Apologies for the bump, but is anyone able to help me on this? Or are these questions more appropriate for the general Bioconductor mailing list rather than Bioc-Devel?
Many thanks, Pete ----- Original Message ----- Date: Mon, 10 Feb 2014 13:20:47 +1100 From: Peter Hickey <hic...@wehi.edu.au> To: bioc-devel@r-project.org Subject: [Bioc-devel] Help in designing class based on SummarizedExperiment Message-ID: <e3127fd3-87e5-43da-b056-a633525ee...@wehi.edu.au> Content-Type: text/plain Hi all, Apologies up front for the rather long post. I'm designing a class to store what I call co-methylation m-tuples. These are based on a very simple tab-delimited file format. For example, here are 1-tuples (m = 1): chr pos1 M U chr1 57691 0 1 chr1 59276 1 0 chr1 60408 1 0 chr1 63495 1 0 chr1 63568 2 0 chr1 63627 3 0 2-tuples (m = 2): chr pos1 pos2 MM MU UM UU chr1 567438 567570 0 0 0 2 chr1 567501 567549 0 0 0 35 chr1 567549 567558 0 1 0 139 3-tuples (m = 3): chr pos1 pos2 pos3 MMM MMU MUM MUU UMM UMU UUM UUU chr1 13644 13823 13828 1 0 0 0 0 0 0 0 chr1 14741 14747 14773 1 0 0 0 0 0 0 0 etc. 1-tuples are basically the standard input to an analysis of BS-seq data. I think of these files as being comprised of 3 parts: the 'chr' column (chr), the 'pos' matrix (pos1, pos2, pos3) and the 'counts' matrix (MMM, MMU, MUM, MUU, UMM, UMU, UUM, UUU), when m = 3. For a given value of 'm' there is one 'chr' column, m 'pos' columns and 2^m 'counts' columns. I want to implement a class for these objects as I'm writing a package for the analysis of this type of data. I'd like a GRanges-type object storing the genomic information and a matrix-like object storing the counts. After tinkering around for a while, and doing some reading of the code in packages such as GenomicRanges and bsseq, I decided to extend the SummarizedExperiment class. I now have a prototype but I have some questions and would appreciate feedback on some of my design choices before I translate my existing functions to work with this class of object. Here is the code for the prototype: ##################################################################### library(GenomicRanges) setClass("CoMeth", contains = "SummarizedExperiment") CoMeth <- function(seqnames, pos, counts, m, methylation_type, sample_name, strand = "*", seqlengths = NULL, seqinfo = NULL){ # Argument checks, etc. go here # gr <- GRanges(seqnames = seqnames, ranges = IRanges(start = pos[[1]], end = pos[[length(pos)]]), strand = strand, seqlengths = seqlengths, seqinfo = seqinfo) # The width of each element is defined by the first and last 'pos', e.g. for 3-tuples it is defined by pos1 and pos3. # Need to store the "extra" positions if m > 2. Each additional position is stored as a separate assay if (m > 2){ extra_pos <- lapply(seq(2, m - 1, 1), function(i, pos){ pos[[i]] }, pos = pos) names(extra_pos) <- names(pos)[2:(m-1)] } else { extra_pos <- NULL } assays <- SimpleList(c(counts, extra_pos)) colData <- DataFrame(sample_name = sample_name, m = m, methylation_type = paste0(sort(methylation_type), collapse = '/')) cometh <- SummarizedExperiment(assays = assays, rowData = gr, colData = colData) cometh <- as(cometh, "CoMeth") return(cometh) } And here's some example data: # A function that roughly imitates the output of a call to scan() to read in BS-seq m-tuple data # m is the size of the m-tuples # n is the number of m-tuples # z is the proportion of each column of 'counts' that is zero make_test_data <- function(m, n, z){ seqnames <- list(seqnames = rep('chr1', n)) pos <- lapply(1:m, function(x, n){matrix(seq(from = 1 + x - 1, to = n + x - 1, by = 1), ncol = 1)}, n = n) # Need these to be matrices rather than vectors names(pos) <- paste0('pos', 1:m) # A rough hack to simulate counts where a proportion (z) are 0 and the rest are sampled from Poisson(lambda). Small values of lambda will inflate the zero-count. counts <- mapply(FUN = function(i, z, n, lambda){ nz <- floor(n * (1 - z)) matrix(sample(c(rpois(nz, lambda), rep(0, n - nz))), ncol = 1) }, i = 1:(2 ^ m), z = z, n = n, lambda = 4, SIMPLIFY = FALSE) # Need these to be matrices rather than vectors names(counts) <- sort(do.call(paste0, expand.grid(lapply(seq_len(m), function(x){c('M', 'U')})))) return(c(seqnames, pos, counts)) } m <- 3 # An example using 3-tuples n <- 1000 # A typical value