anndatar

name	anndatar
domain	compbio
description	Convert between .h5ad (AnnData) files and R single-cell data formats (Seurat, SingleCellExperiment) using anndataR. Use this skill whenever the user mentions: h5ad files, AnnData objects, converting between Python scanpy and R (Seurat/SingleCellExperiment), moving single-cell data between Python and R ecosystems, or needs to read/write .h5ad files from R. This skill provides pure R H5AD interface without Python dependencies.

anndataR: H5AD to R Data Conversion

This skill enables seamless conversion between .h5ad files (AnnData format from Python/scanpy) and R single-cell objects (Seurat, SingleCellExperiment) using the anndataR package.

Installation

Install anndataR using BiocManager:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
  install.packages("BiocManager")
}
BiocManager::install("anndataR")

Core Conversion Functions

Reading .h5ad Files

Read as AnnData object (default - in-memory):

library(anndataR)
h5ad_path <- "path/to/your/file.h5ad"
adata <- read_h5ad(h5ad_path)

Read as SingleCellExperiment:

sce <- read_h5ad(h5ad_path, as = "SingleCellExperiment")

Read as Seurat:

obj <- read_h5ad(h5ad_path, as = "Seurat")

Read as HDF5-backed AnnData (memory-efficient for large files):

adata <- read_h5ad(h5ad_path, as = "HDF5AnnData")

Converting Between Formats

AnnData → SingleCellExperiment:

sce <- adata$as_SingleCellExperiment()

AnnData → Seurat:

obj <- adata$as_Seurat()

SingleCellExperiment → AnnData:

adata <- as_AnnData(sce)

Seurat → AnnData:

adata <- as_AnnData(obj)

Writing .h5ad Files

Write AnnData to disk:

tmpfile <- tempfile(fileext = ".h5ad")
adata$write_h5ad(tmpfile)
# Or: write_h5ad(adata, tmpfile)

Write SingleCellExperiment to disk:

write_h5ad(sce, tmpfile)

Write Seurat to disk:

write_h5ad(obj, tmpfile)

Working with AnnData Objects

Accessing Slots

# Dimensions
dim(adata)
nrow(adata)
ncol(adata)

# Observation metadata (cells)
adata$obs        # Returns data.frame
adata$obs[1:5, ]  # First 5 cells

# Variable metadata (genes)
adata$var         # Returns data.frame
adata$var[1:5, ]  # First 5 genes

# Main expression matrix
adata$X

# Additional matrices (layers)
adata$layers$counts
adata$layers$dense_X

# Embeddings (obsm)
adata$obsm$X_pca
adata$obsm$X_umap

# Gene loadings (varm)
adata$varm$PCsstructured metadata

# Un (uns)
adata$uns$leiden
adata$uns$pca

Subsetting AnnData Objects

anndataR supports standard R subsetting with lazy evaluation (returns views):

# Subset by condition - cells
view1 <- adata[adata$obs$cell_type == "A", ]

# Subset by condition - genes
view2 <- adata[, adata$var$highly_variable]

# Combined subsetting
view3 <- adata[adata$obs$cell_type == "A", adata$var$highly_variable]

# Numeric indices
view4 <- adata[1:5, 1:3]

# Character names
rownames(adata) <- paste0("cell_", 1:nrow(adata))
colnames(adata) <- paste0("gene_", 1:ncol(adata))
view5 <- adata[c("cell_1", "cell_2"), c("gene_1", "gene_3")]

# Convert view to concrete object
concrete <- view3$as_InMemoryAnnData()

Manually Creating AnnData Objects

adata <- AnnData(
  X = matrix(rnorm(100), nrow = 10, ncol = 10),
  obs = data.frame(
    cell_type = factor(rep(c("A", "B"), each = 5)),
    score = runif(10)
  ),
  var = data.frame(
    gene_name = paste0("gene_", 1:10),
    highly_variable = rep(c(TRUE, FALSE), 5)
  )
)

Common Workflows

Python → R Pipeline

1. Process data in Python with scanpy
2. Save as .h5ad: adata.write('data.h5ad')
3. In R:

library(anndataR)
library(Seurat)
adata <- read_h5ad("data.h5ad")
seurat_obj <- adata$as_Seurat()
# Continue with Seurat analysis

R → Python Pipeline

1. Process data in R with Seurat
2. Convert and save:

library(anndataR)
adata <- as_AnnData(seurat_obj)
adata$write_h5ad("data.h5ad")

3. In Python:

import scanpy as sc
adata = sc.read_h5ad("data.h5ad")
# Continue with scanpy analysis

Comparison with Other Packages

Feature	anndataR	zellkonverter	anndata (CRAN)
Python dependency	No	Yes (reticulate)	Yes
Native H5AD reading	Yes	Via basilisk	No
Seurat conversion	Yes	Yes	Yes
SCE conversion	Yes	Yes	Yes

anndataR is recommended when:

• You want to avoid Python dependencies
• You need a pure R solution
• You want simpler installation and setup

Use zellkonverter if you need bidirectional Python-R integration with more features.

Troubleshooting

Matrix column names warning: When writing Seurat objects, matrix column names in obsm slots cannot be preserved. Store embeddings as data.frames instead of matrices if column names are important.

Memory issues: For large .h5ad files, use read_h5ad(path, as = "HDF5AnnData") for memory-efficient backed access instead of loading entirely into memory.

Unsupported dtypes: anndataR handles most common data types. If you encounter unsupported types, you may need to preprocess in Python first.