Hi everyone— I’m working with a team that’s generating single cell ATAC data in large amounts and am designing the framework of an S4 object to facilitate analyses in R. I have a couple of high-level questions that I wanted to pose early to hopefully attain some community guidance in the implementation of these data structures.
Question on S4 scATAC Structure-- It’s easy to imagine scATAC data as a matrix where the rows are particular peaks and the columns are individual samples. We already have such an impressive volume of data, such that if stored in an ordinary matrix, we run into ~20 GB objects. As these data are very sparse, we store the peak values in a sparse matrix (through the Matrix library). I wanted to collate the peak information (probably in GRanges object) and sample information (in a data frame) as well as some potential meta data in an S4 object. Easy enough, sure, but after looking at the scRNA structure (e.g. scater <https://bioconductor.org/packages/devel/bioc/vignettes/scater/inst/doc/vignette.html>), I feel like I should be considering how to inherit some of the nice properties from the canonical `ExpressionSet` structure. However, since my constraints aren’t directly compatible (namely the featureData slot really needs to be a GRanges and the exprs slot must be an object from Matrix), it wasn’t clear to me how to maximize the inheritance properties while adjusting to my unique constraints. Also, it wasn’t clear to me whether or not I could inherent `SummarizedExperiment` due to the different nature of the sparse matrix. Does anyone have any advice on this structure? Question on reading sparse matrices from disk-- I’m trying to work out the best to selectively read certain rows and columns from a sparse matrix on disk into memory. I anticipate a time fairly soon that loading our full scATAC data, even in sparse matrices, is going to be untenable. Any matrix reading/slicing implementations that I’ve seen don’t play friendly with sparse matrices. So, I hacked together two solutions— 1) reads and subsets a gzipped matrix with 3 columns (row index; column index; non-zero value) through a system call to awk. 2) converts that same 3 column matrix into an SQLite object and send queries to read values based on indices. The hiccups are that 1) doesn’t play friendly on non-unix platforms and always scans the full file, and 2) is faster for querying, but the binary object is ~7x larger than the gzipped object. I’ve played around with hdf5 as well, but it didn’t seem to give me much back in terms of speed or storage benefits comparatively. Has anyone found an implementation that achieves a decent lookup time and compression, or am I essentially needing to choose between the two? Thanks and have a great weekend! -Caleb [[alternative HTML version deleted]] _______________________________________________ Bioc-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/bioc-devel
