| Title: | Load and Save Artifacts from File |
|---|---|
| Description: | Save matrices, arrays and similar objects into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. |
| Authors: | Aaron Lun [aut, cre] |
| Maintainer: | Aaron Lun <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.7.8 |
| Built: | 2025-03-06 03:33:06 UTC |
| Source: | https://github.com/bioc/alabaster.matrix |
Implements an amalgamated array, equivalent to a delayed combination of DelayedArray objects.
It allows stageObject to save a combination of multiple matrices without actually aggregating their data into a single file.
AmalgamatedArraySeed(..., along=1) accepts any number of named array-like objects and returns a AmalgamatedArraySeed.
Each object corresponds to a block and should be named accordingly; names should be unique and non-empty.
The along argument specifies the dimension in which matrices should be combined - for matrices, this is 1 for rows, 2 for columns.
AmalgamatedArray(..., along=1) accepts any number of named array-like objects and returns a AmalgamatedArray.
Alternatively, a single AmalgamatedArraySeed may be provided in ....
componentNames(x) will return a character vector of names of component arrays in a AmalgamatedArray(Seed) object x.
extractComponents(x) will return a named list of array-like objects,
corresponding to the component arrays used to construct the AmalgamatedArray(Seed) object x.
stageObject(x, dir, path, child = FALSE) will save the AmalgamatedArray x and its components into the path inside dir.
Each component array is staged into its own subdirectory inside path.
The AmalgamatedArraySeed is closely related to (and in fact, is a subclass of) the DelayedAbind class.
This means that we can leverage many of the DelayedArray methods for handling the delayed bind.
In theory, we could just use a DelayedAbind directly and save it with chihaya in stageObject (via preserveDelayedOperations(TRUE)).
However, this provides fewer opportunities for tracking and manipulating the samples.
It also saves the per-sample matrices into a single file, which eliminates possibilities for per-file deduplication and linking, e.g., with recycleHdf5Files(TRUE).
Aaron Lun
first <- Matrix::rsparsematrix(10, 10, 0.1) second <- Matrix::rsparsematrix(10, 20, 0.1) mat <- AmalgamatedArray(list(foo = first, bar = second), along=2) mat componentNames(mat) out <- extractComponents(mat) lapply(out, dim)first <- Matrix::rsparsematrix(10, 10, 0.1) second <- Matrix::rsparsematrix(10, 20, 0.1) mat <- AmalgamatedArray(list(foo = first, bar = second), along=2) mat componentNames(mat) out <- extractComponents(mat) lapply(out, dim)
Utilities for loading an array saved by stageObject.
.createRawArraySeed(info, project, names = TRUE) .extractArrayDimnames(path, group, ndim).createRawArraySeed(info, project, names = TRUE) .extractArrayDimnames(path, group, ndim)
info |
A named list of metadata for this array. |
project |
Any argument accepted by the acquisition functions, see |
names |
Logical scalar indicating whether the seed should be annotated with dimnames (if available). |
path |
String containing the path to the file containing said array. |
group |
String containing the name of the group with the dimnames. |
ndim |
Integer scalar specifying the number of dimensions. |
For .createArraySeed, the array should be one of:
hdf5_dense_array
hdf5_sparse_matrix
hdf5_delayed_array
amalgamated_array
For delayed arrays, the file may contain a seed array with the "custom alabaster local array" type.
This should have a path dataset containing a relative path to another array in the same project, which is loaded and used as the seed for this delayed array.
Callers can overwrite this behavior by setting "custom alabaster local array" in the knownArrays from chihaya before calling .createRawArraySeed.
For .extractArrayDimnames, path is expected to be a HDF5 file with a group specified by group.
Each child of this group is a string dataset named after a (0-indexed) dimension, containing the names for that dimension.
.createRawArraySeed returns a seed that can be used in the DelayedArray constructor.
.extractArrayDimnames returns a list of character vectors or NULL, containing the dimnames.
