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Seurat (version 3.1.1)

ReadH5AD: Read from and write to h5ad files

Description

Utilize the Anndata h5ad file format for storing and sharing single-cell expression data. Provided are tools for writing objects to h5ad files, as well as reading h5ad files into a Seurat object

Usage

ReadH5AD(file, ...)

WriteH5AD(object, ...)

# S3 method for character ReadH5AD(file, assay = "RNA", layers = "data", verbose = TRUE, ...)

# S3 method for H5File ReadH5AD(file, assay = "RNA", layers = "data", verbose = TRUE, ...)

# S3 method for Seurat WriteH5AD(object, file, assay = NULL, graph = NULL, verbose = TRUE, overwrite = FALSE, ...)

Arguments

file

Name of h5ad file, or an H5File object for reading in

...

arguments passed to other methods

object

An object

assay

Name of assay to store

layers

Slot to store layers as; choose from 'counts' or 'data'; pass FALSE to not pull layers; may pass one value of 'counts' or 'data' for each layer in the H5AD file, must be in order

verbose

Show progress updates

graph

Name of graph to write out, defaults to paste0(assay, '_snn')

overwrite

Overwrite existing file

Value

ReadH5AD: A Seurat object with data from the h5ad file

WriteH5AD: None, writes to disk

Details

ReadH5AD and WriteH5AD will try to automatically fill slots based on data type and presence. For example, objects will be filled with scaled and normalized data if adata.X is a dense matrix and raw is present (when reading), or if the scale.data slot is filled (when writing). The following is a list of how objects will be filled

adata.X is dense and adata.raw is filled; ScaleData is filled

Objects will be filled with scaled and normalized data

adata.X is sparse and adata.raw is filled; NormalizeData has been run, ScaleData has not been run

Objects will be filled with normalized and raw data

adata.X is sparse and adata.raw is not filled; NormalizeData has not been run

Objects will be filled with raw data only

In addition, dimensional reduction information and nearest-neighbor graphs will be searched for and added if and only if scaled data is being added.

When reading: project name is basename(file); identity classes will be set as the project name; all cell-level metadata from adata.obs will be taken; feature level metadata from data.var and adata.raw.var (if present) will be merged and stored in assay meta.features; highly variable features will be set if highly_variable is present in feature-level metadata; dimensional reduction objects will be given the assay name provided to the function call; graphs will be named assay_method if method is present, otherwise assay_adata

When writing: only one assay will be written; all dimensional reductions and graphs associated with that assay will be stored, no other reductions or graphs will be written; active identity classes will be stored in adata.obs as active_ident

See Also

as.loom