anndataR

{anndataR} aims to make the AnnData format a first-class citizen in the R ecosystem, and to make it easy to work with AnnData files in R, either directly or by converting it to a SingleCellExperiment or Seurat object.

Feature list:

Provide an R6 class to work with AnnData objects in R (either in-memory or on-disk).
Read/write *.h5ad files natively
Convert to/from SingleCellExperiment objects
Convert to/from Seurat objects

Warning

This package is still in the experimental stage, and may not work as expected. You can find the status of development of anndataR on the feature tracking page of the website.Please report any issues you encounter.

Installation

You can install the development version of {anndataR} like so:

devtools::install_github("scverse/anndataR")

You might need to install suggested dependencies manually, depending on the task you want to perform.

To read/write *.h5ad files, you need to install hdf5r:
BiocManager::install("hdf5r")
To convert to/from SingleCellExperiment objects, you need to install SingleCellExperiment:
BiocManager::install("SingleCellExperiment")
To convert to/from Seurat objects, you need to install SeuratObject:
install.packages("SeuratObject")

If you’re feeling adventurous, you can install all suggested dependencies at once:

devtools::install_github("scverse/anndataR", dependencies = TRUE)

Example

Here’s a quick example of how to use {anndataR}. First, we download an h5ad file.

library(anndataR)

h5ad_path <- system.file("extdata", "example.h5ad", package = "anndataR")

Read an h5ad file in memory:

adata <- read_h5ad(h5ad_path)

Read an h5ad file on disk:

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

View structure:

adata
#> AnnData object with n_obs × n_vars = 50 × 100
#>     obs: 'Float', 'FloatNA', 'Int', 'IntNA', 'Bool', 'BoolNA', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'leiden'
#>     var: 'String', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'
#>     obsm: 'X_pca', 'X_umap'
#>     varm: 'PCs'
#>     layers: 'counts', 'csc_counts', 'dense_X', 'dense_counts'
#>     obsp: 'connectivities', 'distances'

Access AnnData slots:

dim(adata$X)
#> [1]  50 100
adata$obs[1:5, 1:6]
#>         Float FloatNA Int IntNA  Bool BoolNA
#> Cell000 42.42     NaN   0    NA FALSE  FALSE
#> Cell001 42.42   42.42   1    42  TRUE     NA
#> Cell002 42.42   42.42   2    42  TRUE   TRUE
#> Cell003 42.42   42.42   3    42  TRUE   TRUE
#> Cell004 42.42   42.42   4    42  TRUE   TRUE
adata$var[1:5, 1:6]
#>          String n_cells_by_counts mean_counts log1p_mean_counts pct_dropout_by_counts total_counts
#> Gene000 String0                44        1.94          1.078410                    12           97
#> Gene001 String1                42        2.04          1.111858                    16          102
#> Gene002 String2                43        2.12          1.137833                    14          106
#> Gene003 String3                41        1.72          1.000632                    18           86
#> Gene004 String4                42        2.06          1.118415                    16          103

Interoperability

Convert the AnnData object to a SingleCellExperiment object:

sce <- adata$to_SingleCellExperiment()
sce
#> class: SingleCellExperiment 
#> dim: 100 50 
#> metadata(0):
#> assays(5): X counts csc_counts dense_X dense_counts
#> rownames(100): Gene000 Gene001 ... Gene098 Gene099
#> rowData names(11): String n_cells_by_counts ... dispersions dispersions_norm
#> colnames(50): Cell000 Cell001 ... Cell048 Cell049
#> colData names(11): Float FloatNA ... log1p_total_counts leiden
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):

Convert the AnnData object to a Seurat object:

obj <- adata$to_Seurat()
obj
#> An object of class Seurat 
#> 500 features across 50 samples within 5 assays 
#> Active assay: RNA (100 features, 0 variable features)
#>  2 layers present: counts, data
#>  4 other assays present: counts, csc_counts, dense_X, dense_counts

Manually create an object

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

adata
#> AnnData object with n_obs × n_vars = 10 × 10
#>     obs: 'cell_type'
#>     var: 'gene_name'

License

xxywmt/anndataR

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