H5ADToH5Seurat: Convert AnnData/H5AD files to h5Seurat files

In mojaveazure/seurat-disk: Interfaces for HDF5-Based Single Cell File Formats

Convert AnnData/H5AD files to h5Seurat files

Description

Convert AnnData/H5AD files to h5Seurat files

Usage

H5ADToH5Seurat(source, dest, assay = "RNA", overwrite = FALSE, verbose = TRUE)

Arguments

source	Source dataset
dest	Name of destination dataset
assay	Converting from h5Seurat: name of assay to write out; converting to h5Seurat: name to store assay data as
overwrite	Overwrite existing dest
verbose	Show progress updates

Value

Returns a handle to dest as an h5Seurat object

AnnData/H5AD to h5Seurat

The AnnData/H5AD to h5Seurat conversion will try to automatically fill in datasets based on data presence. It works in the following manner:

Expression data

The expression matrices counts, data, and scale.data are filled by /X and /raw/X in the following manner:

• counts will be filled with /raw/X if present; otherwise, it will be filled with /X

• data will be filled with /raw/X if /raw/X is present and /X is dense; otherwise, it will be filled with /X

• scale.data will be filled with /X if it dense; otherwise, it will be empty

Feature names are taken from the feature-level metadata

Feature-level metadata

Feature-level metadata is added to the meta.features datasets in each assay. Feature names are taken from the dataset specified by the “_index” attribute, the “_index” dataset, or the “index” dataset, in that order. Metadata is populated with /raw/var if present, otherwise with /var; if both /raw/var and /var are present, then meta.features will be populated with /raw/var first, then /var will be added to it. For columns present in both /raw/var and /var, the values in /var will be used instead. Note: it is possible for /var to have fewer features than /raw/var; if this is the case, then only the features present in /var will be overwritten, with the metadata for features not present in /var remaining as they were in /raw/var or empty

Cell-level metadata

Cell-level metadata is added to meta.data; the row names of the metadata (as determined by the value of the “_index” attribute, the “_index” dataset, or the “index” dataset, in that order) are added to the “cell.names” dataset instead. If the “__categories” dataset is present, each dataset within “__categories” will be stored as a factor group. Cell-level metadata will be added as an HDF5 group unless factors are not present and SeuratDisk.dtype.dataframe_as_group is FALSE

Dimensional reduction information:

Cell embeddings are taken from /obsm; dimensional reductions are named based on their names from obsm by removing the preceding “X_”.For example, if a dimensional reduction is named “X_pca” in /obsm, the resulting dimensional reduction information will be named “pca”. The key will be set to one of the following:

• “PC_” if “pca” is present in the dimensional reduction name (grepl("pca", reduction.name, ignore.case = TRUE))

• “tSNE_” if “tsne” is present in the dimensional reduction name (grepl("tsne", reduction.name, ignore.case = TRUE))

• reduction.name_ for all other reductions

Remember that the preceding “X_” will be removed from the reduction name before converting to a key. Feature loadings are taken from /varm and placed in the associated dimensional reduction. The dimensional reduction is determine from the loadings name in /varm:

• “PCs” will be added to a dimensional reduction named “pca”

• All other loadings in /varm will be added to a dimensional reduction named tolower(loading) (eg. a loading named “ICA” will be added to a dimensional reduction named “ica”)

If a dimensional reduction cannot be found according to the rules above, the loading will not be taken from the AnnData/H5AD file. Miscellaneous information will be taken from /uns/reduction where reduction is the name of the reduction in /obsm without the preceding “X_”; if no dimensional reduction information present, then miscellaneous information will not be taken from the AnnData/H5AD file. Standard deviations are taken from a dataset /uns/reduction/variance; the variances will be converted to standard deviations and added to the stdev dataset of a dimensional reduction

Nearest-neighbor graph

If a nearest neighbor graph is present in /uns/neighbors/distances, it will be added as a graph dataset in the h5Seurat file and associated with assay; if a value is present in /uns/neighbors/params/method, the name of the graph will be assay_method, otherwise, it will be assay_anndata

Layers

TODO: add this

Miscellaneous information

All groups and datasets from /uns will be copied to misc in the h5Seurat file except for the following:

• Any group or dataset named the same as a dimensional reduction (eg. /uns/pca)

• /uns/neighbors

mojaveazure/seurat-disk documentation built on Nov. 5, 2023, 9:40 a.m.