Aaron Lun
# Staging an array as an example: dir <- tempfile() dir.create(dir) mat <- array(rpois(10000, 10), c(50, 20, 10)) meta <- stageObject(mat, dir, "whee") # Loading it back as a DelayedArray seed: .createRawArraySeed(meta, project=dir)# Staging an array as an example: dir <- tempfile() dir.create(dir) mat <- array(rpois(10000, 10), c(50, 20, 10)) meta <- stageObject(mat, dir, "whee") # Loading it back as a DelayedArray seed: .createRawArraySeed(meta, project=dir)
Delayed masking of missing values, based on replacement of placeholder values with NA.
This allows missingness to be encoded in frameworks without the same concept of NA as R.
DelayedMask(x, placeholder)DelayedMask(x, placeholder)
x |
An existing DelayedArray seed. |
placeholder |
Placeholder value to replace with |
If is.na(placeholder) is true for double-precision x, masking is performed for all values of x where is.na is true.
This includes both NaNs and NAs; no attempt is made to distinguish between the NaN payloads.
Currently, an error is raised for any integer x that produces non-missing values of -2^31 without a placeholder of NA_integer_.
This is because R cannot distinguish the integer -2^31 from an integer-type NA.
A DelayedMask object, to be wrapped in a DelayedArray.
Aaron Lun
original <- DelayedArray(matrix(rpois(40, lambda=2), ncol=5)) original masked <- DelayedMask(original, 0) DelayedArray(masked)original <- DelayedArray(matrix(rpois(40, lambda=2), ncol=5)) original masked <- DelayedMask(original, 0) DelayedArray(masked)
Preserve delayed operations via chihaya when staging a DelayedArray with stageObject.
preserveDelayedOperations(preserve)preserveDelayedOperations(preserve)
preserve |
Whether to preserve delayed operations using the chihaya specification. |
By default, any DelayedArray in stageObject will be saved as a new dense array or sparse matrix.
However, if this option is enabled, DelayedArrays will instead be saved in the chihaya specification,
where the delayed operations are themselves stored in the HDF5 file (see https://artifactdb.github.io/chihaya/ for details).
The chihaya specification is more complicated to parse but can be helpful in reducing disk usage. One simple example is to avoid sparsity-breaking or integer-to-float operations by storing their delayed representations in the file. If the seed matrix is derived from some immutable reference location, advanced users can even store links to that location instead of duplicating the seed data.
Logical scalar indicating whether delayed operations are to be preserved by the DelayedArray method.
If preserve is supplied, it is used to set this scalar, and the previous value of the scalar is invisibly returned.
Aaron Lun
preserveDelayedOperations() old <- preserveDelayedOperations(TRUE) preserveDelayedOperations() preserveDelayedOperations(old)preserveDelayedOperations() old <- preserveDelayedOperations(TRUE) preserveDelayedOperations() preserveDelayedOperations(old)
Read a dense high-dimensional array from its on-disk representation.
This is usually not directly called by users, but is instead called by dispatch in readObject.
readArray(path, metadata, ...)readArray(path, metadata, ...)
path |
String containing a path to a directory, itself created by the |
metadata |
Named list of metadata for this object, see |
... |
Further arguments, ignored. |
A dense file-backed ReloadedArray.
Aaron Lun
"saveObject,array-method", to create the directory and its contents.
arr <- array(rpois(10000, 10), c(50, 20, 10)) dimnames(arr) <- list( paste0("GENE_", seq_len(nrow(arr))), letters[1:20], NULL ) dir <- tempfile() saveObject(arr, dir) readObject(dir)arr <- array(rpois(10000, 10), c(50, 20, 10)) dimnames(arr) <- list( paste0("GENE_", seq_len(nrow(arr))), letters[1:20], NULL ) dir <- tempfile() saveObject(arr, dir) readObject(dir)
Read a delayed high-dimensional array from its on-disk representation.
This is usually not directly called by users, but is instead called by dispatch in readObject.
readDelayedArray(path, metadata, delayed_array.reload.args = list(), ...)readDelayedArray(path, metadata, delayed_array.reload.args = list(), ...)
path |
String containing a path to a directory, itself created by the |
metadata |
Named list of metadata for this object, see |
delayed_array.reload.args |
Named list of arguments to be passed to |
... |
Further arguments, ignored. |
A multi-dimensional array-like object.
