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

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 rhdf5:
  BiocManager::install("rhdf5")
• 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")

You can also install all suggested dependencies at once (though note that this might take a while to run):

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:

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

View structure:

adata
#> class: InMemoryAnnData
#> dim: 50 obs x 100 var
#> X: dgRMatrix
#> layers: counts csc_counts dense_X dense_counts
#> 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

Access AnnData slots:

dim(adata$X)
#> [1]  50 100
adata$obs[1:5, 1:6]
#>   Float FloatNA Int IntNA  Bool BoolNA
#> 1 42.42     NaN   0    NA FALSE  FALSE
#> 2 42.42   42.42   1    42  TRUE     NA
#> 3 42.42   42.42   2    42  TRUE   TRUE
#> 4 42.42   42.42   3    42  TRUE   TRUE
#> 5 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
#> 1 String0                44        1.94          1.078410                    12
#> 2 String1                42        2.04          1.111858                    16
#> 3 String2                43        2.12          1.137833                    14
#> 4 String3                41        1.72          1.000632                    18
#> 5 String4                42        2.06          1.118415                    16
#>   total_counts
#> 1           97
#> 2          102
#> 3          106
#> 4           86
#> 5          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()
#> Warning: Keys should be one or more alphanumeric characters followed by an
#> underscore, setting key from rna to rna_
#> Warning: Keys should be one or more alphanumeric characters followed by an
#> underscore, setting key from csc_counts_ to csccounts_
#> Warning: Keys should be one or more alphanumeric characters followed by an
#> underscore, setting key from dense_x_ to densex_
#> Warning: Keys should be one or more alphanumeric characters followed by an
#> underscore, setting key from dense_counts_ to densecounts_
obj
#> An object of class Seurat 
#> 500 features across 50 samples within 5 assays 
#> Active assay: RNA (100 features, 0 variable features)
#>  4 other assays present: counts, csc_counts, dense_X, dense_counts