg <- Reduce(function(...) merge(..., all=TRUE), with.names) - if (length(bwfilelist) > 1) { - df.fg <- df.fg %>% dplyr::arrange(seqnames, start) + tiles <- build_bins(bin_size = bin_size, genome = genome) + + if (is.null(bg_bwfiles)) { + result <- multi_bw_ranges(bwfiles, labels, tiles, + per_locus_stat = per_locus_stat, + selection = selection + ) + } else { + result <- multi_bw_ranges_norm(bwfiles, bg_bwfiles, labels, tiles, + per_locus_stat = per_locus_stat, + selection = selection, + norm_func = norm_func + ) } - makeGRangesFromDataFrame(df.fg, keep.extra.columns = T) + result } -#' Build a scored GRanges object from a BED file. + +#' Calculate profile values of a bigWig file over a BED file. #' -#' Build a scored GRanges object from a bigWig file and a BED file. -#' The aggregating function (per locus) can be min, max, sd, mean. +#' Calculates profile values of a set of tracks over the loci speficied in a +#' BED file. Data points are taken each bin_size base pairs. #' -#' @param bwfiles BigWig file (or list) to be summarized. -#' @param bedfile BED file to intersect with the BigWig file. -#' @param colnames Column names of the score fields. Must have the same -#' length as bigwig file list. If not provided, colnames are the names of -#' the files in bwfiles. -#' @param per.locus.stat Aggregate per locus function. -#' @param aggregate.by Statistic to aggregate per group. If NULL, values are -#' not aggregated. This is the behavior by default. +#' Loci are aligned depending on mode parameter: +#' +#' - stretch. Aligns all starts and all ends, sort of stretching the loci. +#' The median of these lenghts is taken as the pseudo-length in order to show +#' a realistic plot when displayed. +#' +#' - start. All loci are aligned by start. +#' +#' - end. All loci are aligned by end. +#' +#' - center. All loci are aligned by center. +#' +#' @param bedfile BED file to summarize +#' @param mode How to handle differences in lengths across loci: +#' +#' stretch: Anchor each locus on both sides. +#' +#' start: Anchor all loci on start. +#' +#' end: Anchor all loci on end. +#' +#' center: Center all loci. +#' +#' @param bin_size Bin size. Length of bin in base pairs. The lower, the higher +#' the resolution. +#' @param upstream Number of base pairs to include upstream of loci. +#' @param downstream Number of base pairs to include downstream of loci. +#' @param ignore_strand Whether to use strand information in BED file. +#' @inheritParams bw_bins +#' @return a data frame in long format #' @export -#' @importFrom rtracklayer import BigWigFile -bw_bed <- function(bwfiles, bedfile, colnames=NULL, per.locus.stat='mean', aggregate.by=NULL) { - if (is.null(colnames)) { - colnames <- basename(bwfiles) +bw_profile <- function(bwfiles, + bg_bwfiles = NULL, + bedfile = NULL, + labels = NULL, + mode = "stretch", + bin_size = 100, + upstream = 2500, + downstream = 2500, + ignore_strand = FALSE, + norm_func = identity) { + + validate_filelist(bwfiles) + validate_filelist(bedfile) + granges <- rtracklayer::import(bedfile) + + if (bin_size <= 0) { + stop(paste("bin size must be a positive value:", bin_size)) } - if (length(bwfiles) != length(colnames)) { - stop("BigWig file list and column names must have the same length.") + + if (upstream <= 0) { + stop(paste("upstream size must be a positive value:", upstream)) } - bed <- import(bedfile) - result <- multi_bw_ranges(bwfiles, colnames, gr=bed, per.locus.stat=per.locus.stat) - if ( 'name' %in% names(mcols(bed)) ) { - result$name <- bed$name + + if (downstream <= 0) { + stop(paste("downstream size must be a positive value:", downstream)) + } + + if (bin_size > upstream || bin_size > downstream) { + stop("bin size must be smaller than flanking regions") } - if (! is.null(aggregate.by)) { - df <- aggregate_scores(result, group.col='name', aggregate.by=aggregate.by) - result <- df + + if (is.null(labels)) { + labels <- basename(bwfiles) } - result -} + if (length(bwfiles) != length(labels)) { + stop("labels and bwfiles must have the same length") + } -#' Rename score function of a GRanges object -#' -#' @param gr GRanges object -#' @param new.name Name to give to the column. -rename_score <- function(gr, new.name) { - colnames(mcols(gr)) <- replace(colnames(mcols(gr)), colnames(mcols(gr))=='score', new.name) - gr + calculate_bw_profile_fixed <- purrr::partial(calculate_bw_profile, + granges = granges, + mode = mode, + bin_size = bin_size, + upstream = upstream, + downstream = downstream, + ignore_strand = ignore_strand, + norm_func = norm_func + ) + + if (is.null(bg_bwfiles)) { + values_list <- purrr::map2(bwfiles, labels, calculate_bw_profile_fixed, + bg_bw = NULL + ) + } else { + values_list <- purrr::pmap(list(bwfiles, bg_bwfiles, labels), + calculate_bw_profile_fixed + ) + } + + values <- do.call(rbind, values_list) + values } -#' Build a bins GRanges object. + +#' Build a unscored bins GRanges object. #' #' Build a GRanges of bins of a given size, for a specific genome. Supported #' genomes (required for the package): mm9, hg38. #' -#' @param bsize Bin size. 10000 by default. -#' @param genome Genome used. mm9 by default. Valid values: mm9, hg38 -#' @return A GRanges object -#' @export +#' @param bin_size Bin size. +#' @param genome Genome. #' @importFrom GenomicRanges tileGenome #' @importFrom GenomeInfoDb seqinfo #' @importFrom BSgenome.Mmusculus.UCSC.mm9 BSgenome.Mmusculus.UCSC.mm9 -build_bins <- function(bsize=10000, genome='mm9') { +#' @importFrom BSgenome.Hsapiens.UCSC.hg38 BSgenome.Hsapiens.UCSC.hg38 +#' @return A GRanges object +#' @export +build_bins <- function(bin_size = 10000, genome = "mm9") { seq_lengths <- NULL - if (genome == 'mm9') { + if (genome == "mm9") { seq_lengths <- seqinfo(BSgenome.Mmusculus.UCSC.mm9) } else { - if (genome == 'hg38') { + if (genome == "hg38") { seq_lengths <- seqinfo(BSgenome.Hsapiens.UCSC.hg38) } else { stop("Supported genomes: mm9, hg38") } } - bins <- tileGenome(seq_lengths, - tilewidth=bsize, - cut.last.tile.in.chrom=T) + tileGenome(seq_lengths, tilewidth = bin_size, cut.last.tile.in.chrom = TRUE) +} + + +#' Intersect a list of bigWig files with a GRanges object +#' +#' Build a binned-scored GRanges object from a list of bigWig files. The +#' aggregating function per locus can be min, max, sd, mean. +#' +#' @param granges GRanges object to intersect +#' @importFrom GenomicRanges makeGRangesFromDataFrame +#' @importFrom GenomeInfoDb sortSeqlevels +#' @inheritParams bw_bins +#' @return A sorted GRanges object. +multi_bw_ranges <- function(bwfiles, + labels, + granges, + per_locus_stat = "mean", + selection = NULL) { + + if (length(bwfiles) != length(labels)) { + stop("BigWig file list and column names must have the same length.") + } + + summaries <- purrr::map(bwfiles, bw_ranges, + granges = granges, + per_locus_stat = per_locus_stat, + selection = selection + ) + + # granges_cbind sorts each element so it's safer to merge and no need to + # sort after + result <- granges_cbind(summaries, labels) + + # Include names if granges has them + if ("name" %in% names(mcols(granges))) { + result$name <- granges$name + } + + result +} + + +#' Intersect a list of bigWig files with a GRanges object and aggregate by name +#' +#' @inheritParams bw_bed +#' @param granges GRanges object to summarize. Should have a valid name field. +#' @return An aggregated dataframe +multi_bw_ranges_aggregated <- function(bwfiles, + labels, + granges, + per_locus_stat, + aggregate_by) { + + result <- multi_bw_ranges(bwfiles, labels, + granges = granges, + per_locus_stat = per_locus_stat + ) + + df <- aggregate_scores( + result, + group_col = "name", + aggregate_by = aggregate_by + ) + + natural_sort_by_field(df, "name") +} + + +#' Intersect a list of bigWig files with a GRanges object (with background) +#' +#' Intersect a list of bigWig files with a GRanges object and normalize values +#' with a set of background bigWig files. +#' +#' @param bwfilelist BigWig file list to be summarized. +#' @param bg_bwfilelist Background BigWig files. +#' @param granges GRanges object to intersect +#' @param selection A GRanges object to restrict analysis to. +#' @param norm_func Function to apply after bw/bg_bw. +#' @importFrom GenomicRanges makeGRangesFromDataFrame +#' @inheritParams bw_bins +#' @return a sorted GRanges object +multi_bw_ranges_norm <- function(bwfilelist, + bg_bwfilelist, + labels, + granges, + per_locus_stat = "mean", + selection = NULL, + norm_func = identity) { + + if (length(bwfilelist) != length(bg_bwfilelist)) { + stop("Background and signal bwfile lists must have the same length.") + } + + result <- multi_bw_ranges(bwfilelist, labels, granges, + per_locus_stat = per_locus_stat, + selection = selection + ) + + bg <- multi_bw_ranges(bg_bwfilelist, labels, granges, + per_locus_stat = per_locus_stat, + selection = selection + ) - bins + result_df <- data.frame(result) + bg_df <- data.frame(bg) + result_df[, labels] <- norm_func(result_df[, labels] / bg_df[, labels]) + + makeGRangesFromDataFrame(result_df, keep.extra.columns = TRUE) } + #' Score a GRanges object against a BigWig file #' -#' Build a scored GRanges object from a GRanges object and a BigWig file. +#' Build a scored GRanges object from a GRanges object and a single BigWig file. #' The aggregating function can be min, max, sd, mean. #' -#' @param bwfile BigWig file to be summarized. -#' @param gr GRanges object file to be summarized. -#' @param per.locus.stat Aggregating function (per locus). Mean by default. -#' Choices: min, max, sd, mean. These choices depend on rtracklayer library. +#' @param bwfile Path to a single BigWig file to be summarized. +#' @param granges GRanges object file to be summarized. #' @importFrom rtracklayer BigWigFile -#' @return Data frame with columns score and group.col (if provided). -bw_ranges <- function (bwfile, gr, per.locus.stat='mean') { +#' @importFrom IRanges subsetByOverlaps +#' @importFrom methods getMethod +#' @inheritParams bw_bins +#' @return GRanges with column score. +bw_ranges <- function(bwfile, + granges, + per_locus_stat = "mean", + selection = NULL) { + bw <- BigWigFile(bwfile) explicit_summary <- getMethod("summary", "BigWigFile") - unlist(explicit_summary(bw, gr, type=per.locus.stat)) + + if (! is.null(selection)) { + granges <- subsetByOverlaps(granges, selection) + } + + unlist(explicit_summary(bw, granges, type = per_locus_stat)) } + +#' Aggregate scores of a GRanges object on a field +#' +#' Aggregates scores of a GRanges object on a specific field. Used for summary +#' functions. #' -#' Aggregate scores of a GRanges object on a specific field +#' @param scored_granges A GRanges object with numerical metadata columns +#' @param group_col A column among the mcols that can be seen as a factor. +#' @param aggregate_by Function used to aggregate: mean, median, true_mean. +#' +#' true_mean: Mean coverage taking all elements in a class as one large bin. +#' +#' mean: mean-of-distribution approach. The mean of the aggregated value per +#' locus is reported. +#' +#' median: median-of-distribution. The median of the aggregated value per +#' locus is reported. #' -#' Aggregates scores of a GRanges object on a specific field. -#' @param scored.gr A GRanges object with numerical metadata columns -#' @param group.col A column among the mcols that can be seen as a factor. -#' @param aggregate.by Function used to aggregate (mean, median, any valid -#' function). -#' @return A DataFrame with the aggregated scores (any numerical column will be -#' aggregated). #' @importFrom dplyr group_by_at summarise across `%>%` -aggregate_scores <- function(scored.gr, group.col, aggregate.by) { - df <- data.frame(mcols(scored.gr)) - if ( !is.null(group.col) && group.col %in% names(mcols(scored.gr))) { +#' @importFrom rtracklayer mcols +#' @return A data frame with aggregated scores. +aggregate_scores <- function(scored_granges, group_col, aggregate_by) { + # print(scored_granges) + validate_group_col(scored_granges, group_col) + + # GRanges objects are 1-based and inclusive [start, end] + end_value <- GenomicRanges::end(scored_granges) + start_value <- GenomicRanges::start(scored_granges) + scored_granges$length <- end_value - start_value + 1 + + df <- data.frame(mcols(scored_granges)) + validate_categories(df[, group_col]) + # Make sure special characters are taken into account + score_cols <- colnames(mcols(scored_granges)) + score_cols <- make.names(score_cols) + score_cols <- score_cols[!score_cols %in% c(group_col)] + + if (aggregate_by == "true_mean") { + sum_vals <- df[, score_cols] * df$length + colnames(sum_vals) <- score_cols + sum_vals[, group_col] <- df[, group_col] + sum_vals$length <- df$length + + # Summarize SUM only + sum_vals <- sum_vals %>% + group_by_at(group_col) %>% + summarise(across(where(is.numeric), sum)) + + # Divide sum(scores) by sum(length) and keep only scores + df <- sum_vals[, score_cols] / sum_vals$length + df[, group_col] <- sum_vals[, group_col] + + } else if (aggregate_by %in% c("mean", "median")) { + f <- get(aggregate_by) df <- df %>% - group_by_at(group.col) %>% - summarise(across(where(is.numeric), aggregate.by)) + group_by_at(group_col) %>% + summarise(across(where(is.numeric), f)) + + } else { + stop(paste("Function not implemented as aggregate_by:", aggregate_by)) } - as.data.frame(df) + + score_cols <- score_cols[!score_cols %in% c("length")] + df <- df[, c(score_cols, group_col), drop = FALSE] + data.frame(df) } + + +#' Calculate profile values of a bigWig file over GRanges object. +#' +#' Calculates profile values of a bigWig file over the loci speficied in a +#' GRanges object. Data points are taken each bin_size base pairs. +#' +#' Loci are aligned depending on mode parameter: +#' +#' - stretch. Aligns all starts and all ends, sort of stretching the loci. +#' The median of these lenghts is taken as the pseudo-length in order to show +#' a realistic plot when displayed. +#' +#' - start. All loci are aligned by start. +#' +#' - end. All loci are aligned by end. +#' +#' - center. All loci are aligned by center. +#' +#' Adapted from seqplots rtracklayer wrapper functions to compute coverage: +#' For more on seqplots: +#' https://www.bioconductor.org/packages/release/bioc/html/seqplots.html +#' +#' @param bw BigWig file to be summarized. +#' @param granges GRanges object +#' @param bg_bw BigWig file to be used as background. +#' @param label Name to give to the values +#' @importFrom rtracklayer BigWigFile import +#' @inheritParams bw_profile +#' @return A DataFrame with the aggregated scores +calculate_bw_profile <- function(bw, + granges, + bg_bw = NULL, + label = NULL, + mode = "stretch", + bin_size = 100, + upstream = 2500, + downstream = 2500, + ignore_strand = FALSE, + norm_func = identity) { + + bwfile <- BigWigFile(path = bw) + + if (is.null(label)) { + label <- basename(bw) + } + + if (mode == "stretch") { + + full <- calculate_stretch_matrix(bwfile, granges, + bin_size = bin_size, + upstream = upstream, + downstream = downstream, + ignore_strand = ignore_strand + ) + + if (!is.null(bg_bw)) { + bg_bwfile <- BigWigFile(path = bg_bw) + bg <- calculate_stretch_matrix(bg_bwfile, granges, + bin_size = bin_size, + upstream = upstream, + downstream = downstream, + ignore_strand = ignore_strand + ) + + full <- norm_func(full / bg) + } + + } else { + start_pos <- GenomicRanges::resize(granges, 1, fix = mode) + granges <- GenomicRanges::promoters(start_pos, upstream, downstream) + + npoints <- floor((upstream + downstream) / bin_size) + + full <- intersect_bw_and_granges( + bwfile, + granges, + npoints = npoints, + ignore_strand = FALSE + ) + + if (!is.null(bg_bw)) { + bg_bwfile <- BigWigFile(path = bg_bw) + bg <- intersect_bw_and_granges( + bg_bwfile, + granges, + npoints = npoints, + ignore_strand = FALSE + ) + + full <- norm_func(full / bg) + } + } + + summarize_matrix(full, label) +} + + +#' Calculate matrix for stretch mode +#' +#' @param bw BigWigFile object +#' @param granges GRanges object +#' @param bin_size Bin size +#' @param upstream Number of basepairs upstream +#' @param downstream Number of basepairs downstream +#' @param ignore_strand Ignore strand (bool) +#' +#' @return Summary matrix +calculate_stretch_matrix <- function(bw, + granges, + bin_size = 100, + upstream = 2500, + downstream = 2500, + ignore_strand = FALSE) { + + left_npoints <- floor(upstream / bin_size) + right_npoints <- floor(downstream / bin_size) + + # Stretch to the median value of the GR object + middle_npoints <- floor(median(GenomicRanges::width(granges)) / bin_size) + + left <- intersect_bw_and_granges(bw, + GenomicRanges::flank(granges, upstream, start = TRUE), + npoints = left_npoints, + ignore_strand = ignore_strand + ) + + right <- intersect_bw_and_granges(bw, + GenomicRanges::flank(granges, downstream, start = FALSE), + npoints = right_npoints, + ignore_strand = ignore_strand + ) + + middle <- intersect_bw_and_granges(bw, + granges, + npoints = middle_npoints, + ignore_strand = ignore_strand + ) + + cbind(left, middle, right) +} + + +#' Intersect a BigWig file over loci on a GRanges object +#' +#' Intersect a BigWig file over loci on a GRanges object, taking npoints points +#' per locus. +#' +#' @param bw BigWigFile object (rtracklayer). +#' @param granges GRanges object. +#' @param npoints How many points to take (different to bin size!). +#' @param ignore_strand Ignore strand information in granges. +#' @return A value matrix of dimensions len(granges) x npoints. +intersect_bw_and_granges <- function(bw, + granges, + npoints, + ignore_strand = FALSE) { + + values <- rtracklayer::summary(bw, + which = granges, + as = "matrix", + size = npoints + ) + + # Reverse minus strand rows + if (!ignore_strand) { + indices <- seq(ncol(values), 1) + values[as.character(GenomicRanges::strand(granges)) == "-", ] <- + values[as.character(GenomicRanges::strand(granges)) == "-", indices] + } + + values +} + + +#' Summarize a intersect_bw_and_granges matrix +#' +#' Compute averages and standard error values for a matrix returned by +#' intersect_bw_and_granges. +#' +#' @param matrix A matrix returned by intersect_bw_and_granges. +#' @param label Label for the sample. +#' @importFrom stats median sd +#' @return A data frame with summarized values, stderr and medians, plus label. +summarize_matrix <- function(matrix, label) { + # Ignore Inf and NaN in the computation of means/SD + matrix[is.infinite(matrix)] <- NA + + omitted_vals <- sum(is.na(matrix)) + if (omitted_vals > 100) { + mean_per_locus <- omitted_vals / nrow(matrix) + warning(paste("Profile plot:", + omitted_vals, "generated (", + mean_per_locus, "per locus)" + ) + ) + } + + df <- data.frame( + mean = colMeans(matrix, na.rm = TRUE), + sderror = apply(matrix, 2, + function(n) { + sd(n, na.rm = TRUE) / sqrt(sum(!is.na(n))) + }), + median = apply(matrix, 2, median, na.rm = TRUE) + ) + + df$index <- as.integer(rownames(df)) + df$sample <- label + df +} + + +#' GRanges cbind-like operation +#' +#' Perform a cbind operation on a GRanges list, appending scores to mcols. +#' It will sort the GRanges elements in order to ensure the match is proper. +#' +#' This assumes that you are trying to cbind things that match (bins generated +#' from the same parameters, BED intersections from the same BED.) +#' +#' @param grlist A list of GRanges objects that have all the same fields. +#' @param labels Vector of names for the score columns. +#' @importFrom GenomeInfoDb sortSeqlevels +granges_cbind <- function(grlist, labels) { + fixed_fields <- c("seqnames", "start", "end", "width", "strand") + + grlist[[1]] <- sortSeqlevels(grlist[[1]]) + grlist[[1]] <- sort(grlist[[1]]) + + result <- data.frame(grlist[[1]])[, fixed_fields] + for (i in seq(1, length(grlist))) { + grlist[[i]] <- sortSeqlevels(grlist[[i]]) + grlist[[i]] <- sort(grlist[[i]]) + + result[, labels[[i]]] <- grlist[[i]]$score + } + + result <- makeGRangesFromDataFrame(result, keep.extra.columns = TRUE) + result +} + + +#' Take a column of a data frame to use as row names, drop such column and +#' reorder the data frame so row names follow natural order. +#' +#' @param df A data frame. +#' @param col The column (usually name). +#' @importFrom stringr str_sort +#' @return A sorted df +natural_sort_by_field <- function(df, col) { + rownames(df) <- df[, col] + order <- str_sort(df[, col], numeric = TRUE) + df[, col] <- NULL + df[order, , drop = FALSE] +} + + +#' Validate a category array +#' +#' Checks whether the number of categories is reasonable for an aggregation. +#' Throws a warning if it finds more than 50 different values. +#' +#' @param cat_values An array of values +validate_categories <- function(cat_values) { + max_categories <- 50 + # Test number of values in group_col + ncat <- length(levels(as.factor(cat_values))) + if (ncat > max_categories) { + warning(paste( + "Number of values in group column field very large:", ncat, + "(does BED file have unique IDs instead of categories?)") + ) + } +} + + +#' Validate an array of paths +#' +#' Check that a list of files is valid: not empty and contents exist. +#' +#' @param filelist An array of files +#' @return NULL +validate_filelist <- function(filelist) { + if (length(filelist) == 0) { + stop("File list provided is empty.") + } + + if (!all(file.exists(filelist))) { + msg <- paste("Files not found:", filelist[!file.exists(filelist)]) + stop(msg) + } +} + + +#' Validate that group col exists in granges +#' +#' @param granges GRanges object to check +#' @param group_col Group column name. Usually, name. +validate_group_col <- function(granges, group_col) { + if (!group_col %in% names(mcols(granges))) { + stop(paste("Invalid group column not present in granges", group_col)) + } +} + diff --git a/elsasserlib/R/templates.R b/elsasserlib/R/templates.R new file mode 100644 index 0000000..70f69ba --- /dev/null +++ b/elsasserlib/R/templates.R @@ -0,0 +1,38 @@ +#' Renders a report with specified bigwig directory and bedfiles +#' +#' @param bwdir Directory where bigwig files are +#' @param bed Bed file that contains some regions of interest to summarize +#' @param highlight Optional bed file to highlight bins overlapping (bin analysis) +#' @param bsize Bin size used. +#' @param minoverlap Minimum overlap between a bin and a highlighted region to be marked. +#' @param genome Genome used (supported: mm9, hg38) +#' @param force Force creation of tables or use them if they exist. +#' @param outdir Output directory +#' @param outfile Output filename (without the full path) +#' @importFrom rmarkdown render +#' @export +render_bw_report <- function(bwdir, + bed, + outfile='bwreport.html', + highlight=NULL, + bsize=10000, + minoverlap=2000, + genome='mm9', + outdir='.', + force=FALSE) { + + template <- system.file("templates", "bw_report.Rmd", package="elsasserlib") + + render(template, + params=list(datadir=bwdir, + bed=bed, + binsize=bsize, + highlight=highlight, + genome=genome, + outdir=outdir, + force=force), + output_file=c(basename(outfile), basename(outfile)), + output_format=c('html_document', 'md_document') + ) +} + diff --git a/elsasserlib/R/themes.R b/elsasserlib/R/themes.R index cc01761..0cdf9f0 100644 --- a/elsasserlib/R/themes.R +++ b/elsasserlib/R/themes.R @@ -5,6 +5,7 @@ #' #' @param n Number of values to be generated. #' @return A list of n colors. +#' @importFrom grDevices hcl gg_color_hue <- function(n) { hues = seq(15, 375, length = n + 1) hcl(h = hues, l = 65, c = 100)[1:n] @@ -54,6 +55,7 @@ palette_categorical <- function(n) { #' @param base_family Font family. #' @param base_line_size Thickness of lines. Default is defined based on base_size. #' @importFrom ggplot2 %+replace% theme_minimal theme element_text element_line element_blank rel margin unit +#' @importFrom grDevices rgb #' @export theme_elsasserlab_print <- function(base_size = 22, base_family = "", @@ -101,6 +103,7 @@ theme_elsasserlab_print <- function(base_size = 22, #' @param base_family Font family. #' @param base_line_size Thickness of lines. Default is defined based on base_size. #' @importFrom ggplot2 %+replace% theme_minimal theme element_text element_line element_blank rel margin unit +#' @importFrom grDevices rgb #' @export theme_elsasserlab_screen <- function(base_size = 12, base_family = "", diff --git a/elsasserlib/README.Md b/elsasserlib/README.Md new file mode 100644 index 0000000..9381a47 --- /dev/null +++ b/elsasserlib/README.Md @@ -0,0 +1,64 @@ +![](https://github.com/elsasserlab/labcode/workflows/R-CMD-check/badge.svg) + +## Installation + +`elsasserlib` is a package under development. You will need `remotes` to +install it (and `devtools` if you plan to work on it): + + install.packages(c('devtools', 'remotes')) + +Additionally, there is an issue with installing dependencies that come from +`BioConductor` repository. Therefore, I recommend installing manually these +dependencies before running the installation: + + install.packages(('BiocManager')) + BiocManager::install(c('GenomeInfoDbData', + 'GenomeInfoDb', + 'GenomicRanges', + 'rtracklayer', + 'BSgenome.Mmusculus.UCSC.mm9', + 'BSgenome.Hsapiens.UCSC.hg38')) + +Then you can install directly from this GitHub repository: + + library(remotes) + install_github('elsasserlab/labcode', + subdir='elsasserlib', + build_vignettes=TRUE) + +## Getting started + +I recommend looking at the vignettes as they can give a comprehensive +overview of what is available in the package. These are the groups of +functions that are included in `elsasserlib` as of now: + +- `bwtools`. Functionality to handle `bigWig` files. Importing, binning + and intersecting, aggregating `BED` files and so on. +- `normGeneCounts`. Normalization functions on gene counts matrices. +- `themes`. Helper `ggplot2` based functions that can make plots all + share the same looks for publication, including a print-specific + one that has larger-than-usual font size. + +## Troubleshooting + +**Q**: When running `install_github` I get the following error: + + Error: package or namespace load failed for ‘GenomeInfoDb’ in loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vI[[i]]): + there is no package called ‘GenomeInfoDbData’ + Error: package ‘GenomeInfoDb’ could not be loaded + Execution halted + +**A**: This seems to be a problem that comes from installing `Bioconductor` +dependencies. A workaround is installing the `BioConductor` packages manually: + + if (!requireNamespace('BiocManager', quietly = TRUE)) + install.packages('BiocManager') + + BiocManager::install(c('GenomeInfoDbData', + 'GenomeInfoDb', + 'GenomicRanges', + 'rtracklayer', + 'BSgenome.Mmusculus.UCSC.mm9', + 'BSgenome.Hsapiens.UCSC.hg38')) + + diff --git a/elsasserlib/inst/extdata/ChromHMM7_subsample.bed b/elsasserlib/inst/extdata/ChromHMM7_subsample.bed deleted file mode 100644 index 1c7a116..0000000 --- a/elsasserlib/inst/extdata/ChromHMM7_subsample.bed +++ /dev/null @@ -1,20000 +0,0 @@ -chr15 77144200 77144800 1 0 . 