Aaron Lun
"saveObject,DelayedArray-method", to create the directory and its contents.
reloadDelayedObject, for the methods to reload each delayed operation.
arr <- array(rpois(10000, 10), c(50, 20, 10)) dimnames(arr) <- list( paste0("GENE_", seq_len(nrow(arr))), letters[1:20], NULL ) dir <- tempfile() saveObject(arr, dir) readObject(dir)arr <- array(rpois(10000, 10), c(50, 20, 10)) dimnames(arr) <- list( paste0("GENE_", seq_len(nrow(arr))), letters[1:20], NULL ) dir <- tempfile() saveObject(arr, dir) readObject(dir)
Read a sparse matrix from its on-disk representation.
This is usually not directly called by users, but is instead called by dispatch in readObject.
readSparseMatrix(path, metadata, ...)readSparseMatrix(path, metadata, ...)
path |
String containing a path to a directory, itself created by the |
metadata |
Named list of metadata for this object, see |
... |
Further arguments, ignored. |
A sparse ReloadedMatrix object.
Aaron Lun
"saveObject,sparseMatrix-method", to create the directory and its contents.
mat <- Matrix::rsparsematrix(100, 200, density=0.2) rownames(mat) <- paste0("GENE_", seq_len(nrow(mat))) dir <- tempfile() saveObject(mat, dir) readObject(dir)mat <- Matrix::rsparsematrix(100, 200, density=0.2) rownames(mat) <- paste0("GENE_", seq_len(nrow(mat))) dir <- tempfile() saveObject(mat, dir) readObject(dir)
Re-use existing files in HDF5-backed arrays rather than reserializing them in stageObject.
recycleHdf5Files(recycle)recycleHdf5Files(recycle)
recycle |
Whether to recycle existing files for HDF5-backed DelayedArrays. |
If this options is enabled, stageObject will attempt to link/copy existing files for any HDF5-backed DelayedArray instances -
most specifically, HDF5Array objects and H5SparseMatrix objects using the 10X format.
This avoids re-serialization of the data for faster staging.
It also allows advanced users to add their own customizations into the HDF5 file during staging, as long as they do not interfere with loadArray.
By default, this option is disabled as the properties of the existing file are not known in the general case. In particular, the file might contain other groups/datasets that are irrelevant, and use up extra disk space if copied; or confidential, and should not be stored in the staging directory. Users should only enable this option if they have full control over the generation and contents of the backing HDF5 files.
Also note that any dimnames on x will be ignored during recycling.
Logical scalar indicating whether HDF5 files are to be reused.
If recycle is supplied, it is used to set this scalar, and the previous value of the scalar is invisibly returned.
Aaron Lun
recycleHdf5Files() old <- recycleHdf5Files(TRUE) recycleHdf5Files() recycleHdf5Files(old)recycleHdf5Files() old <- recycleHdf5Files(TRUE) recycleHdf5Files() recycleHdf5Files(old)
An array that was reloaded from disk by the readObject function.
This allows methods to refer to the existing on-disk representation by inspecting the path.
For example, saveObject can just copy/link to the files instead of repeating the saving process.
ReloadedArraySeed(path, seed = NULL, ...) ReloadedArray(path, seed = NULL, ...)ReloadedArraySeed(path, seed = NULL, ...) ReloadedArray(path, seed = NULL, ...)
path |
String containing a path to the directory with the on-disk array representation. Alternatively an existing ReloadedArraySeed, which is returned without modification. |
seed |
Contents of the loaded array, e.g., as an ordinary R array, a DelayedArray or a sparse matrix.
If |
... |
Further arguments to pass to |
The ReloadedArraySeed is a DelayedUnaryIsoOp subclass that will just forward all operations to the underlying seed.
Its main purpose is to track the path that was originally used to generate seed, which enables optimizations for methods that need to operate on the files.
One obvious optimization is the specialization of saveObject on ReloadedArray instances.
Instead of loading the array data back into the R session and saving it again, the saveObject method can just link or copy the existing files.
This behavior is controlled by the ReloadedArray.reuse.files= option in the saveObject method,
which can be any of the choices for action= in cloneDirectory.
It may also be "none" to ignore existing files and just save the contents by calling "saveObject,DelayedArray-method".
