![]() ![]() A MARGIN of 1 will chunk over genes while a MARGIN of 2 will chunk When iterating over matrix and the layers, the MARGINĪrgument tells the loom object which way to chunk the data. Work on any dataset, except for two-dimensional attributes and any graph dataset. The batch.scan method will break a dataset in the loom file into chunks, based on a chunk size given to it. Initializes the iterator and batch.next moves through the iterator. ![]() Use of itertools::ichunk object to chunk through the data in a loom file. This section will cover the former two methods the latterīatch.scan and batch.next are the heart of all chunk-based iteration in the loom object. ![]() The batch.scan, batch.next, map, and apply methods. To combat this problem, loom objects offer native chunk-based iteration through Requirements for holding such a dataset in memory. Not all graphs must be the same length as each other.Įach dataset within layers must have the exact same dimensions as matrix Chunk-based iterationĪs loom files can theoretically hold million-cell datasets, performing analysis on these datasets can be impossible due to the memory Each dataset within a graph must be one-dimensional and all datasets within a graph must be Each graph group will have three datasets that represent the graph inĬoordinate format: a for row indices, b forĬolumn indices, and w for values. Instead, within these groups are groups for Unlike other datasets within a loom file, these are not controlled by matrix. HDF5 libraries will show this specific dimension as the first dimension for two-dimensional datasets, or the length of one-dimensional Within loomR, this must be the second dimension of two-dimensional datasets, or the length of one-dimensional datasets Most other These are one- or two-dimensional datasets where a specific dimension is of length 'n', for row attributes, or 'm', for column attributes. Generally present the data as 'n' rows and 'm' columns This dataset has 'n' genes and 'm' cells.ĭue to the way that loomR presents data, this will appear as 'm' rows and 'n' columns. The dataset that sets the dimensions for most other datasets within a loom file. This structure is enforced to ensure that data remains intact and retriveable when spread across the various datasets in the loom file. Groups for gene-based and cell-based cluster graphs ( row_graphs and col_graphs, respectively),Īnd layers, a group containing alternative representations of the data in matrix.Įach dataset within the loom file has rules as to what size it may be, creating a structure for the entire loom file and all the data within. Groups for gene- and cell-metadata ( row_attrs and col_attrs, respectively), Loom files are an HDF5-based format for storing and interacting with large single-cell RNAseq datasets.Įach loom file has at least six parts to it: This style will be used throughout documentation for loomR as well as any vignettes and tutorials When referring to the pacakge loomR, it will always be written in normal text with the 'l', 'o's, and 'm' lowercased and Otherwise, it will be lowercase.įor loom objects, or the object within R, the word 'loom' will always be lowercase and written in monospaced text. For English, that means if the word 'loom' appears at the beginning of a sentenceĪnd is being used to refer to a loom file, it will be capilatized. Capitalization will be done based on a language's rules forĬapitalization in sentences. ![]() The word 'loom' will be written in normal text. When talking about loom files, or the actual HDF5 file on disk, Loom objects, and loomR will and be used. Throughout all loomR-related documentation and writing, the following styles for distinguising between loom files, User to focus on their analysis and not worry about the integrity of their data. Unlike other HDF5 pacakges, loomR actively protectes a loom file's structure, enabling the Iterating with chunks through data within the loom file,Īnd provide a platform for other packages to build support for loom files. We provide routines for validating loom files, LoomR provides an interface for working with loom files in a loom-specific way. In mojaveazure/loomR: An R interface for loom filesĭescription Semantics Loom Files Chunk-based iteration Extending loomR ![]()
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