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
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