for 3-tuples from a methylC-seq experiment is n = 17,000,000 z <- c(0.2, 0.6, 0.6, 0.7, 0.6, 0.8, 0.8, 0.7) # Typical proportions of each column of 'counts' that are zero when using 3-tuples for a methylC-seq experiment test_data <- make_test_data(n = n, m = m, z = z) cometh <- CoMeth(seqnames = test_data[['seqnames']], pos = test_data[grepl('pos', names(test_data))], counts = test_data[grepl('[MU]', names(test_data))], m = m, methylation_type = 'CG', sample_name = 'test_data') sessionInfo() R version 3.0.2 (2013-09-25) Platform: x86_64-apple-darwin10.8.0 (64-bit) locale: [1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8 attached base packages: [1] parallel stats graphics grDevices utils datasets methods base other attached packages: [1] GenomicRanges_1.14.4 XVector_0.2.0 IRanges_1.20.6 BiocGenerics_0.8.0 loaded via a namespace (and not attached): [1] stats4_3.0.2 tools_3.0.2 ##################################################################### Questions 1. How can I move the 'extra_pos' columns from the assay slot but keep the copy-on-change behaviour? From a design perspective, I think it would make more sense for the 'extra_pos' columns, i.e. ('pos2') for 3-tuples and ('pos2', 'pos3') for 4-tuples etc., to be in their own slot rather than in the assays slot, after all, they aren't assays but rather are additional genomic co-ordinates. The 'extra_pos' fields are fixed (at least until I start subsetting or combining multiple CoMeth objects). My understanding of the the SummarizedExperiment class is that the assays slot is a reference class to avoid excessive copying when changing other slots of a SummarizedExperiment object. So if the 'extra_pos' columns were stored outside of the assays slot then these would have to be copied when any changes are made to the other slots of a CoMeth object, correct? Is there a way to avoid this, i.e. so that these 'extra_pos' columns are stored separately from the assays slot but with the ! copy-on-change behaviour of the assays slot? 2. Is the correct to compute something based on the 'counts' data via the assay() accessor? For example, I might want a helper function getCounts(cometh) that does the equivalent of sapply(X = 1:(2^m), function(i, cometh){assay(cometh, i)}, cometh = cometh). Similarly, I might want to compute the coverage of an m-tuple, which would be the equivalent of rowSums(getCounts(cometh)). Is this the correct way to do this sort of thing? 3. How do I measure the size of a SummarizedExperiment/CoMeth object? For example, with the test data, print(object.size(cometh), units = "auto") < print(object.size(assays(cometh)), units = "auto"), so it seems that the size of the assays slot isn't counted by object.size(). 4. Is it possible to store an Rle-type object in the assays slot of a SummarizedExperiment? 20-80% of the entries in each column of 'counts' are zero and there are often runs of zeros. So I thought that perhaps an Rle representation (column-wise) might be more (memory) efficient. But I can't seem to get an Rle object in the assays slot (I tried via DataFrame); is it even possible? 5. Are there matrix-like objects with Rle columns? I found this thread started by Kasper Hansen (https://stat.ethz.ch/pipermail/bioconductor/2012-June/046473.html) discussing the idea of matrix-like object where the columns are Rle's; I could imagine using such an object for a CoMeth object containing multiple samples, i.e. MMM is a matrix-like object with ncol = # of samples, MMU is matrix-like object with ncol = # of samples, etc. Was anything like this ever implemented? My reading of the previous thread was to use a DataFrame but the "matrix API", e.g. rowSums, doesn't work with DataFrames (and see (4) as to whether it's even possible to store such objects in the assays slot). Many thanks for your help in answering these questions. Any other suggestions on the design of the CoMeth class are appreciated. Thanks, Pete -------------------------------- Peter Hickey, PhD Student/Research Assistant, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Vic 3052, Australia. Ph: +613 9345 2324 hic...@wehi.edu.au http://www.wehi.edu.au ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:6}} _______________________________________________ Bioc-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/bioc-devel