For the constructors, an instance of the ReloadedArraySeed or ReloadedArray.
arr <- array(rpois(10000, 10), c(50, 20, 10)) dir <- tempfile() saveObject(arr, dir) obj <- readArray(dir) obj DelayedArray::showtree(obj)arr <- array(rpois(10000, 10), c(50, 20, 10)) dir <- tempfile() saveObject(arr, dir) obj <- readArray(dir) obj DelayedArray::showtree(obj)
Save a high-dimensional array to its on-disk representations.
## S4 method for signature 'array' saveObject( x, path, array.dedup.session = NULL, array.dedup.action = "link", array.character.vls = FALSE, ... ) ## S4 method for signature 'denseMatrix' saveObject( x, path, array.dedup.session = NULL, array.dedup.action = "link", ... )## S4 method for signature 'array' saveObject( x, path, array.dedup.session = NULL, array.dedup.action = "link", array.character.vls = FALSE, ... ) ## S4 method for signature 'denseMatrix' saveObject( x, path, array.dedup.session = NULL, array.dedup.action = "link", ... )
x |
An integer, numeric, logical or character array. Alternatively, any of the denseMatrix subclasses from the Matrix package. |
path |
String containing the path to a directory in which to save |
array.dedup.session |
A session object created by |
array.dedup.action |
String specifying how deduplication should occur, see options for the |
array.character.vls |
Logical scalar indicating whether to save character arrays in the custom variable length string (VLS) array format.
If |
... |
Further arguments, currently ignored. |
When saveObject is called multiple times on the same x, this method can avoid re-saving the array if a user supplies a deduplication session in array.dedup.session=.
Instead, the method will link or copy (depending on the choice of array.dedup.action=) the files produced by the first saveObject call to the new path=.
This saves time and reduces disk usage, and is particularly useful when saving complex data structures like a SummarizedExperiment.
For example, if we have a SummarizedExperiment that contains multiple copies of the same array, we can pass a array.dedup.session= to the saveObject call on the SummarizedExperiment.
This will instruct the internal saveObject calls to only write the array to disk once and subsequently make copies or links for all duplicates of that array.
The same approach can be applied to deduplicate seeds in a DelayedArray, see ?"storeDelayedObject" for details.
x is saved to path and NULL is invisibly returned.
Aaron Lun
readArray, to read the directory contents back into the R session.
mat <- array(rpois(10000, 10), c(50, 20, 10)) dimnames(mat) <- list( paste0("GENE_", seq_len(nrow(mat))), letters[1:20], NULL ) dir <- tempfile() saveObject(mat, dir) list.files(dir)mat <- array(rpois(10000, 10), c(50, 20, 10)) dimnames(mat) <- list( paste0("GENE_", seq_len(nrow(mat))), letters[1:20], NULL ) dir <- tempfile() saveObject(mat, dir) list.files(dir)
Save DelayedArray objects to their on-disk representation.
## S4 method for signature 'DelayedArray' saveObject( x, path, DelayedArray.dispatch.pristine = TRUE, DelayedArray.preserve.ops = FALSE, DelayedArray.force.external = FALSE, DelayedArray.store.args = list(), array.dedup.session = NULL, array.dedup.action = NULL, ... )## S4 method for signature 'DelayedArray' saveObject( x, path, DelayedArray.dispatch.pristine = TRUE, DelayedArray.preserve.ops = FALSE, DelayedArray.force.external = FALSE, DelayedArray.store.args = list(), array.dedup.session = NULL, array.dedup.action = NULL, ... )
x |
A DelayedArray object. |
path |
String containing a path to a directory in which to save |
DelayedArray.dispatch.pristine |
Logical scalar indicating whether to call the |
DelayedArray.preserve.ops |
Logical scalar indicating whether delayed operations should be preserved on-disk. |
DelayedArray.force.external |
Logical scalar indicating to save the seeds of |
DelayedArray.store.args |
More named arguments to pass to |
array.dedup.session, array.dedup.action
|
Arguments controlling deduplication of |
... |
Further arguments passed to |
Supplying array.dedup.session= by itself is only guaranteed to deduplicate x itself and may not deduplicate its seeds.
Users should combine this with DelayedArray.force.external=TRUE to force seeds to be saved via saveObject,
which exposes the seeds to the deduplication machinery in their respective saveObject methods.