77144200 77144800 -chr13 81778400 81781000 3 0 . 81778400 81781000 -chr17 49710400 49721000 6 0 . 49710400 49721000 -chr18 73786200 73787000 1 0 . 73786200 73787000 -chr3 4939000 4977200 6 0 . 4939000 4977200 -chr18 38050600 38052000 4 0 . 38050600 38052000 -chr11 113509400 113509600 2 0 . 113509400 113509600 -chr10 122500600 122508800 6 0 . 122500600 122508800 -chr15 34448600 34454600 6 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b/elsasserlib/inst/extdata/ES_H33_inp_rep1_hoxc.bw differ diff --git a/elsasserlib/inst/extdata/chromhmm_hoxc.bed b/elsasserlib/inst/extdata/chromhmm_hoxc.bed new file mode 100644 index 0000000..2ac700d --- /dev/null +++ b/elsasserlib/inst/extdata/chromhmm_hoxc.bed @@ -0,0 +1,74 @@ +chr15 102723600 102726800 7 0 . 102723600 102726800 +chr15 102726800 102732200 6 0 . 102726800 102732200 +chr15 102732200 102732800 7 0 . 102732200 102732800 +chr15 102732800 102738800 6 0 . 102732800 102738800 +chr15 102738800 102740400 7 0 . 102738800 102740400 +chr15 102740400 102740800 2 0 . 102740400 102740800 +chr15 102740800 102741200 1 0 . 102740800 102741200 +chr15 102741200 102743600 7 0 . 102741200 102743600 +chr15 102743600 102743800 2 0 . 102743600 102743800 +chr15 102743800 102750600 7 0 . 102743800 102750600 +chr15 102750600 102752800 1 0 . 102750600 102752800 +chr15 102752800 102757400 7 0 . 102752800 102757400 +chr15 102757400 102758200 2 0 . 102757400 102758200 +chr15 102758200 102761800 7 0 . 102758200 102761800 +chr15 102761800 102762000 1 0 . 102761800 102762000 +chr15 102762000 102762200 2 0 . 102762000 102762200 +chr15 102762200 102762400 1 0 . 102762200 102762400 +chr15 102762400 102767000 7 0 . 102762400 102767000 +chr15 102767000 102767800 2 0 . 102767000 102767800 +chr15 102767800 102773000 7 0 . 102767800 102773000 +chr15 102773000 102773800 1 0 . 102773000 102773800 +chr15 102773800 102774000 2 0 . 102773800 102774000 +chr15 102774000 102784600 7 0 . 102774000 102784600 +chr15 102784600 102785200 2 0 . 102784600 102785200 +chr15 102785200 102786200 7 0 . 102785200 102786200 +chr15 102786200 102787000 2 0 . 102786200 102787000 +chr15 102787000 102797000 7 0 . 102787000 102797000 +chr15 102797000 102797400 1 0 . 102797000 102797400 +chr15 102797400 102798600 2 0 . 102797400 102798600 +chr15 102798600 102806000 7 0 . 102798600 102806000 +chr15 102806000 102806600 2 0 . 102806000 102806600 +chr15 102806600 102807400 1 0 . 102806600 102807400 +chr15 102807400 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+chr15 102933400 102947800 7 0 . 102933400 102947800 +chr15 102947800 102951800 6 0 . 102947800 102951800 +chr15 102951800 102956600 7 0 . 102951800 102956600 +chr15 102956600 102959000 6 0 . 102956600 102959000 diff --git a/elsasserlib/inst/extdata/example.1000bs.bw b/elsasserlib/inst/extdata/example.1000bs.bw deleted file mode 100644 index 0b897ca..0000000 Binary files a/elsasserlib/inst/extdata/example.1000bs.bw and /dev/null differ diff --git a/elsasserlib/inst/templates/bw_report.Rmd b/elsasserlib/inst/templates/bw_report.Rmd new file mode 100644 index 0000000..5db1f21 --- /dev/null +++ b/elsasserlib/inst/templates/bw_report.Rmd @@ -0,0 +1,260 @@ +--- +title: BigWig analysis report template +output: + html_document: + toc: true + theme: cerulean + highlight: pygments +params: + datadir: './bw_test' + binsize: 10000 + bed: NA + highlight: NA + minoverlap: 2000 + genome: 'mm9' + force: FALSE + outdir: '.' +--- + +```{r setup, include=FALSE, messages=FALSE} +library(elsasserlib) +library(ggplot2) +library(reshape2) +library(ggpubr) +library(stringr) +library(pheatmap) +library(RColorBrewer) + +# This will be added as functions, but right now is contained in the template, but hidden. +compute.lims <- function(mat) { + maxmat <- mat + maxmat[is.na(maxmat)] <- -Inf + maxval <- max(maxmat) + + minmat <- mat + minmat[is.na(minmat)] <- Inf + minval <- min(minmat) + + nsteps <- 21.0 + + breaklim <- ceiling(abs(max(abs(c(minval, maxval))))) + + # Compute a reasonable amount of steps? + stepsize <- 2*breaklim / nsteps + + breakslist <- seq(-breaklim, +breaklim, by=stepsize) + breakslist +} + +summary_heatmap <- function(values, title, size=35) { + bcolor <- "white" + + breakslist <- compute.lims(values) + palette <- colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(length(breakslist)) + + cellsize <- size + + pheatmap(values, + main=title, + cellwidth=cellsize, + cluster_rows=F, + cluster_cols=F, + cellheight=cellsize, + border_color=bcolor, + breaks=breakslist, + color=palette, + display_numbers=TRUE) +} + + +``` + +This is a self-explanatory template markdown file that does some default +analysis on a set of `bigWig` files contained in a directory. + +```{r parameters, include=FALSE} +# This lists all the files in the path you put there +bwfiles <- list.files(params$datadir) +bwfiles.full <- paste(params$datadir, bwfiles, sep='/') +bed <- params$bed +highlight <- params$highlight +``` + +## Run summary + +This is a summary of the parameters with wich `render_bw_report()` was called. +It is meant as a trace of which data was used to produce this. + +- **Data directory**: `r normalizePath(params$datadir)`. +- **BigWig files found**: `r bwfiles`. +- **BED region file**: `r normalizePath(params$bed)`. +- **Highlight file**: `r normalizePath(params$highlight)`. +- **Genome**: `r params$genome`. +- **Bin size**: `r format(params$binsize, scientific=F)`. +- **Force**: `r params$force`. + +## Parameters description + +These parameters are present on the header of the `Rmd` file. You generate a +report like this calling `render_bw_report()`. See documentation to see how +to do this: + +- `datadir`: Where the `bigWig` files are. +- `binsize`: Size of the bin for binned plots. +- `outdir`: Where to put the output files (basically the intermediate tables). +- `bed`: A `BED` file containing relevant regions. This will typically be: + i) ChromHMM partition of the genome, + ii) Set of repetitive elements, + iii) Gene types (highly/low expressed). + + Any `BED` file that contains categories on the name field (4th column) will + work for such plots. +- `highlight`: If there is a set of loci that you want highlighted in the plots, + meaning that bins that overlap with any of this will be highlighted in the + relevant plots. You can leave this blank for no highlight. +- `minoverlap`: If `highlight` is set, this controls the amount of overlap required + to consider a bin highlighted (this is still not used but it will be soon). +- `genome`: This is needed for the bin analysis. Currently supported genomes + are `mm9` and `hg38`. +- `force`: If `TRUE` it will re-generate the tables regardless of whether they + exist. + +## Bin analysis + +First bins need to be calculated. This is a costful step, so this markdown will +generate tables as a side-product. If they exist, the tables will just be read. + +```{r compute_bins} +precomputed_bins <- paste(outdir, paste('bins_', params$binsize, '.csv', sep=''), sep='/') +precomputed_high <- paste(outdir, paste('high_', params$binsize, '.csv', sep=''), sep='/') + +# Costful chunk of code. If ran before, avoid re-running it unless force is +# set to true. +if (file.exists(precomputed_bins) && !params$force) { + bins.values <- read.csv(precomputed_bins, header=T) + +} else { + bins.values <- bw_bins(bwfiles.full, + bsize=params$binsize, + genome=params$genome) + + bins.values <- data.frame(bins.values) + write.csv(bins.values, precomputed_bins, row.names=FALSE) +} + +# Precompute also the bins for highlighting, if necessary. + +if (file.exists(highlight)) { + if (file.exists(precomputed_high) && !params$force) { + highlight.values <- read.csv(precomputed_high, header=T) + } else { + highlight.gr <- import(highlight) + + highlight.values <- bw_bins(bwfiles.full, + bsize=params$binsize, + genome=params$genome, + selection=highlight.gr) + + highlight.values <- data.frame(highlight.values) + write.csv(highlight.values, precomputed_high, row.names=FALSE) + } +} + +``` + + +### Global per-sample bin coverage distribution + +This shows a violin plot for global bin coverage distribution per sample. +If `highlight` parameter is set, it will show the corresponding overlapping +bins' values, along with (to be implemented) a p-value and effect size of the +enrichment compared to global. + +```{r plot_violin} +bin.id <- c('seqnames', 'start', 'end') + +names <- colnames(bins.values) +names <- names[! names %in% c(bin.id, 'width', 'strand')] + +melted.highlight <- melt(highlight.values[, c(bin.id, names)], id.vars=bin.id) +melted.bins <- melt(bins.values[, c(bin.id, names)], id.vars=bin.id) + +ggplot(melted.bins, aes(x=variable, y=value)) + + geom_violin(fill='#cccccc') + + theme_elsasserlab_screen() + + geom_jitter(data=melted.highlight, + aes(x=variable, y=value, color=variable), + alpha=0.7) + + # Zoom in, this removes quite some values + # However, think that the value output as a warning is the sum of all samples + # not the bins per sample that are removed, this is way smaller + ylim(0,10) + + xlab('') + + ylab('Mean coverage') + + theme(legend.position='none', axis.text.x=element_text(angle=45, hjust=1)) + +``` + + +### Scatterplots for selected samples + +You can choose to compare some pair of samples against each other (perhaps +replicates, or some other information that would be relevant to have paired). +This is only set here as a stub, as it is not a parameter of the notebook (yet). +But, once rendered as notebook, you can still modify this manually for a more +complete analysis, or add chunks of code to add more x-y comparisons. + + +```{r plot_scatter} +used.names <- make.names(bwfiles) +highlight.color <- '#20B2AA' +xcol <- used.names[1] +ycol <- used.names[2] + +scatter.values <- bins.values[, c(xcol, ycol)] + +ggplot(scatter.values, aes_string(x=xcol, y=ycol)) + + geom_point(color='#cccccc', alpha=0.8) + + geom_point(data=highlight.values, aes_string(x=xcol, y=ycol), color=highlight.color, alpha=0.8) + + theme_elsasserlab_screen() + + xlim(0,50) + + ylim(0,50) + + xlab(xcol) + + ylab(ycol) + + ggtitle(paste('Bin coverage scatterplot (bsize =', params$binsize, ')', sep='')) + + theme(legend.position='none') + +``` + + +## Summarized bed plot + +The summarized ChromHMM-like plots are made by `bw_bed` function: + +```{r precompute_bed} +precomputed_summary <- paste(outdir, paste('truemean_', + basename(params$bed), + '.csv', sep=''), + sep='/') + +# Costful chunk of code. If ran before, avoid re-running it unless force is +# set to true. +if (file.exists(precomputed_summary) && !params$force) { + summary.values <- read.csv(precomputed_summary, header=T, row.names=1) + +} else { + summary.values <- bw_bed(bwfiles.full, + bed=params$bed, + aggregate.by='true_mean') + + write.csv(summary.values, precomputed_summary, row.names=TRUE) +} +``` + + +Once the values are calculated we can plot it: + +```{r summary_heatmap, fig.width=12} +summary_heatmap(t(summary.values), "Mean coverage per region") +``` + diff --git a/elsasserlib/man/aggregate_scores.Rd b/elsasserlib/man/aggregate_scores.Rd index f16175e..2c0c847 100644 --- a/elsasserlib/man/aggregate_scores.Rd +++ b/elsasserlib/man/aggregate_scores.Rd @@ -2,22 +2,29 @@ % Please edit documentation in R/bwtools.R \name{aggregate_scores} \alias{aggregate_scores} -\title{Aggregate scores of a GRanges object on a specific field} +\title{Aggregate scores of a GRanges object on a field} \usage{ -aggregate_scores(scored.gr, group.col, aggregate.by) +aggregate_scores(scored_granges, group_col, aggregate_by) } \arguments{ -\item{scored.gr}{A GRanges object with numerical metadata columns} +\item{scored_granges}{A GRanges object with numerical metadata columns} -\item{group.col}{A column among the mcols that can be seen as a factor.} +\item{group_col}{A column among the mcols that can be seen as a factor.} -\item{aggregate.by}{Function used to aggregate (mean, median, any valid -function).} +\item{aggregate_by}{Function used to aggregate: mean, median, true_mean. + + true_mean: Mean coverage taking all elements in a class as one large bin. + + mean: mean-of-distribution approach. The mean of the aggregated value per + locus is reported. + + median: median-of-distribution. The median of the aggregated value per + locus is reported.