Using anndataR
Robrecht Cannoodt
Luke Zappia
Martin Morgan
Louise Deconinck
Source:vignettes/anndataR.Rmd
anndataR.Rmd{anndataR} allows users to work with
.h5ad files, access various slots in the datasets and
convert these files to SingleCellExperiment objects and
SeuratObjects, and vice versa.
Check out ?anndataR for a full list of the functions
provided by this package.
Installation
Install using:
if (!require("pak", quietly = TRUE)) {
install.packages("pak")
}
pak::pak("scverse/anndataR")Usage
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$var[1:5, 1:6]
#> String n_cells_by_counts mean_counts log1p_mean_counts pct_dropout_by_counts total_counts
#> Gene000 String0 44 1.94 1.078410 12 97
#> Gene001 String1 42 2.04 1.111858 16 102
#> Gene002 String2 43 2.12 1.137833 14 106
#> Gene003 String3 41 1.72 1.000632 18 86
#> Gene004 String4 42 2.06 1.118415 16 103Interoperability
Convert the AnnData object to a SingleCellExperiment object:
sce <- adata$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_countsManually 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'Session info
sessionInfo()
#> R version 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.5 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] SingleCellExperiment_1.28.1 SummarizedExperiment_1.36.0
#> [3] Biobase_2.66.0 GenomicRanges_1.58.0
#> [5] GenomeInfoDb_1.42.0 IRanges_2.40.0
#> [7] S4Vectors_0.44.0 BiocGenerics_0.52.0
#> [9] MatrixGenerics_1.18.0 matrixStats_1.4.1
#> [11] SeuratObject_5.0.2 sp_2.1-4
#> [13] BiocStyle_2.34.0
#>
#> loaded via a namespace (and not attached):
#> [1] sass_0.4.9 future_1.34.0 generics_0.1.3
#> [4] SparseArray_1.6.0 lattice_0.22-6 listenv_0.9.1
#> [7] digest_0.6.37 evaluate_1.0.1 grid_4.4.2
#> [10] bookdown_0.41 fastmap_1.2.0 jsonlite_1.8.9
#> [13] Matrix_1.7-1 BiocManager_1.30.25 httr_1.4.7
#> [16] spam_2.11-0 UCSC.utils_1.2.0 codetools_0.2-20
#> [19] textshaping_0.4.0 jquerylib_0.1.4 abind_1.4-8
#> [22] cli_3.6.3 crayon_1.5.3 rlang_1.1.4
#> [25] XVector_0.46.0 parallelly_1.39.0 future.apply_1.11.3
#> [28] DelayedArray_0.32.0 cachem_1.1.0 yaml_2.3.10
#> [31] S4Arrays_1.6.0 tools_4.4.2 parallel_4.4.2
#> [34] GenomeInfoDbData_1.2.13 globals_0.16.3 R6_2.5.1
#> [37] lifecycle_1.0.4 zlibbioc_1.52.0 fs_1.6.5
#> [40] htmlwidgets_1.6.4 ragg_1.3.3 desc_1.4.3
#> [43] progressr_0.15.0 pkgdown_2.1.1 bslib_0.8.0
#> [46] Rcpp_1.0.13-1 systemfonts_1.1.0 xfun_0.49
#> [49] knitr_1.49 htmltools_0.5.8.1 rmarkdown_2.29
#> [52] dotCall64_1.2 compiler_4.4.2