Convert single-cell datasets between formats

Rapidly convert between single-cell file formats (h5Seurat, h5ad, Loom) using minimal memory. This function enables seamless interoperability between R/Seurat and Python/scanpy/squidpy ecosystems. Accepts Seurat objects directly, filename paths, or H5File connections.

Usage

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

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

# 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,
  standardize = FALSE,
  ...
)

# S3 method for class 'Seurat'
Convert(
  source,
  dest,
  assay = DefaultAssay(object = source),
  overwrite = FALSE,
  verbose = TRUE,
  standardize = FALSE,
  ...
)

Arguments

source: Source dataset: a Seurat object, filename path, or H5File connection
dest: Name/path of destination file. If only a file type is provided (e.g., "h5seurat", "h5ad", "loom"), the extension is appended to the source filename (for file sources) or the Seurat project name (for Seurat objects). Supported formats: h5seurat, h5ad, loom
assay: For h5Seurat -> other formats: name of assay to convert. For other formats -> h5Seurat: name to assign to the assay. Default is "RNA".
overwrite: Logical; if TRUE, overwrite an existing destination file. Default is FALSE.
verbose: Logical; if TRUE (default), show progress updates
standardize: Logical; if TRUE, convert Seurat-style metadata column names to scanpy/AnnData conventions when converting to h5ad format. For example, nCount_RNA becomes n_counts, nFeature_RNA becomes n_genes. Only applicable for conversions to h5ad format. Default is FALSE.
...: Arguments passed to specific conversion methods

Value

If source is a character path or Seurat object, invisibly returns the destination filename. If source is an H5File connection, returns an H5File object connection to the destination file (e.g., h5Seurat for h5Seurat format).

Details

Supported Conversion Pathways:

Seurat -> h5ad: Direct conversion via temporary h5Seurat intermediate
Seurat -> h5Seurat: Save Seurat object to h5Seurat file
Seurat -> Loom: Save Seurat object to Loom file
h5ad <-> h5Seurat: Convert between Python AnnData and R Seurat formats
Loom <-> h5Seurat: Convert Loom files (limited support)

Key Features:

Memory-efficient on-disk conversion (no full dataset loading)
Preserves expression matrices, metadata, and dimensional reductions
For Visium/spatial data: reconstructs images with scale factors
Handles multiple data layers (V5 compatibility)
Rapid conversion of large datasets (>100K cells)

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 the SeuratDisk.dtypes.dataframe_as_group option 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”).

Examples

if (FALSE) { # \dontrun{
library(srtdisk)
library(Seurat)

# --- Convert from Seurat objects directly ---
# Seurat to h5ad (for Python/scanpy)
Convert(seurat_obj, dest = "output.h5ad")

# Seurat to Loom
Convert(seurat_obj, dest = "output.loom")

# Seurat to h5Seurat
Convert(seurat_obj, dest = "output.h5Seurat")

# Quick format inference - uses Project name
Convert(seurat_obj, dest = "h5ad")  # Creates <project>.h5ad

# --- Convert between file formats ---
# h5ad (Python/scanpy) to h5Seurat (R/Seurat)
Convert("python_data.h5ad", dest = "seurat_data.h5seurat")

# h5Seurat to h5ad
Convert("seurat_data.h5seurat", dest = "python_data.h5ad")

# Visium spatial data
Convert("visium_scanpy.h5ad", dest = "visium_seurat.h5seurat")
visium <- LoadH5Seurat("visium_seurat.h5seurat")
} # }

Convert single-cell datasets between formats

Usage

Arguments

Value

Details

AnnData/H5AD to h5Seurat

Expression data

Feature-level metadata

Cell-level metadata

Dimensional reduction information:

Nearest-neighbor graph

Layers

Miscellaneous information

h5Seurat to AnnData/H5AD

Assay data

Cell-level metadata

Dimensional reduction information

Nearest-neighbor graphs

Layers

See also

Examples