} } \value{ -A DataFrame with the aggregated scores (any numerical column will be - aggregated). +A data frame with aggregated scores. } \description{ -Aggregates scores of a GRanges object on a specific field. +Aggregates scores of a GRanges object on a specific field. Used for summary +functions. } diff --git a/elsasserlib/man/build_bins.Rd b/elsasserlib/man/build_bins.Rd index 3ea1a61..2d2d0eb 100644 --- a/elsasserlib/man/build_bins.Rd +++ b/elsasserlib/man/build_bins.Rd @@ -2,14 +2,14 @@ % Please edit documentation in R/bwtools.R \name{build_bins} \alias{build_bins} -\title{Build a bins GRanges object.} +\title{Build a unscored bins GRanges object.} \usage{ -build_bins(bsize = 10000, genome = "mm9") +build_bins(bin_size = 10000, genome = "mm9") } \arguments{ -\item{bsize}{Bin size. 10000 by default.} +\item{bin_size}{Bin size.} -\item{genome}{Genome used. mm9 by default. Valid values: mm9, hg38} +\item{genome}{Genome.} } \value{ A GRanges object diff --git a/elsasserlib/man/bw_bed.Rd b/elsasserlib/man/bw_bed.Rd index bada3da..e8bbec3 100644 --- a/elsasserlib/man/bw_bed.Rd +++ b/elsasserlib/man/bw_bed.Rd @@ -2,31 +2,50 @@ % Please edit documentation in R/bwtools.R \name{bw_bed} \alias{bw_bed} -\title{Build a scored GRanges object from a BED file.} +\title{Build a bed-scored GRanges object from a bigWig file list and a BED file.} \usage{ bw_bed( bwfiles, bedfile, - colnames = NULL, - per.locus.stat = "mean", - aggregate.by = NULL + bg_bwfiles = NULL, + labels = NULL, + per_locus_stat = "mean", + aggregate_by = NULL, + norm_func = identity ) } \arguments{ -\item{bwfiles}{BigWig file (or list) to be summarized.} +\item{bwfiles}{Path or array of paths to the bigWig files to be summarized.} \item{bedfile}{BED file to intersect with the BigWig file.} -\item{colnames}{Column names of the score fields. Must have the same -length as bigwig file list. If not provided, colnames are the names of -the files in bwfiles.} +\item{bg_bwfiles}{Path or array of paths to the bigWig files to be used as +background.} -\item{per.locus.stat}{Aggregate per locus function.} +\item{labels}{List of names to give to the mcols of the returned GRanges +object. If NULL, file names are used.} -\item{aggregate.by}{Statistic to aggregate per group. If NULL, values are +\item{per_locus_stat}{Aggregating function (per locus). Mean by default. +Choices: min, max, sd, mean.} + +\item{aggregate_by}{Statistic to aggregate per group. If NULL, values are not aggregated. This is the behavior by default.} + +\item{norm_func}{Function to apply to normalize bin values f(bw / bw_bg).} } \description{ -Build a scored GRanges object from a bigWig file and a BED file. +Build a scored GRanges object from a bigWig file list and a BED file. The aggregating function (per locus) can be min, max, sd, mean. } +\details{ +bwfiles and bg_bwfiles must have the same length. If you are using same +background for several files, then file paths must be repeated accordingly. + +Values can be normalized using background bigWig files (usually input +files). By default, the value obtained will be bigwig / bg_bigwig per bin, +per bigWig. + +If norm_func is specified, this can be changed to any given function, for +instance, if norm_func = log2, values will represent log2(bigwig / bg_bigwig) +per bin. +} diff --git a/elsasserlib/man/bw_bins.Rd b/elsasserlib/man/bw_bins.Rd index 85373f3..fb177e1 100644 --- a/elsasserlib/man/bw_bins.Rd +++ b/elsasserlib/man/bw_bins.Rd @@ -4,26 +4,56 @@ \alias{bw_bins} \title{Build a binned-scored GRanges object from a bigWig file} \usage{ -bw_bins(bwfiles, colnames = NULL, stat = "mean", bsize = 10000, genome = "mm9") +bw_bins( + bwfiles, + bg_bwfiles = NULL, + labels = NULL, + per_locus_stat = "mean", + bin_size = 10000, + genome = "mm9", + selection = NULL, + norm_func = identity +) } \arguments{ -\item{colnames}{List of names to give to the mcols of the returned GRanges -object. If NULL, filenames are used (default).} +\item{bwfiles}{Path or array of paths to the bigWig files to be summarized.} -\item{stat}{Aggregating function (per locus). Mean by default. +\item{bg_bwfiles}{Path or array of paths to the bigWig files to be used as +background.} + +\item{labels}{List of names to give to the mcols of the returned GRanges +object. If NULL, file names are used.} + +\item{per_locus_stat}{Aggregating function (per locus). Mean by default. Choices: min, max, sd, mean.} -\item{bsize}{Bin size. Default 10000.} +\item{bin_size}{Bin size.} \item{genome}{Genome. Available choices are mm9, hg38.} -\item{bwfile}{BigWig file to be summarized (or list).} +\item{selection}{A GRanges object to restrict binning to a certain set of +intervals. This is useful for debugging and improving performance of +locus specific analyses.} + +\item{norm_func}{Function to apply to normalize bin values f(bw / bw_bg).} } \value{ -A GenomicRanges object with each bwfile as a metadata column named - after colnames. +A GRanges object with each bwfile as a metadata column named + after labels, if provided, or after filenames otherwise. } \description{ Build a binned-scored GRanges object from a bigWig file. The aggregating -function can be min, max, sd, mean. +function per bin can be min, max, sd, mean. +} +\details{ +bwfiles and bg_bwfiles must have the same length. If you are using same +background for several files, then file paths must be repeated accordingly. + +Values can be normalized using background bigWig files (usually input +files). By default, the value obtained will be bigwig / bg_bigwig per bin, +per bigWig. + +If norm_func is specified, this can be changed to any given function, for +instance, if norm_func = log2, values will represent log2(bigwig / bg_bigwig) +per bin. } diff --git a/elsasserlib/man/bw_profile.Rd b/elsasserlib/man/bw_profile.Rd new file mode 100644 index 0000000..b0d6fb5 --- /dev/null +++ b/elsasserlib/man/bw_profile.Rd @@ -0,0 +1,71 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwtools.R +\name{bw_profile} +\alias{bw_profile} +\title{Calculate profile values of a bigWig file over a BED file.} +\usage{ +bw_profile( + bwfiles, + bg_bwfiles = NULL, + bedfile = NULL, + labels = NULL, + mode = "stretch", + bin_size = 100, + upstream = 2500, + downstream = 2500, + ignore_strand = FALSE, + norm_func = identity +) +} +\arguments{ +\item{bwfiles}{Path or array of paths to the bigWig files to be summarized.} + +\item{bg_bwfiles}{Path or array of paths to the bigWig files to be used as +background.} + +\item{bedfile}{BED file to summarize} + +\item{labels}{List of names to give to the mcols of the returned GRanges +object. If NULL, file names are used.} + +\item{mode}{How to handle differences in lengths across loci: + + stretch: Anchor each locus on both sides. + + start: Anchor all loci on start. + + end: Anchor all loci on end. + + center: Center all loci.} + +\item{bin_size}{Bin size. Length of bin in base pairs. The lower, the higher +the resolution.} + +\item{upstream}{Number of base pairs to include upstream of loci.} + +\item{downstream}{Number of base pairs to include downstream of loci.} + +\item{ignore_strand}{Whether to use strand information in BED file.} + +\item{norm_func}{Function to apply to normalize bin values f(bw / bw_bg).} +} +\value{ +a data frame in long format +} +\description{ +Calculates profile values of a set of tracks over the loci speficied in a +BED file. Data points are taken each bin_size base pairs. +} +\details{ +Loci are aligned depending on mode parameter: + +- stretch. Aligns all starts and all ends, sort of stretching the loci. +The median of these lenghts is taken as the pseudo-length in order to show +a realistic plot when displayed. + +- start. All loci are aligned by start. + +- end. All loci are aligned by end. + +- center. All loci are aligned by center. +} diff --git a/elsasserlib/man/bw_ranges.Rd b/elsasserlib/man/bw_ranges.Rd index f94e2f8..4bba240 100644 --- a/elsasserlib/man/bw_ranges.Rd +++ b/elsasserlib/man/bw_ranges.Rd @@ -4,20 +4,24 @@ \alias{bw_ranges} \title{Score a GRanges object against a BigWig file} \usage{ -bw_ranges(bwfile, gr, per.locus.stat = "mean") +bw_ranges(bwfile, granges, per_locus_stat = "mean", selection = NULL) } \arguments{ -\item{bwfile}{BigWig file to be summarized.} +\item{bwfile}{Path to a single BigWig file to be summarized.} -\item{gr}{GRanges object file to be summarized.} +\item{granges}{GRanges object file to be summarized.} -\item{per.locus.stat}{Aggregating function (per locus). Mean by default. -Choices: min, max, sd, mean. These choices depend on rtracklayer library.} +\item{per_locus_stat}{Aggregating function (per locus). Mean by default. +Choices: min, max, sd, mean.} + +\item{selection}{A GRanges object to restrict binning to a certain set of +intervals. This is useful for debugging and improving performance of +locus specific analyses.} } \value{ -Data frame with columns score and group.col (if provided). +GRanges with column score. } \description{ -Build a scored GRanges object from a GRanges object and a BigWig file. +Build a scored GRanges object from a GRanges object and a single BigWig file. The aggregating function can be min, max, sd, mean. } diff --git a/elsasserlib/man/calculate_breakslist.Rd b/elsasserlib/man/calculate_breakslist.Rd new file mode 100644 index 0000000..426d550 --- /dev/null +++ b/elsasserlib/man/calculate_breakslist.Rd @@ -0,0 +1,22 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwplot.R +\name{calculate_breakslist} +\alias{calculate_breakslist} +\title{Calculate breaks list for the values in a matrix} +\usage{ +calculate_breakslist(values) +} +\arguments{ +\item{values}{Value matrix.} +} +\value{ +Sequence of breaks used by heatmap function +} +\description{ +Given a numerical matrix, calculate breakslist to use in the summarized +heatmap plot. This is done in order to unify color scale regardless of +normalization used (mostly log vs linear), keeping white as the zero value. +} +\details{ +This function ignores NA values to calculate min and max values. +} diff --git a/elsasserlib/man/calculate_bw_profile.Rd b/elsasserlib/man/calculate_bw_profile.Rd new file mode 100644 index 0000000..c072fb2 --- /dev/null +++ b/elsasserlib/man/calculate_bw_profile.Rd @@ -0,0 +1,73 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwtools.R +\name{calculate_bw_profile} +\alias{calculate_bw_profile} +\title{Calculate profile values of a bigWig file over GRanges object.} +\usage{ +calculate_bw_profile( + bw, + granges, + bg_bw = NULL, + label = NULL, + mode = "stretch", + bin_size = 100, + upstream = 2500, + downstream = 2500, + ignore_strand = FALSE, + norm_func = identity +) +} +\arguments{ +\item{bw}{BigWig file to be summarized.} + +\item{granges}{GRanges object} + +\item{bg_bw}{BigWig file to be used as background.} + +\item{label}{Name to give to the values} + +\item{mode}{How to handle differences in lengths across loci: + + stretch: Anchor each locus on both sides. + + start: Anchor all loci on start. + + end: Anchor all loci on end. + + center: Center all loci.} + +\item{bin_size}{Bin size. Length of bin in base pairs. The lower, the higher +the resolution.} + +\item{upstream}{Number of base pairs to include upstream of loci.} + +\item{downstream}{Number of base pairs to include downstream of loci.} + +\item{ignore_strand}{Whether to use strand information in BED file.} + +\item{norm_func}{Function to apply to normalize bin values f(bw / bw_bg).} +} +\value{ +A DataFrame with the aggregated scores +} +\description{ +Calculates profile values of a bigWig file over the loci speficied in a +GRanges object. Data points are taken each bin_size base pairs. +} +\details{ +Loci are aligned depending on mode parameter: + +- stretch. Aligns all starts and all ends, sort of stretching the loci. +The median of these lenghts is taken as the pseudo-length in order to show +a realistic plot when displayed. + +- start. All loci are aligned by start. + +- end. All loci are aligned by end. + +- center. All loci are aligned by center. + +Adapted from seqplots rtracklayer wrapper functions to compute coverage: +For more on seqplots: +https://www.bioconductor.org/packages/release/bioc/html/seqplots.html +} diff --git a/elsasserlib/man/calculate_stretch_matrix.Rd b/elsasserlib/man/calculate_stretch_matrix.Rd new file mode 100644 index 0000000..e1c3434 --- /dev/null +++ b/elsasserlib/man/calculate_stretch_matrix.Rd @@ -0,0 +1,34 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwtools.R +\name{calculate_stretch_matrix} +\alias{calculate_stretch_matrix} +\title{Calculate matrix for stretch mode} +\usage{ +calculate_stretch_matrix( + bw, + granges, + bin_size = 100, + upstream = 2500, + downstream = 2500, + ignore_strand = FALSE +) +} +\arguments{ +\item{bw}{BigWigFile object} + +\item{granges}{GRanges object} + +\item{bin_size}{Bin size} + +\item{upstream}{Number of basepairs upstream} + +\item{downstream}{Number of basepairs downstream} + +\item{ignore_strand}{Ignore strand (bool)} +} +\value{ +Summary matrix +} +\description{ +Calculate matrix for stretch mode +} diff --git a/elsasserlib/man/granges_cbind.Rd b/elsasserlib/man/granges_cbind.Rd new file mode 100644 index 0000000..b8bdff6 --- /dev/null +++ b/elsasserlib/man/granges_cbind.