Check out ?"storeDelayedObject" for more details.
x is saved to path and NULL is invisibly returned.
Aaron Lun
storeDelayedObject, for the methods to save each delayed operation.
mat <- Matrix::rsparsematrix(100, 200, density=0.2) rownames(mat) <- paste0("GENE_", seq_len(nrow(mat))) dmat <- DelayedArray::DelayedArray(mat) * 1 dir <- tempfile() saveObject(dmat, dir, delayed.preserve.ops=TRUE) list.files(dir)mat <- Matrix::rsparsematrix(100, 200, density=0.2) rownames(mat) <- paste0("GENE_", seq_len(nrow(mat))) dmat <- DelayedArray::DelayedArray(mat) * 1 dir <- tempfile() saveObject(dmat, dir, delayed.preserve.ops=TRUE) list.files(dir)
Save a sparse matrix to its on-disk representations.
## S4 method for signature 'sparseMatrix' saveObject( x, path, array.dedup.session = NULL, array.dedup.action = "link", ... ) ## S4 method for signature 'SVT_SparseMatrix' saveObject( x, path, array.dedup.session = NULL, array.dedup.action = "link", ... )## S4 method for signature 'sparseMatrix' saveObject( x, path, array.dedup.session = NULL, array.dedup.action = "link", ... ) ## S4 method for signature 'SVT_SparseMatrix' saveObject( x, path, array.dedup.session = NULL, array.dedup.action = "link", ... )
x |
A sparse matrix of some kind, typically from either the Matrix or SparseArray packages. |
path |
String containing the path to a directory in which to save |
array.dedup.session, array.dedup.action
|
Arguments controlling deduplication of |
... |
Further arguments, currently ignored. |
x is saved to path and NULL is invisibly returned.
Aaron Lun
readSparseMatrix, to read the directory contents back into the R session.
mat <- Matrix::rsparsematrix(100, 200, density=0.2) rownames(mat) <- paste0("GENE_", seq_len(nrow(mat))) dir <- tempfile() saveObject(mat, dir) list.files(dir)mat <- Matrix::rsparsematrix(100, 200, density=0.2) rownames(mat) <- paste0("GENE_", seq_len(nrow(mat))) dir <- tempfile() saveObject(mat, dir) list.files(dir)
Store or reload the delayed operations or array-like seeds of a DelayedArray in an existing HDF5 file.
storeDelayedObject(x, handle, name, ...) reloadDelayedObject(handle, name, version = package_version("1.1"), ...) reloadDelayedObjectFunctionRegistry(type = c("operation", "array")) registerReloadDelayedObjectFunction( type = c("operation", "array"), subtype, fun, existing = c("old", "new", "error") ) ## S4 method for signature 'array' storeDelayedObject( x, handle, name, version = package_version("1.1"), save.external.array = FALSE, ... ) ## S4 method for signature 'ANY' storeDelayedObject( x, handle, name, version = package_version("1.1"), external.save.args = list(), external.dedup.session = NULL, external.dedup.action = NULL, ... ) altStoreDelayedObjectFunction(store) altStoreDelayedObject(...) altReloadDelayedObjectFunction(reload) altReloadDelayedObject(...)storeDelayedObject(x, handle, name, ...) reloadDelayedObject(handle, name, version = package_version("1.1"), ...) reloadDelayedObjectFunctionRegistry(type = c("operation", "array")) registerReloadDelayedObjectFunction( type = c("operation", "array"), subtype, fun, existing = c("old", "new", "error") ) ## S4 method for signature 'array' storeDelayedObject( x, handle, name, version = package_version("1.1"), save.external.array = FALSE, ... ) ## S4 method for signature 'ANY' storeDelayedObject( x, handle, name, version = package_version("1.1"), external.save.args = list(), external.dedup.session = NULL, external.dedup.action = NULL, ... ) altStoreDelayedObjectFunction(store) altStoreDelayedObject(...) altReloadDelayedObjectFunction(reload) altReloadDelayedObject(...)
x |
Any of the delayed operation/array classes from DelayedArray. |
handle |
An rhdf5 handle of a HDF5 file to save into (for |
name |
String containing the name of the group in |
... |
For For |
version |
Package version of the chihaya format to use when storing or reloading delayed objects.
