Convert: Convert an on-disk single-cell dataset to another format

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

Convert an on-disk single-cell dataset to another format

Description

HDF5-based single-cell datasets can be converted from one format to another using minimal memory. Details about conversion formats implemented are provided below

Usage

Convert(source, dest, assay, overwrite = FALSE, verbose = TRUE, ...)

## S3 method for class 'character'
Convert(source, dest, assay, overwrite = FALSE, verbose = TRUE, ...)

## S3 method for class 'H5File'
Convert(
  source,
  dest = "h5seurat",
  assay = "RNA",
  overwrite = FALSE,
  verbose = TRUE,
  ...
)

## S3 method for class 'h5Seurat'
Convert(
  source,
  dest = "h5ad",
  assay = DefaultAssay(object = source),
  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
...	Arguments passed to other methods

Value

If source is a character, invisibly returns dest; otherwise, returns an H5File, or filetype-specific subclass of H5File (eg. h5Seurat), connection to dest

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

h5Seurat to AnnData/H5AD

The h5Seurat to AnnData/H5AD conversion will try to automatically fill in datasets based on data presence. Data presense is determined by the h5Seurat index (source$index()). It works in the following manner:

Assay data

• X will be filled with scale.data if scale.data is present; otherwise, it will be filled with data

• var will be filled with meta.features only for the features present in X; for example, if X is filled with scale.data, then var will contain only features that have been scaled

• raw.X will be filled with data if X is filled with scale.data; otherwise, it will be filled with counts. If counts is not present, then raw will not be filled

• raw.var will be filled with meta.features with the features present in raw.X; if raw.X is not filled, then raw.var will not be filled

Cell-level metadata

Cell-level metadata is added to obs

Dimensional reduction information

Only dimensional reductions associated with assay or marked as global will be transfered to the H5AD file. For every reduction reduc:

• cell embeddings are placed in obsm and renamed to X_reduc

• feature loadings, if present, are placed in varm and renamed to either “PCs” if reduc is “pca” otherwise reduc in all caps

For example, if reduc is “ica”, then cell embeddings will be “X_ica” in obsm and feature loaodings, if present, will be “ICA” in varm

Nearest-neighbor graphs

If a nearest-neighbor graph is associated with assay, it will be added to uns/neighbors/distances; if more than one graph is present, then only the last graph according to the index will be added.

Layers

Data from other assays can be added to layers if they have the same shape as X (same number of cells and features). To determine this, the shape of each alternate assays's scale.data and data slots are determined. If they are the same shape as X, then that slot (scale.data is given priority over data) will be added as a layer named the name of the assay (eg. “SCT”). In addition, the features names will be added to var as assay_features (eg. “SCT_features”).

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