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwtools.R +\name{granges_cbind} +\alias{granges_cbind} +\title{GRanges cbind-like operation} +\usage{ +granges_cbind(grlist, labels) +} +\arguments{ +\item{grlist}{A list of GRanges objects that have all the same fields.} + +\item{labels}{Vector of names for the score columns.} +} +\description{ +Perform a cbind operation on a GRanges list, appending scores to mcols. +It will sort the GRanges elements in order to ensure the match is proper. +} +\details{ +This assumes that you are trying to cbind things that match (bins generated +from the same parameters, BED intersections from the same BED.) +} diff --git a/elsasserlib/man/hello.Rd b/elsasserlib/man/hello.Rd deleted file mode 100644 index 0fa7c4b..0000000 --- a/elsasserlib/man/hello.Rd +++ /dev/null @@ -1,12 +0,0 @@ -\name{hello} -\alias{hello} -\title{Hello, World!} -\usage{ -hello() -} -\description{ -Prints 'Hello, world!'. -} -\examples{ -hello() -} diff --git a/elsasserlib/man/intersect_bw_and_granges.Rd b/elsasserlib/man/intersect_bw_and_granges.Rd new file mode 100644 index 0000000..5456183 --- /dev/null +++ b/elsasserlib/man/intersect_bw_and_granges.Rd @@ -0,0 +1,24 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwtools.R +\name{intersect_bw_and_granges} +\alias{intersect_bw_and_granges} +\title{Intersect a BigWig file over loci on a GRanges object} +\usage{ +intersect_bw_and_granges(bw, granges, npoints, ignore_strand = FALSE) +} +\arguments{ +\item{bw}{BigWigFile object (rtracklayer).} + +\item{granges}{GRanges object.} + +\item{npoints}{How many points to take (different to bin size!).} + +\item{ignore_strand}{Ignore strand information in granges.} +} +\value{ +A value matrix of dimensions len(granges) x npoints. +} +\description{ +Intersect a BigWig file over loci on a GRanges object, taking npoints points +per locus. +} diff --git a/elsasserlib/man/make_norm_label.Rd b/elsasserlib/man/make_norm_label.Rd new file mode 100644 index 0000000..60dee6f --- /dev/null +++ b/elsasserlib/man/make_norm_label.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwplot.R +\name{make_norm_label} +\alias{make_norm_label} +\title{Generate a human-readable normalization function string} +\usage{ +make_norm_label(f, bg) +} +\arguments{ +\item{f}{String representing normalization function.} + +\item{bg}{Background file.} +} +\value{ +A string describing normalization. +} +\description{ +Generate a human-readable normalization function string +} diff --git a/elsasserlib/man/mean_ratio_norm.Rd b/elsasserlib/man/mean_ratio_norm.Rd new file mode 100644 index 0000000..56c77c6 --- /dev/null +++ b/elsasserlib/man/mean_ratio_norm.Rd @@ -0,0 +1,18 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/normGeneCounts.R +\name{mean_ratio_norm} +\alias{mean_ratio_norm} +\title{Median Ratio Normalization of Gene Count Matrix} +\usage{ +mean_ratio_norm(gene.counts) +} +\arguments{ +\item{gene.counts}{Gene count table for normalization} +} +\value{ +Normalised Counts +} +\description{ +This function devides original values with median ratios comparing to the first sample +according to edgeR method. +} diff --git a/elsasserlib/man/multi_bw_ranges.Rd b/elsasserlib/man/multi_bw_ranges.Rd index 1b33483..2806417 100644 --- a/elsasserlib/man/multi_bw_ranges.Rd +++ b/elsasserlib/man/multi_bw_ranges.Rd @@ -2,18 +2,33 @@ % Please edit documentation in R/bwtools.R \name{multi_bw_ranges} \alias{multi_bw_ranges} -\title{Intersect a list of bw files with a GRanges object} +\title{Intersect a list of bigWig files with a GRanges object} \usage{ -multi_bw_ranges(bwfilelist, colnames, gr, per.locus.stat = "mean") +multi_bw_ranges( + bwfiles, + labels, + granges, + per_locus_stat = "mean", + selection = NULL +) } \arguments{ -\item{bwfilelist}{BigWig file to be summarized.} +\item{bwfiles}{Path or array of paths to the bigWig files to be summarized.} -\item{gr}{GRanges object to intersect} +\item{labels}{List of names to give to the mcols of the returned GRanges +object. If NULL, file names are used.} -\item{per.locus.stat}{Aggregating function per stat} +\item{granges}{GRanges object to intersect} -\item{col.names}{Names to be assigned to the columns} +\item{per_locus_stat}{Aggregating function (per locus). Mean by default. +Choices: min, max, sd, mean.} + +\item{selection}{A GRanges object to restrict binning to a certain set of +intervals. This is useful for debugging and improving performance of +locus specific analyses.} +} +\value{ +A sorted GRanges object. } \description{ Build a binned-scored GRanges object from a list of bigWig files. The diff --git a/elsasserlib/man/multi_bw_ranges_aggregated.Rd b/elsasserlib/man/multi_bw_ranges_aggregated.Rd new file mode 100644 index 0000000..cf3f669 --- /dev/null +++ b/elsasserlib/man/multi_bw_ranges_aggregated.Rd @@ -0,0 +1,34 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwtools.R +\name{multi_bw_ranges_aggregated} +\alias{multi_bw_ranges_aggregated} +\title{Intersect a list of bigWig files with a GRanges object and aggregate by name} +\usage{ +multi_bw_ranges_aggregated( + bwfiles, + labels, + granges, + per_locus_stat, + aggregate_by +) +} +\arguments{ +\item{bwfiles}{Path or array of paths to the bigWig files to be summarized.} + +\item{labels}{List of names to give to the mcols of the returned GRanges +object. If NULL, file names are used.} + +\item{granges}{GRanges object to summarize. Should have a valid name field.} + +\item{per_locus_stat}{Aggregating function (per locus). Mean by default. +Choices: min, max, sd, mean.} + +\item{aggregate_by}{Statistic to aggregate per group. If NULL, values are +not aggregated. This is the behavior by default.} +} +\value{ +An aggregated dataframe +} +\description{ +Intersect a list of bigWig files with a GRanges object and aggregate by name +} diff --git a/elsasserlib/man/multi_bw_ranges_norm.Rd b/elsasserlib/man/multi_bw_ranges_norm.Rd new file mode 100644 index 0000000..d67d896 --- /dev/null +++ b/elsasserlib/man/multi_bw_ranges_norm.Rd @@ -0,0 +1,40 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwtools.R +\name{multi_bw_ranges_norm} +\alias{multi_bw_ranges_norm} +\title{Intersect a list of bigWig files with a GRanges object (with background)} +\usage{ +multi_bw_ranges_norm( + bwfilelist, + bg_bwfilelist, + labels, + granges, + per_locus_stat = "mean", + selection = NULL, + norm_func = identity +) +} +\arguments{ +\item{bwfilelist}{BigWig file list to be summarized.} + +\item{bg_bwfilelist}{Background BigWig files.} + +\item{labels}{List of names to give to the mcols of the returned GRanges +object. If NULL, file names are used.} + +\item{granges}{GRanges object to intersect} + +\item{per_locus_stat}{Aggregating function (per locus). Mean by default. +Choices: min, max, sd, mean.} + +\item{selection}{A GRanges object to restrict analysis to.} + +\item{norm_func}{Function to apply after bw/bg_bw.} +} +\value{ +a sorted GRanges object +} +\description{ +Intersect a list of bigWig files with a GRanges object and normalize values +with a set of background bigWig files. +} diff --git a/elsasserlib/man/natural_sort_by_field.Rd b/elsasserlib/man/natural_sort_by_field.Rd new file mode 100644 index 0000000..cc8278f --- /dev/null +++ b/elsasserlib/man/natural_sort_by_field.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwtools.R +\name{natural_sort_by_field} +\alias{natural_sort_by_field} +\title{Take a column of a data frame to use as row names, drop such column and +reorder the data frame so row names follow natural order.} +\usage{ +natural_sort_by_field(df, col) +} +\arguments{ +\item{df}{A data frame.} + +\item{col}{The column (usually name).} +} +\value{ +A sorted df +} +\description{ +Take a column of a data frame to use as row names, drop such column and +reorder the data frame so row names follow natural order. +} diff --git a/elsasserlib/man/plot_bw_bed_summary_heatmap.Rd b/elsasserlib/man/plot_bw_bed_summary_heatmap.Rd new file mode 100644 index 0000000..5f6fbf0 --- /dev/null +++ b/elsasserlib/man/plot_bw_bed_summary_heatmap.Rd @@ -0,0 +1,42 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwplot.R +\name{plot_bw_bed_summary_heatmap} +\alias{plot_bw_bed_summary_heatmap} +\title{Summary heatmap of a categorized BED file} +\usage{ +plot_bw_bed_summary_heatmap( + bwfiles, + bedfile, + bg_bwfiles = NULL, + labels = NULL, + aggregate_by = "true_mean", + norm_func = identity, + file_out = NA +) +} +\arguments{ +\item{bwfiles}{Path or array of paths to the bigWig files to be summarized.} + +\item{bedfile}{BED file to intersect with the BigWig file.} + +\item{bg_bwfiles}{Path or array of paths to the bigWig files to be used as +background.} + +\item{labels}{Labels to use for in the plot for the bw files.} + +\item{aggregate_by}{Statistic to aggregate per group. If NULL, values are +not aggregated. This is the behavior by default.} + +\item{norm_func}{Function to apply to normalize bin values f(bw / bw_bg).} + +\item{file_out}{Output the plot to a file.} +} +\value{ +A pheatmap object +} +\description{ +Make a summary heatmap where each cell contains an aggregated value of a +bigWig file from bwfiles and a category of a BED file (bedfile). The +provided BED file must have a name field that is valid (i.e. can be grouped, +representing some type of category, not a per-locus unique ID). +} diff --git a/elsasserlib/man/plot_bw_bins_scatter.Rd b/elsasserlib/man/plot_bw_bins_scatter.Rd new file mode 100644 index 0000000..b8230a6 --- /dev/null +++ b/elsasserlib/man/plot_bw_bins_scatter.Rd @@ -0,0 +1,69 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwplot.R +\name{plot_bw_bins_scatter} +\alias{plot_bw_bins_scatter} +\title{Bin-based scatterplot of a pair of bigWig files} +\usage{ +plot_bw_bins_scatter( + x, + y, + bg_x = NULL, + bg_y = NULL, + bin_size = 10000, + per_locus_stat = "mean", + genome = "mm9", + highlight = NULL, + minoverlap = 0L, + highlight_label = NULL, + norm_func_x = identity, + norm_func_y = identity +) +} +\arguments{ +\item{x}{BigWig file corresponding to the x axis.} + +\item{y}{BigWig file corresponding to the y axis.} + +\item{bg_x}{BigWig file to be used as x axis background (us. input).} + +\item{bg_y}{BigWig file to be used as y axis background (us. input).} + +\item{bin_size}{Bin size.} + +\item{per_locus_stat}{Aggregating function (per locus). Mean by default. +Choices: min, max, sd, mean.} + +\item{genome}{Genome. Available choices are mm9, hg38.} + +\item{highlight}{List of bed files to use as highlight for subgroups.} + +\item{minoverlap}{Minimum overlap required for a bin to be highlighted} + +\item{highlight_label}{Labels for the highlight groups. +If not provided, filenames are used.} + +\item{norm_func_x}{Function to use after x / x_bg.} + +\item{norm_func_y}{Function to use after y / y_bg.} +} +\value{ +A ggplot object. +} +\description{ +Plots a scatter plot from two given bigWig files and an optional set of BED +files as highlighted annotations. Bins are highlighted if there is at least +minoverlap base pairs overlap with any loci in BED file. +} +\details{ +If specifying minoverlap, you must take into account the bin_size parameter +and the size of the loci you are providing as BED file. + +Values in x and y axis can be normalized using background bigWig files +(usually input files). By default, the value shown will be x / bg_x per bin. +If norm_func_x or norm_func_y are provided, this can be changed to any given +function, for instance, if norm_func_x = log2, values on the x axis will +represent log2(x / bg_x) for each bin. + +Values that are invalid (NaN, Inf, -Inf) in doing such normalization will +be ignored and shown as warnings, as this is ggplot default behavior. +} diff --git a/elsasserlib/man/plot_bw_bins_violin.Rd b/elsasserlib/man/plot_bw_bins_violin.Rd new file mode 100644 index 0000000..8ee1b31 --- /dev/null +++ b/elsasserlib/man/plot_bw_bins_violin.Rd @@ -0,0 +1,48 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwplot.R +\name{plot_bw_bins_violin} +\alias{plot_bw_bins_violin} +\title{Bin-based violin plot of a set of bigWig files} +\usage{ +plot_bw_bins_violin( + bwfiles, + bg_bwfiles = NULL, + labels = NULL, + bin_size = 10000, + per_locus_stat = "mean", + genome = "mm9", + highlight = NULL, + minoverlap = 0L, + norm_func = identity +) +} +\arguments{ +\item{bwfiles}{Path or array of paths to the bigWig files to be summarized.} + +\item{bg_bwfiles}{Path or array of paths to the bigWig files to be used as +background.} + +\item{labels}{List of names to give to the mcols of the returned GRanges +object. If NULL, file names are used.} + +\item{bin_size}{Bin size.} + +\item{per_locus_stat}{Aggregating function (per locus). Mean by default. +Choices: min, max, sd, mean.} + +\item{genome}{Genome. Available choices are mm9, hg38.} + +\item{highlight}{BED file to use as highlight for subgroups.} + +\item{minoverlap}{Minimum overlap required for a bin to be highlighted.} + +\item{norm_func}{Function to apply to normalize bin values f(bw / bw_bg).