When reloading, the version should be retrieved from the attributes of the outermost group, typically by |
type |
String specifying the type of delayed object, i.e., operation or array.
This corresponds to |
subtype |
String specifying the subtype of the delayed object,
This corresponds to |
fun |
Function to reload a delayed object.
This should accept the same arguments as |
existing |
Logical scalar indicating the action to take if a function has already been registered for |
save.external.array |
Logical scalar indicating whether to save an array-like seed as an external seed,
even if a dedicated |
external.save.args |
Named list of further arguments to pass to |
external.dedup.session |
Deprecated, set |
external.dedup.action |
Deprecated, set |
store |
Function (typically a generic) to store delayed objects to file.
This should accept the same arguments as |
reload |
Function to reload delayed objects from file.
This should accept the same arguments as |
For storeDelayedObject and altStoreDelayedObject, the contents of x are saved to file, and NULL is invisibly returned.
For reloadDelayedObject and altReloadDelayedObject, a delayed operation or DelayedArray is returned.
For altStoreDelayedObjectFunction, the current store function is returned if store is missing.
Otherwise, store is set as the current store function and the previous store function is returned.
For altReloadDelayedObjectFunction, the current reload function is returned if reload is missing.
Otherwise, reload is set as the current reload function and the previous reload function is returned.
Developers can easily extend alabaster.matrix to new delayed objects by writing new methods for storeDelayedObject.
Methods should save the contents of the delayed object to the HDF5 file in the chihaya format.
Each new store method typically requires a corresponding reloading function to be registered via registerReloadDelayedObjectFunction,
so that reloadDelayedObject knows how to reconstitute the object from file.
Application developers can customize the process of storing/reloading delayed objects
by specifying alternative functions in altReloadDelayedObjectFunction and altStoreDelayedObjectFunction.
For example, if we want to preserve all delayed objects except for DelayedSubset,
we could replace storeDelayedObject with an altStoreDelayedObject that realizes any DelayedSubset instance into an ordinary matrix.
This is analogous to the overrides for altReadObject and altSaveObject.
Extension developers (i.e., those who write new methods for storeDelayedObject or new functions for reloadDelayedObject)
should generally use altStoreDelayedObject and altReloadDelayedObject in their method/funcion bodies.
This ensures that any custom overrides specified by application developers are still respected in the extensions to alabaster.matrix.
Whenever storeDelayedObject encounters a delayed operation or array-like seed for which it has no methods,
the ANY method will save the delayed object as an “external seed”.
The array is saved via altSaveObject into a seeds directory next to the file associated with handle.
A reference to this external location is then stored in the name group inside handle.
Users can force this behavior for all array-like seeds by specifying save.external.array=TRUE.
This instructs storeDelayedObject to save everything as external seeds, including those arrays for which it has methods.
Doing so can be beneficial to enable deduplication, e.g., when two delayed arrays perform different operations on the same underlying seed.
By saving the seeds externally, file management systems can identify the redundancy to save storage space.
Advanced users can explicitly deduplicate external seeds by setting both save.external.array=TRUE and supplying array.dedup.session= in external.save.args=.
The array.dedup.session object is filled up with unique seeds as storeDelayedObject is called on various DelayedArrays (see ?"saveObject,array-method" for details).
Whenever a duplicate seed is encountered, it is not saved again, but is instead linked or copied from the file path associated with the identical external seed.
For example, a new session can be created when saving a SummarizedExperiment to deduplicate seeds across its assays.
When external seeds are encountered by reloadDelayedObject, they are loaded as ReloadedArrays (or some variant thereof) by altReadObject.
Users can forcibly realize the reloaded seed into memory by passing custom.takane.reload=TRUE in ... for the reloadDelayedObject call.
This is occasionally helpful for providing a more faithful roundtrip from file back into memory.