} +} +\value{ +A ggplot object. +} +\description{ +Plots a violin plot of bin distribution of a set of bigWig files optionally +overlaid with annotated bins. Bins overlapping loci of the provided BED +file will be shown as a jitter plot on top of the violin plot. +} diff --git a/elsasserlib/man/plot_bw_profile.Rd b/elsasserlib/man/plot_bw_profile.Rd new file mode 100644 index 0000000..cfbb71e --- /dev/null +++ b/elsasserlib/man/plot_bw_profile.Rd @@ -0,0 +1,56 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwplot.R +\name{plot_bw_profile} +\alias{plot_bw_profile} +\title{Profile plot of a set of bigWig files} +\usage{ +plot_bw_profile( + bwfiles, + bedfile, + bg_bwfiles = NULL, + mode = "stretch", + bin_size = 100, + upstream = 2500, + downstream = 2500, + ignore_strand = FALSE, + show_error = FALSE, + norm_func = identity +) +} +\arguments{ +\item{bwfiles}{Path or array of paths to the bigWig files to be summarized.} + +\item{bedfile}{BED file to summarize} + +\item{bg_bwfiles}{Path or array of paths to the bigWig files to be used as +background.} + +\item{mode}{How to handle differences in lengths across loci: + + stretch: Anchor each locus on both sides. + + start: Anchor all loci on start. + + end: Anchor all loci on end. + + center: Center all loci.} + +\item{bin_size}{Bin size. Length of bin in base pairs. The lower, the higher +the resolution.} + +\item{upstream}{Number of base pairs to include upstream of loci.} + +\item{downstream}{Number of base pairs to include downstream of loci.} + +\item{ignore_strand}{Whether to use strand information in BED file.} + +\item{show_error}{Show standard error.} + +\item{norm_func}{Function to apply to normalize bin values f(bw / bw_bg).} +} +\value{ +A ggplot object. +} +\description{ +Plots a profile of a set of bigWig files over a set of loci in a BED file. +} diff --git a/elsasserlib/man/quantile_norm.Rd b/elsasserlib/man/quantile_norm.Rd new file mode 100644 index 0000000..6b8f32f --- /dev/null +++ b/elsasserlib/man/quantile_norm.Rd @@ -0,0 +1,18 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/normGeneCounts.R +\name{quantile_norm} +\alias{quantile_norm} +\title{Quantile Normalization of Gene Count Matrix} +\usage{ +quantile_norm(gene.counts) +} +\arguments{ +\item{gene.counts}{Gene count table for normalization} +} +\value{ +Normalised Counts +} +\description{ +This function replaces original values with estimated values according +to the rank within each sample. +} diff --git a/elsasserlib/man/rename_score.Rd b/elsasserlib/man/rename_score.Rd deleted file mode 100644 index 2d86520..0000000 --- a/elsasserlib/man/rename_score.Rd +++ /dev/null @@ -1,16 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/bwtools.R -\name{rename_score} -\alias{rename_score} -\title{Rename score function of a GRanges object} -\usage{ -rename_score(gr, new.name) -} -\arguments{ -\item{gr}{GRanges object} - -\item{new.name}{Name to give to the column.} -} -\description{ -Rename score function of a GRanges object -} diff --git a/elsasserlib/man/render_bw_report.Rd b/elsasserlib/man/render_bw_report.Rd new file mode 100644 index 0000000..9f2ee70 --- /dev/null +++ b/elsasserlib/man/render_bw_report.Rd @@ -0,0 +1,40 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/templates.R +\name{render_bw_report} +\alias{render_bw_report} +\title{Renders a report with specified bigwig directory and bedfiles} +\usage{ +render_bw_report( + bwdir, + bed, + outfile = "bwreport.html", + highlight = NULL, + bsize = 10000, + minoverlap = 2000, + genome = "mm9", + outdir = ".", + force = FALSE +) +} +\arguments{ +\item{bwdir}{Directory where bigwig files are} + +\item{bed}{Bed file that contains some regions of interest to summarize} + +\item{outfile}{Output filename (without the full path)} + +\item{highlight}{Optional bed file to highlight bins overlapping (bin analysis)} + +\item{bsize}{Bin size used.} + +\item{minoverlap}{Minimum overlap between a bin and a highlighted region to be marked.} + +\item{genome}{Genome used (supported: mm9, hg38)} + +\item{outdir}{Output directory} + +\item{force}{Force creation of tables or use them if they exist.} +} +\description{ +Renders a report with specified bigwig directory and bedfiles +} diff --git a/elsasserlib/man/size_factor_norm.Rd b/elsasserlib/man/size_factor_norm.Rd new file mode 100644 index 0000000..918eeb8 --- /dev/null +++ b/elsasserlib/man/size_factor_norm.Rd @@ -0,0 +1,18 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/normGeneCounts.R +\name{size_factor_norm} +\alias{size_factor_norm} +\title{Size Factor Normalization of Gene Count Matrix} +\usage{ +size_factor_norm(gene.counts) +} +\arguments{ +\item{gene.counts}{Gene count table for normalization} +} +\value{ +Normalised Counts +} +\description{ +This function devides original values with size factors of each sample according +to DESeq method. +} diff --git a/elsasserlib/man/summarize_matrix.Rd b/elsasserlib/man/summarize_matrix.Rd new file mode 100644 index 0000000..8cab34a --- /dev/null +++ b/elsasserlib/man/summarize_matrix.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwtools.R +\name{summarize_matrix} +\alias{summarize_matrix} +\title{Summarize a intersect_bw_and_granges matrix} +\usage{ +summarize_matrix(matrix, label) +} +\arguments{ +\item{matrix}{A matrix returned by intersect_bw_and_granges.} + +\item{label}{Label for the sample.} +} +\value{ +A data frame with summarized values, stderr and medians, plus label. +} +\description{ +Compute averages and standard error values for a matrix returned by +intersect_bw_and_granges. +} diff --git a/elsasserlib/man/summary_heatmap.Rd b/elsasserlib/man/summary_heatmap.Rd new file mode 100644 index 0000000..809e3e6 --- /dev/null +++ b/elsasserlib/man/summary_heatmap.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwplot.R +\name{summary_heatmap} +\alias{summary_heatmap} +\title{Plot a pretty heatmap using pheatmap library} +\usage{ +summary_heatmap(values, title, size = 35, file_out = NA) +} +\arguments{ +\item{values}{Value matrix.} + +\item{title}{Plot title.} + +\item{size}{Square size in points.} + +\item{file_out}{Optional file output.} +} +\value{ +pheatmap object +} +\description{ +This function ignores NA values to calculate min and max values. +} diff --git a/elsasserlib/man/validate_categories.Rd b/elsasserlib/man/validate_categories.Rd new file mode 100644 index 0000000..2c0a229 --- /dev/null +++ b/elsasserlib/man/validate_categories.Rd @@ -0,0 +1,15 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwtools.R +\name{validate_categories} +\alias{validate_categories} +\title{Validate a category array} +\usage{ +validate_categories(cat_values) +} +\arguments{ +\item{cat_values}{An array of values} +} +\description{ +Checks whether the number of categories is reasonable for an aggregation. +Throws a warning if it finds more than 50 different values. +} diff --git a/elsasserlib/man/validate_filelist.Rd b/elsasserlib/man/validate_filelist.Rd new file mode 100644 index 0000000..c245d76 --- /dev/null +++ b/elsasserlib/man/validate_filelist.Rd @@ -0,0 +1,14 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwtools.R +\name{validate_filelist} +\alias{validate_filelist} +\title{Validate an array of paths} +\usage{ +validate_filelist(filelist) +} +\arguments{ +\item{filelist}{An array of files} +} +\description{ +Check that a list of files is valid: not empty and contents exist. +} diff --git a/elsasserlib/man/validate_group_col.Rd b/elsasserlib/man/validate_group_col.Rd new file mode 100644 index 0000000..b5d9ebb --- /dev/null +++ b/elsasserlib/man/validate_group_col.Rd @@ -0,0 +1,16 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bwtools.R +\name{validate_group_col} +\alias{validate_group_col} +\title{Validate that group col exists in granges} +\usage{ +validate_group_col(granges, group_col) +} +\arguments{ +\item{granges}{GRanges object to check} + +\item{group_col}{Group column name. Usually, name.} +} +\description{ +Validate that group col exists in granges +} diff --git a/elsasserlib/tests/testthat.R b/elsasserlib/tests/testthat.R new file mode 100644 index 0000000..f3e46c3 --- /dev/null +++ b/elsasserlib/tests/testthat.R @@ -0,0 +1,4 @@ +library(testthat) +library(elsasserlib) + +test_check("elsasserlib") diff --git a/elsasserlib/tests/testthat/test_bwplot.R b/elsasserlib/tests/testthat/test_bwplot.R new file mode 100644 index 0000000..b337cb6 --- /dev/null +++ b/elsasserlib/tests/testthat/test_bwplot.R @@ -0,0 +1,255 @@ +context("Test functions for bigWig plots") +library(GenomicRanges) +library(testthat) +library(mockery) + +get_file_path <- function(filename) { + system.file("extdata", filename, package = "elsasserlib") +} + +bw1 <- get_file_path("ES_H33_00h_rep1_hoxc.bw") +bw2 <- get_file_path("ES_H33_03h_rep1_hoxc.bw") +bg_bw <- get_file_path("ES_H33_inp_rep1_hoxc.bw") +bed <- get_file_path("chromhmm_hoxc.bed") + +bw_limits <- GRanges(seqnames = c("chr15"), + ranges = IRanges(c(102723600, 102959000))) + +reduced_bins <- bw_bins(bw1, selection = bw_limits, labels = "x") +reduced_bins_2 <- bw_bins(bw2, selection = bw_limits, labels = "y") +summary_values <- bw_bed(c(bw1, bw2), bed, aggregate_by = "mean") +profile_values <- bw_profile(bw1, + bedfile = bed, + upstream = 1000, + downstream = 1000 + ) + +test_that("Setup files exist", { + expect_true(file_test("-f", bw1)) + expect_true(file_test("-f", bw2)) + expect_true(file_test("-f", bg_bw)) + expect_true(file_test("-f", bed)) +}) + +test_that("plot_bw_bins_scatter with defaults returns a plot", { + m <- mock(reduced_bins, reduced_bins_2) + with_mock(bw_bins = m, { + p <- plot_bw_bins_scatter(bw1, bw2) + expect_is(p, "ggplot") + }) +}) + +test_that("plot_bw_bins_scatter with highlight set returns a plot", { + m <- mock(reduced_bins, reduced_bins_2) + with_mock(bw_bins = m, { + p <- plot_bw_bins_scatter(bw1, bw2, highlight = bed) + expect_is(p, "ggplot") + }) +}) + +test_that("plot_bw_bins_scatter with bg files passes on parameters", { + m <- mock(reduced_bins, reduced_bins_2) + with_mock(bw_bins = m, { + p <- plot_bw_bins_scatter(bw1, bw2, bg_x = bg_bw, bg_y = bg_bw) + }) + + expect_call(m, 1, + bw_bins( + x, + bg_bwfiles = bg_x, + bin_size = bin_size, + genome = genome, + per_locus_stat = per_locus_stat, + norm_func = norm_func_x, + labels = "x" + ) + ) + + expect_args(m, 1, + x = bw1, + bg_bwfiles = bg_bw, + bin_size = 10000, + genome = "mm9", + per_locus_stat = "mean", + norm_func = identity, + labels = "x" + ) + + expect_args(m, 2, + x = bw2, + bg_bwfiles = bg_bw, + bin_size = 10000, + genome = "mm9", + per_locus_stat = "mean", + norm_func = identity, + labels = "y" + ) +}) + +test_that("plot_bw_bins_violin with defaults returns a plot", { + m <- mock(reduced_bins, reduced_bins_2) + with_mock(bw_bins = m, { + p <- plot_bw_bins_violin(c(bw1, bw2)) + expect_is(p, "ggplot") + }) +}) + +test_that("plot_bw_bins_violin with bg files passes on parameters", { + m <- mock(reduced_bins, reduced_bins_2) + with_mock(bw_bins = m, { + p <- plot_bw_bins_violin(c(bw1, bw2), + bg_bwfiles = c(bg_bw, bg_bw), + labels = c("A", "B"), + bin_size = 5000, + norm_func = log2, + genome = "hg38", + ) + }) + + expect_call(m, 1, + bw_bins(bwfiles, + bg_bwfiles = bg_bwfiles, + labels = labels, + bin_size = bin_size, + genome = genome, + per_locus_stat = per_locus_stat, + norm_func = norm_func + ) + ) + + expect_args(m, 1, + bwfiles = c(bw1, bw2), + bg_bwfiles = c(bg_bw, bg_bw), + labels = c("A", "B"), + bin_size = 5000, + genome = "hg38", + per_locus_stat = "mean", + norm_func = log2 + ) + +}) + +test_that("plot_bw_bins_violin with highlight returns a plot with jitter", { + m <- mock(reduced_bins, reduced_bins_2) + with_mock(bw_bins = m, { + p <- plot_bw_bins_violin(c(bw1, bw2), + bg_bwfiles = c(bg_bw, bg_bw), + labels = c("A", "B"), + highlight = bed, + bin_size = 5000, + norm_func = log2, + genome = "hg38", + ) + }) + + expect_call(m, 1, + bw_bins(bwfiles, + bg_bwfiles = bg_bwfiles, + labels = labels, + bin_size = bin_size, + genome = genome, + per_locus_stat = per_locus_stat, + norm_func = norm_func + ) + ) + + expect_args(m, 1, + bwfiles = c(bw1, bw2), + bg_bwfiles = c(bg_bw, bg_bw), + labels = c("A", "B"), + bin_size = 5000, + genome = "hg38", + per_locus_stat = "mean", + norm_func = log2 + ) + +}) + +test_that( + "plot_bw_bed_summary_heatmap with defaults returns a pheatmap object", { + m <- mock(summary_values) + with_mock(bw_bed = m, { + p <- plot_bw_bed_summary_heatmap(c(bw1, bw2), bedfile = bed) + expect_is(p, "pheatmap") + }) +}) + +test_that( + "plot_bw_bed_summary_heatmap passes parameters on", { + m <- mock(summary_values) + with_mock(bw_bed = m, { + p <- plot_bw_bed_summary_heatmap(c(bw1, bw2), + bedfile = bed, + bg_bwfiles <- c(bg_bw, bg_bw), + aggregate_by = "median", + norm_func = log2 + ) + }) + + expect_call(m, 1, + bw_bed(bwfiles, + bedfile, + bg_bwfiles = bg_bwfiles, + aggregate_by = aggregate_by, + norm_func = norm_func + ) + ) + + expect_args(m, 1, + bwfiles = c(bw1, bw2), + bedfile = bed, + bg_bwfiles = c(bg_bw, bg_bw), + aggregate_by = "median", + norm_func = log2 + ) +}) + +test_that( + "plot_bw_profile with defaults returns a ggplot object", { + m <- mock(profile_values) + with_mock(bw_profile = m, { + p <- plot_bw_profile(c(bw1, bw2), bedfile = bed) + expect_is(p, "ggplot") + }) +}) + +test_that( + "plot_bw_profile passes on parameters to bw_profile call", { + m <- mock(profile_values) + with_mock(bw_profile = m, { + p <- plot_bw_profile(c(bw1, bw2), + bedfile = bed, + bg_bwfiles = c(bg_bw, bg_bw), + mode = "start", + bin_size = 1000, + upstream = 1500, + downstream = 1500, + ignore_strand = TRUE, + show_error = TRUE, + norm_func = log2) + }) + + expect_call(m, 1, + bw_profile(bwfiles, bedfile, + bg_bwfiles = bg_bwfiles, + mode = mode, + bin_size = bin_size, + upstream = upstream, + downstream = downstream, + ignore_strand = ignore_strand, + norm_func = norm_func + ) + ) + + expect_args(m, 1, + c(bw1, bw2), + bedfile = bed, + bg_bwfiles = c(bg_bw, bg_bw), + mode = "start", + bin_size = 1000, + upstream = 1500, + downstream = 1500, + ignore_strand = TRUE, + norm_func = log2 + ) +}) diff --git a/elsasserlib/tests/testthat/test_bwtools.R b/elsasserlib/tests/testthat/test_bwtools.R new file mode 100644 index 0000000..81b8c9b --- /dev/null +++ b/elsasserlib/tests/testthat/test_bwtools.R @@ -0,0 +1,587 @@ +context("Test functions for bigWig handling") +library(GenomicRanges) +library(rtracklayer) + + +toy_example <- function(bw1, + bw2, + bw_special, + bed_with_names, + bg1, + bg2, + unnamed_bed) { + granges <- GRanges( + seqnames = Rle(c("chr1", "chr2"), c(10, 10)), + ranges = IRanges(c(seq(1, 181, by = 20), seq(1, 181, by = 20)), + c(seq(20, 200, by = 20), seq(20, 200, by = 20))), + score = 1:20 + ) + + gr2 <- GRanges( + seqnames = Rle(c("chr1", "chr2"), c(10, 10)), + ranges = IRanges(c(seq(1, 181, by = 20), seq(1, 181, by = 20)), + c(seq(20, 200, by = 20), seq(20, 200, by = 20))), + score = 20:1 + ) + + gr_bg1 <- GRanges( + seqnames = Rle(c("chr1", "chr2"), c(10, 10)), + ranges = IRanges(c(seq(1, 181, by = 20), seq(1, 181, by = 20)), + c(seq(20, 200, by = 20), seq(20, 200, by = 20))), + score = rep(1, 20) + ) + + gr_bg2 <- GRanges( + seqnames = Rle(c("chr1", "chr2"), c(10, 10)), + ranges = IRanges(c(seq(1, 181, by = 20), seq(1, 181, by = 20)), + c(seq(20, 200, by = 20), seq(20, 200, by = 20))), + score = rep(2, 20) + ) + + labeled_gr <- GRanges( + seqnames = c("chr1", "chr1", "chr2", "chr2", "chr2"), + ranges = IRanges(c(21, 61, 21, 111, 161), + c(40, 100, 40, 130, 180)) + ) + + export(labeled_gr, unnamed_bed) + labeled_gr$name <- c("typeA", "typeB", "typeA", "typeB", "typeB") + + chromsizes <- c(200, 200) + + seqlengths(granges) <- chromsizes + seqlengths(gr2) <- chromsizes + seqlengths(labeled_gr) <- chromsizes + seqlengths(gr_bg1) <- chromsizes + seqlengths(gr_bg2) <- chromsizes + + export(granges, bw1) + export(gr2, bw2) + export(gr2, bw_special) + export(labeled_gr, bed_with_names) + export(gr_bg1, bg1) + export(gr_bg2, bg2) +} + +bw1 <- tempfile("bigwig", fileext = ".bw") +bw2 <- tempfile("bigwig", fileext = ".bw") +bg1 <- tempfile("bigwig_bg1", fileext = ".bw") +bg2 <- tempfile("bigwig_bg2", fileext = ".bw") +bw_special <- tempfile("bigwig", fileext = ".bw") +bed_with_names <- tempfile("bed", fileext = ".bed") +unnamed_bed <- tempfile("bed2", fileext=".bed") + +tiles <- tileGenome(c(chr1 = 200, chr2 = 200), + tilewidth = 20, + cut.last.tile.in.chrom = TRUE +) + + +setup(toy_example(bw1, bw2, bw_special, bed_with_names, bg1, bg2, unnamed_bed)) + +teardown({ + unlink(c(bw1, bw2, bg1, bg2, bw_special, bed_with_names)) +}) + +test_that("Setup files exist", { + expect_true(file_test("-f", bw1)) + expect_true(file_test("-f", bw2)) + expect_true(file_test("-f", bg1)) + expect_true(file_test("-f", bg2)) + expect_true(file_test("-f", bed_with_names)) + expect_true(file_test("-f", bw_special)) +}) + +test_that("bw_ranges returns a GRanges object", { + bins <- bw_ranges(bw1, tiles, per_locus_stat = "mean") + expect_is(bins, "GRanges") + +}) + +test_that("bw_ranges returns correct values", { + bins <- bw_ranges(bw1, tiles, per_locus_stat = "mean") + + expect_equal(bins[1]$score, 1) + expect_equal(bins[2]$score, 2) + expect_equal(bins[10]$score, 10) +}) + +test_that("bw_ranges returns correct values on subset", { + subset <- GRanges(seqnames = c("chr1"), + ranges = IRanges(c(10, 40))) + + bins <- bw_ranges(bw1, tiles, per_locus_stat = "mean", selection = subset) + + expect_equal(bins[1]$score, 1) + expect_equal(bins[2]$score, 2) + expect_equal(length(bins), 2) +}) + +test_that("multi_bw_ranges returns correct values", { + values <- multi_bw_ranges(c(bw1, bw2), c("bw1", "bw2"), tiles) + + expect_is(values, "GRanges") + expect_equal(values[1]$bw1, 1) + expect_equal(values[1]$bw2, 20) + expect_equal(values[2]$bw1, 2) + expect_equal(values[2]$bw2, 19) +}) + +test_that( + "multi_bw_ranges_norm with bwfiles == background returns all 1 values", { + values <- multi_bw_ranges_norm(c(bw1, bw2), + bg_bwfilelist = c(bw1, bw2), + c("bw1", "bw2"), + tiles, + norm_func = identity + ) + + expect_is(values, "GRanges") + expect_equal(values[1]$bw1, 1) + expect_equal(values[1]$bw2, 1) + expect_equal(values[2]$bw1, 1) + expect_equal(values[2]$bw2, 1) +}) + +test_that("multi_bw_ranges_norm returns correct values", { + values <- multi_bw_ranges_norm(c(bw1, bw2), + bg_bwfilelist = c(bg1, bg2), + c("bw1", "bw2"), + tiles, + norm_func = identity + ) + + expect_is(values, "GRanges") + expect_equal(values[1]$bw1, 1) + expect_equal(values[2]$bw1, 2) + expect_equal(values[1]$bw2, 10) + expect_equal(values[2]$bw2, 9.5) +}) + +test_that( + "multi_bw_ranges_norm fails on background length not matching bwlist", { + expect_error({ + values <- multi_bw_ranges_norm(c(bw1, bw2), + bg_bwfilelist = c(bg1), + c("bw1", "bw2"), + tiles, + norm_func = identity + ) + }, + "Background and signal bwfile lists must have the same length.") +}) + +test_that("multi_bw_ranges returns correct values for single bigWig", { + values <- multi_bw_ranges(bw1, "bw1", tiles) + + expect_is(values, "GRanges") + expect_equal(values[1]$bw1, 1) + expect_equal(values[2]$bw1, 2) +}) + +test_that("multi_bw_ranges returns correct values on subset", { + subset <- GRanges(seqnames = c("chr1"), ranges = IRanges(c(30, 50))) + + values <- multi_bw_ranges(c(bw1, bw2), + c("bw1", "bw2"), + tiles, + selection = subset + ) + + expect_is(values, "GRanges") + expect_equal(values[1]$bw1, 2) + expect_equal(values[1]$bw2, 19) + expect_equal(values[2]$bw1, 3) + expect_equal(values[2]$bw2, 18) +}) + +test_that("bw_bed returns correct per locus values", { + values <- bw_bed(bw1, bed_with_names, labels = "bw1", per_locus_stat = "mean") + expect_is(values, "GRanges") + expect_equal(values[1]$bw1, 2) + expect_equal(values[2]$bw1, 4.5) +}) + +test_that("bw_bins returns correct per locus values", { + values <- bw_bins(bw1, + selection = import(bed_with_names), + labels = "bw1", + per_locus_stat = "mean" + ) + + expect_is(values, "GRanges") + expect_equal(values[1]$bw1, 5.5) + expect_equal(values[2]$bw1, 15.5) +}) + +test_that("bw_bins returns correct per locus values, with bg", { + values <- bw_bins(bw1, + bg_bwfiles = bw1, + selection = import(bed_with_names), + labels = "bw1", + per_locus_stat = "mean" + ) + + # FIXME: underlying normalized values in GRanges object are matrix + # expect_is(values, 'GRanges') + # expect_equal(values[1]$bw1, 1) + # expect_equal(values[2]$bw1, 1) +}) + +test_that("bw_bed returns correct per locus values on multiple files", { + values <- bw_bed(c(bw1, bw2), + bed_with_names, + labels = c("bw1", "bw2"), + per_locus_stat = "mean" + ) + + expect_is(values, "GRanges") + expect_equal(values[1]$bw1, 2) + expect_equal(values[2]$bw1, 4.5) + expect_equal(values[1]$bw2, 19) + expect_equal(values[2]$bw2, 16.5) +}) + +test_that( + "bw_bed returns correct per locus values on multiple files, with bg", { + values <- bw_bed(c(bw1, bw2), + bg_bwfiles = c(bg1, bg2), + bed_with_names, + labels = c("bw1", "bw2"), + per_locus_stat = "mean" + ) + + expect_is(values, "GRanges") + expect_equal(values[1]$bw2, 9.5) + expect_equal(values[2]$bw2, 8.25) +}) + + +test_that("bw_bed handles default names with special characters", { + values <- bw_bed(bw_special, + bed_with_names, + aggregate_by = "true_mean" + ) + + expect_is(values, "data.frame") +}) + +test_that("bw_bed crashes on wrong number of labels for multiple files", { + expect_error({ + values <- bw_bed(c(bw1, bw2), bed_with_names, + labels = "bw1", + per_locus_stat = "mean" + ) + }, + "BigWig file list and column names must have the same length.") + +}) + +test_that("bw_bed returns correct mean-of-means aggregated values", { + values <- bw_bed(bw1, + bed_with_names, + labels = "bw1", + per_locus_stat = "mean", + aggregate_by = "mean" + ) + + expect_is(values, "data.frame") + expect_equal(values["typeA", "bw1"], 7) + expect_equal(values["typeB", "bw1"], 13.3333333333) +}) + +test_that("bw_bed returns correct true_mean aggregated values", { + values <- bw_bed(bw1, bed_with_names, + labels = "bw1", + per_locus_stat = "mean", + aggregate_by = "true_mean" + ) + + expect_is(values, "data.frame") + expect_equal(values["typeA", "bw1"], 7) + expect_equal(values["typeB", "bw1"], 11.125) +}) + +test_that("bw_bed on an empty list throws an error", { + expect_error({ + values <- bw_bed(c(), bed_with_names, + per_locus_stat = "mean", + aggregate_by = "true_mean" + ) + }, + "File list provided is empty.") + +}) + +test_that("bw_profile on an empty list throws an error", { + expect_error({ + values <- bw_profile(c(), + bed_with_names, + labels = NULL + ) + }, + "File list provided is empty.") + +}) + +test_that("bw_bed on non-existing bed file throws an error", { + expect_error({ + values <- bw_bed(bw1, + "invalidname.bed", + per_locus_stat = "mean", + aggregate_by = "true_mean" + ) + }, + "Files not found: invalidname.bed") +}) + +test_that("bw_profile on non-existing bed file throws an error", { + expect_error({ + values <- bw_profile(bw1, + bedfile = "invalidname.bed", + labels = NULL + ) + }, + "Files not found: invalidname.bed") +}) + + +test_that("bw_bed errors on non existing files on bwlist", { + expect_error({ + values <- bw_bed(c(bw1, "invalidname.bw"), + bed_with_names, + per_locus_stat = "mean", + aggregate_by = "true_mean" + ) + }, + "Files not found: invalidname.bw") +}) + + +test_that("bw_profile errors on non existing files on bwlist", { + expect_error({ + values <- bw_profile(c(bw1, "invalidname.bw"), + bedfile = bed_with_names, + labels = NULL + ) + }, + "Files not found: invalidname.bw") +}) + + +test_that("bw_profile runs quiet on valid parameters", { + expect_silent({ + values <- bw_profile(c(bw1, bw2), + bedfile = bed_with_names, + upstream = 1, + downstream = 1, + bin_size = 1 + ) + }) + +}) + +test_that("bw_profile runs quiet on valid parameters, mode start", { + expect_silent({ + values <- bw_profile(c(bw1, bw2), + bedfile = bed_with_names, + upstream = 1, + downstream = 1, + bin_size = 1, + mode = "start" + ) + }) + +}) + +test_that( + "bw_profile runs quiet on valid parameters, mode start, with background", { + expect_silent({ + values <- bw_profile(c(bw1, bw2), + bg_bwfiles = c(bg1, bg2), + bedfile = bed_with_names, + upstream = 1, + downstream = 1, + bin_size = 1, + mode = "start" + ) + }) + +}) + +test_that("bw_profile normalized returns correct values", { + values <- bw_profile(c(bw1, bw2), + bg_bwfiles = c(bw1, bw2), + bedfile = bed_with_names, + upstream = 1, + downstream = 1, + bin_size = 1, + mode = "start" + ) + + expect_is(values, "data.frame") + expect_equal(values[1, "mean"], 1) + expect_equal(values[2, "mean"], 1) + +}) + +test_that("bw_profile fails if labels and bwfiles have different length", { + expect_error({ + values <- bw_profile(c(bw1, bw2), + bedfile = bed_with_names, + labels = c("bw1"), + upstream = 1, + downstream = 1, + bin_size = 1, + mode = "start" + ) + }, + "labels and bwfiles must have the same length") +}) + + +test_that("bw_profile runs quiet on valid parameters, mode end", { + expect_silent({ + values <- bw_profile(c(bw1, bw2), + bedfile = bed_with_names, + upstream = 1, + downstream = 1, + bin_size = 1, + mode = "end" + ) + }) + +}) + + +test_that("bw_profile runs quiet on valid parameters with background", { + expect_silent({ + values <- bw_profile(c(bw1, bw2), + bg_bwfiles = c(bg1, bg2), + bedfile = bed_with_names, + upstream = 1, + downstream = 1, + bin_size = 1 + ) + }) + +}) + +test_that("bw_profile throws error on flanking region smaller than bin size", { + expect_error({ + values <- bw_profile(c(bw1, bw2), + bedfile = bed_with_names, + upstream = 1, + downstream = 1, + bin_size = 10 + ) + }, + "bin size must be smaller than flanking regions") + +}) + +test_that("bw_profile throws error on negative bin size", { + expect_error({ + values <- bw_profile(c(bw1, bw2), + bedfile = bed_with_names, + upstream = 1, + downstream = 1, + bin_size = -10 + ) + }, + "bin size must be a positive value: -10") + +}) + +test_that("bw_profile throws error on negative upstream value", { + expect_error({ + values <- bw_profile(c(bw1, bw2), + bedfile = bed_with_names, + upstream = -10, + downstream = 10, + bin_size = 10 + ) + }, + "upstream size must be a positive value: -10") + +}) + +test_that("bw_profile throws error on negative downstream value", { + expect_error({ + values <- bw_profile(c(bw1, bw2), + bedfile = bed_with_names, + upstream = 10, + downstream = -10, + bin_size = 10 + ) + }, + "downstream size must be a positive value: -10") + +}) + +test_that("bw_bed returns correct median-of-means aggregated values", { + values <- bw_bed(bw1, bed_with_names, + labels = "bw1", + per_locus_stat = "mean", + aggregate_by = "median" + ) + + expect_is(values, "data.frame") + expect_equal(values["typeA", "bw1"], 7) + expect_equal(values["typeB", "bw1"], 16.5) +}) + +test_that("build_bins crashes on unknown or not included genome", { + expect_error({ + build_bins(bin_size = 10000, genome = "mm10") + }, + "Supported genomes: mm9, hg38") +}) + +test_that("build_bins runs for mm9", { + values <- build_bins(bin_size = 50000, genome = "mm9") + expect_is(values, "GRanges") +}) + +test_that("build_bins creates bins of correct size", { + values <- build_bins(bin_size = 50000, genome = "mm9") + expect_is(values, "GRanges") + expect_equal(width(ranges(values[1])), 50000) +}) + +test_that("build_bins runs for hg38", { + values <- build_bins(bin_size = 50000, genome = "hg38") + expect_is(values, "GRanges") +}) + +test_that("bw_bed throws error on not implemented aggregate_by", { + expect_error( + values <- bw_bed(bw1, bed_with_names, + labels = "bw1", + per_locus_stat = "mean", + aggregate_by = "max" + ) + ) +}) + +test_that("bw_bed runs with background and aggregate_by parameter", { + values <- bw_bed(bw1, bed_with_names, bg_bwfiles = bw2, + labels = "bw1", + per_locus_stat = "mean", + aggregate_by = "mean" + ) + + expect_is(values, "data.frame") +}) + +test_that("bw_bed fails if aggregate_by in an unnamed bed file", { + expect_error({ + values <- bw_bed(bw1, unnamed_bed, bg_bwfiles = bw2, + labels = "bw1", + per_locus_stat = "mean", + aggregate_by = "mean" + )}, + "missing values in 'row.names' are not allowed" + ) + +}) diff --git a/elsasserlib/vignettes/bw-tools.Rmd b/elsasserlib/vignettes/bw-tools.Rmd deleted file mode 100644 index a4d1066..0000000 --- a/elsasserlib/vignettes/bw-tools.Rmd +++ /dev/null @@ -1,203 +0,0 @@ ---- -title: "BigWig tools" -author: "Carmen Navarro" -date: "`r Sys.Date()`" -output: - prettydoc::html_pretty: - theme: cayman - highlight: github - toc: true - -vignette: > - %\VignetteIndexEntry{bw-tools} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteDepends{elsasserlib} - %\VignetteEncoding{UTF-8} - \usepackage[utf8]{inputenc} ---- - -```{r, include = FALSE} -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>" -) -``` - -```{r setup, message=FALSE} -library(elsasserlib) -library(ggplot2) -library(rtracklayer) -``` - -## Summary - -This module contains functions to handle BigWig files to perform analyses we -usually do, like summarizing the information across *loci* in a `BED` file or making -a binned analysis of a set of `BigWig` files. - -## Binned analysis - -You can use the function `bw_bins` to average a `bigWig` file over a set of -fixed-size bins genome-wide. - -```{r} -# Example bw file included in the package. -# Note: this should be a path for an actual file. -bw.file <- system.file("extdata", "example.1000bs.bw", package = "elsasserlib") - -# Now we bin it -binned.score <- bw_bins(bw.file, colnames=c('score'), bsize=10000) - -head(binned.score) - -binned.score[10000, ] -``` - -Yes, not very informative, since signal at the first bins is zero. - -Note that the `bw_bins` operation is **not immediate**. It will take some seconds to execute -in a regular laptop. In my experience, it goes between 30-40 seconds depending -on the size of the `bigWig` file for bin sizes of `5000`-`10000`. If you want -higher resolutions, consider precomputing these values beforehand. - -`bw_bins` supports multi `bigWig` file, so if you provide several files, you will -get a `GRanges` object with several columns, one per file. - -Additional parameters of `bw_bins` function are `per.locus.stat`, that tells -`bw_bins` which function is used to aggregate the values within a bin or locus. - - -```{r} -# Example bw file included in the package. -# Note: this should be a path for an actual file. -bw.file <- system.file("extdata", "example.1000bs.bw", package = "elsasserlib") -bw.file.2 <- system.file("extdata", "example.1000bs.bw", package = "elsasserlib") - -# Now we bin it -# The files are actually the same, so we need to provide column names to avoid -# confusion: -binned.score <- bw_bins(c(bw.file, bw.file.2), - colnames=c('file1', 'file2'), - bsize=10000) - -head(binned.score) -``` - - -## Locus-based analysis - -You can also intersect a `bigWig` file with a set of loci specified in a `BED` -file using `bw_bed`: - -```{r} -# Example bw file included in the package. -# Note: this should be a path for an actual file. -bw.file <- system.file("extdata", "example.1000bs.bw", package="elsasserlib") -bed.file <- system.file("extdata", "ChromHMM7_subsample.bed", package="elsasserlib") - -# We intersect these two files -summary.score <- bw_bed(bw.file, bed.file, per.locus.stat = "mean") - -# We get a GRanges object which one score per locus -head(summary.score) -``` - -Note the `per.locus.stat` parameter is a parameter that is passed to `rtracklayer`. -They provide functionality to calculate min, max, mean, sd and coverage, but the -meaning of coverage results is not clear to me, so I would recommend using mean, -which is the actual mean of the coverage calculated in the analysed bin. - -When intersecting a `BED` file with a `BigWig` file, sometimes we have a `name` -field that has some category, for instance: - -```{r} -# This import is `rtracklayer` import -loci <- import(bed.file) - -table(as.factor(loci$name)) -``` - -In this case, we may be interested in obtaining a summarized table. This can -be done with the same function: - -```{r} -# We intersect these two files -summary.score <- bw_bed(bw.file, - bed.file, - colnames=c('score'), - per.locus.stat = "mean", - aggregate.by=mean) - -# We get a GRanges object which one score per locus -summary.score -``` - -Note the difference between `per.locus.stats`, which aggregates data within a -single bin or loci, and `aggregate.by`, which calculates an aggregated value -based on the whole distribution of bins that belong to a category. For instance, -we can have **mean** coverage *per locus* and calculate the **median** of all -those, to avoid influence of outliers: - -```{r} -# We intersect these two files, but this time we summarize by median -median.score <- bw_bed(bw.file, - bed.file, - per.locus.stat="mean", - aggregate.by=median) - -# We get a GRanges object which one score group of loci -median.score -``` - -With this info you could already have an idea of which loci groups have more -coverage than others: - -```{r} -ggplot(as.data.frame(summary.score), aes(x=name, y=score, fill=name)) + - geom_bar(stat='identity') + - ylab('Mean coverage') + - xlab('ChromHMM7 (subsampled)') + - theme_elsasserlab_screen() -``` - - -## Computing values on several bigWig files - -Many times you will want to look at several `bigWig` files at the same time. -Both functions `bw_bins` and `bw_bed` accept both single `bigWig` files and -lists, as shown above. - -## Returning type of bigWig functions - -The value returned is specified in each function documentation, but, in general, -a function will return `GRanges` objects when possible, to keep the structure -of the original `BED` file or bins. - -However, there are cases where this is not possible or meaningful, for instance, -when calling `bw_bed` with an `aggregate.by` parameter, the result is an -aggregated score of *loci* labeled with a certain value, so we lose the -`locus` information. In these cases, a `DataFrame` is returned. - -## Other useful functions - -### Making a GRanges object as a binned genome - -To do this, you only need to call the function `build_bins`: - -```{r} -mm9.genome.bins.10000 <- build_bins(bsize=10000, genome="mm9") - -head(mm9.genome.bins.10000) -``` - -`build_bins` relies on `GenomeInfoDb` package and additional data contained in -`BSgenome.Mmusculus.UCSC.mm9` and `BSgenome.Hsapiens.UCSC.hg38`, from which it -takes sequence info necessary to build the bins. I included these because in -the lab we mostly use these two references. If you want to use another -genome, you will have to make the tiling yourself. This is pretty easy, really -(see documentation on `tileGenome` to do this). - -## More documentation -Even though this is a mixed-code package, it is documented as any proper R -package is, which means when you're programming you can access help -for functions using `?` operator: `?bw_bins`. diff --git a/notebooks/bedfile_template.Rmd b/notebooks/bedfile_template.Rmd new file mode 100644 index 0000000..22ff4bf --- /dev/null +++ b/notebooks/bedfile_template.Rmd @@ -0,0 +1,162 @@ +--- +title: "Template for summarized heatmaps" +output: html_notebook +--- +```{r echo=FALSE, messages=FALSE} +library(elsasserlib) +library(ggplot2) +library(reshape2) +library(ggpubr) +# Ongoing Issue with mcols <- +library(rtracklayer) +``` + +This is a template markdown file that creates a summary heatmap (like the ones +we make for ChromHMM17) + +## Calculating the aggregated values + +This is the most computationally costful part. I recommend to put results in a +table after running this if you want to play with them. Additionally, this notebook +contains a parameter `cache=TRUE` for the costful chunks of code, which means +if you don't touch those chunks after running, it will not try to run them +again. + +If you want, first set your working directory to the one where the data is: + +```{r} +expdir <- 'path-to-your-experiment' + +``` + +Now this will plot and label every bigwig file on a certain directory: + +```{r} + +run.dir <- './bw' +bwpath <- paste(expdir, run.dir, sep='/') + +# You can actually do this with any other type of bed file. The important part +# is that the name field contains categories that can be aggregated. For instance +# you could also run genes_hi / genes_med and so on +bed <- 'path-to-chromhmmfile-or-any-other-bed' + +# This lists all the files in the path you put there +bwfiles <- list.files(bwpath) + +# And outputs them so you can see if there is any problem with it. +bwfiles + +``` + +Now we calculate the aggregated values across all these files, using +true mean: + +```{r cache=TRUE} +values <- bw_bed(paste(bwpath, bwfiles, sep='/'), + bed, + aggregate.by='true_mean') +``` + +We get a `data.frame` object where each column is a file and each row a ChromHMM +value: +```{r} +head(values) +``` + +Reorder the values: + +```{r} +library(stringr) + +# This is a trick for naturally sort the values when they have numbers but they +# don't have padded zeros to the left. Basically applies to ChromHMM values +order <- str_sort(values$name, numeric=TRUE) + +rownames(values) <- values$name +write.csv(values[order, bwfiles], 'your_bedfile_truemeans.csv') +``` + +## Plotting the tables + +This uses `pheatmap` and `RColorBrewer` to plot the values. I will add this +functionality to the package, but as of now, here is code that will do. You +may need to adapt `fig.width` and `fig.height` on the header, as pheatmap does +not seem to be aware of the limits of the page. + +```{r fig.width=12, fig.height=7} +library(pheatmap) +library(RColorBrewer) + +# This computes limits and breaks for the color scale to look always the same, +# regardless of only positive values or -max, 0, +max. +compute.lims <- function(mat) { + maxmat <- mat + maxmat[is.na(maxmat)] <- -Inf + maxval <- max(maxmat) + + minmat <- mat + minmat[is.na(minmat)] <- Inf + minval <- min(minmat) + + nsteps <- 21.0 + + breaklim <- ceiling(abs(max(abs(c(minval, maxval))))) + + # Compute a reasonable amount of steps? + stepsize <- 2*breaklim / nsteps + + breakslist <- seq(-breaklim, +breaklim, by=stepsize) + breakslist +} + +# Just a wrapper that sets some defaults on pheatmap +summary_heatmap <- function(values, title, size=35) { + bcolor <- "white" + + breakslist <- compute.lims(values) + palette <- colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(length(breakslist)) + + cellsize <- size + + pheatmap(values, + main=title, + cellwidth=cellsize, + cluster_rows=F, + cluster_cols=F, + cellheight=cellsize, + border_color=bcolor, + breaks=breakslist, + color=palette, + display_numbers=TRUE) +} + + +values.from.table <- read.csv('./chromhmm17_truemean_runs.csv', header=T, row.names = 1) +summary_heatmap(t(values.from.table), "Log2 True mean per category") +``` + +## Checking other aggregating functions + +We can check variability for median or mean (mean-of-means) values. + +```{r cache=TRUE} +median.values <- bw_bed(paste(bwpath, bwfiles, sep='/'), + bed, + aggregate.by='median') + +``` + +```{r} +rownames(median.values) <- median.values$name + +write.csv(median.values[order, bwfiles], 'your_bedfile_median_runs.csv') + +median.values[order, bwfiles] +``` + +You can plot also the median values: + +```{r fig.width=12, fig.height=7} +summary_heatmap(t(median.values[order, bwfiles]), "Median mean per category") +```