Aaron Lun
saveObject,DelayedArray-method and readDelayedArray, where these methods are used.
https://artifactdb.github.io/chihaya/, for the file format specification of delayed objects.
library(DelayedArray) X <- DelayedArray(matrix(runif(100), ncol=20)) Y <- cbind(X, DelayedArray::ConstantArray(value=50, c(5, 10))) library(rhdf5) temp <- tempfile() dir.create(temp) fpath <- file.path(temp, "foo.h5") fhandle <- H5Fcreate(fpath) storeDelayedObject(Y@seed, fhandle, "YAY") rhdf5::h5ls(fhandle) H5Fclose(fhandle) fhandle <- H5Fopen(fpath, "H5F_ACC_RDONLY") reloadDelayedObject(fhandle, "YAY") H5Fclose(fhandle)library(DelayedArray) X <- DelayedArray(matrix(runif(100), ncol=20)) Y <- cbind(X, DelayedArray::ConstantArray(value=50, c(5, 10))) library(rhdf5) temp <- tempfile() dir.create(temp) fpath <- file.path(temp, "foo.h5") fhandle <- H5Fcreate(fpath) storeDelayedObject(Y@seed, fhandle, "YAY") rhdf5::h5ls(fhandle) H5Fclose(fhandle) fhandle <- H5Fopen(fpath, "H5F_ACC_RDONLY") reloadDelayedObject(fhandle, "YAY") H5Fclose(fhandle)
Writes a sparse matrix to file in a compressed sparse format.
writeSparseMatrix( x, file, name, chunk = 10000, column = TRUE, tenx = FALSE, guess.integer = TRUE )writeSparseMatrix( x, file, name, chunk = 10000, column = TRUE, tenx = FALSE, guess.integer = TRUE )
x |
A sparse matrix of some sort. This includes sparse DelayedMatrix objects. |
file |
String containing a path to the HDF5 file. The file is created if it is not already present. |
name |
String containing the name of the group to store |
chunk |
Integer scalar specifying the chunk size for the indices and values. |
column |
Logical scalar indicating whether to store as compressed sparse column format. |
tenx |
Logical scalar indicating whether to use the 10X compressed sparse column format. |
guess.integer |
Logical scalar specifying whether to guess an appropriate integer type from |
This writes a sparse matrix to file in various formats:
column=TRUE and tenx=FALSE uses H5AD's csr_matrix format.
column=FALSE and tenx=FALSE uses H5AD's csc_matrix format.
tenx=TRUE uses 10X Genomics' HDF5 matrix format.
For the first two formats, the apparent transposition is deliberate, because columns in R are interpreted as rows in H5AD. This allows us to retain consistency the interpretation of samples (columns in R, rows in H5AD) and features (vice versa). Constructors for classes like H5SparseMatrix will automatically transpose so no extra work is required.
If guess.integer=TRUE, we attempt to save x's values into the smallest type that will accommodate all of its values.
If x only contains unsigned integers, we will attempt to save either 8-, 16- or 32-bit unsigned integers.
If x contains signed integers, we will fall back to 32-bit signed integers.
For all other values, we will fall back to double-precision floating point values.
We attempt to save x's indices to unsigned 16-bit integers if the relevant dimension of x is small enough.
Otherwise we will save it as an unsigned 32-bit integer.
A NULL invisibly.
The contents of x are written to name in file.
Aaron Lun
library(Matrix) x <- rsparsematrix(100, 20, 0.5) tmp <- tempfile(fileext=".h5") writeSparseMatrix(x, tmp, "csc_matrix") writeSparseMatrix(x, tmp, "csr_matrix", column=FALSE) writeSparseMatrix(x, tmp, "tenx_matrix", tenx = TRUE) rhdf5::h5ls(tmp) library(HDF5Array) H5SparseMatrix(tmp, "csc_matrix") H5SparseMatrix(tmp, "csr_matrix") H5SparseMatrix(tmp, "tenx_matrix")library(Matrix) x <- rsparsematrix(100, 20, 0.5) tmp <- tempfile(fileext=".h5") writeSparseMatrix(x, tmp, "csc_matrix") writeSparseMatrix(x, tmp, "csr_matrix", column=FALSE) writeSparseMatrix(x, tmp, "tenx_matrix", tenx = TRUE) rhdf5::h5ls(tmp) library(HDF5Array) H5SparseMatrix(tmp, "csc_matrix") H5SparseMatrix(tmp, "csr_matrix") H5SparseMatrix(tmp, "tenx_matrix")