Convert a SingleCellExperiment object to an AnnData object — from

from_SingleCellExperiment() converts a SingleCellExperiment to an AnnData object.

Usage

from_SingleCellExperiment(
  sce,
  output_class = c("InMemory", "HDF5AnnData"),
  ...
)

Arguments

sce: An object inheriting from SingleCellExperiment.
output_class: Name of the AnnData class. Must be one of "HDF5AnnData" or "InMemoryAnnData".
...: Additional arguments passed to the generator function. See the "Details" section for more information on which parameters

Value

from_SingleCellExperiment() returns an AnnData object (e.g., InMemoryAnnData) representing the content of sce.

Examples

## construct an AnnData object from a SingleCellExperiment
library(SingleCellExperiment)
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#> 
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> 
#> Attaching package: ‘BiocGenerics’
#> The following object is masked from ‘package:SeuratObject’:
#> 
#>     intersect
#> The following objects are masked from ‘package:stats’:
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
#> 
#>     Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
#>     as.data.frame, basename, cbind, colnames, dirname, do.call,
#>     duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
#>     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
#>     pmin.int, rank, rbind, rownames, sapply, saveRDS, setdiff, table,
#>     tapply, union, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#> 
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:utils’:
#> 
#>     findMatches
#> The following objects are masked from ‘package:base’:
#> 
#>     I, expand.grid, unname
#> Loading required package: IRanges
#> 
#> Attaching package: ‘IRanges’
#> The following object is masked from ‘package:sp’:
#> 
#>     %over%
#> Loading required package: GenomeInfoDb
#> Loading required package: Biobase
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 
#> Attaching package: ‘Biobase’
#> The following object is masked from ‘package:MatrixGenerics’:
#> 
#>     rowMedians
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     anyMissing, rowMedians
#> Warning: replacing previous import ‘S4Arrays::makeNindexFromArrayViewport’ by ‘DelayedArray::makeNindexFromArrayViewport’ when loading ‘SummarizedExperiment’
#> 
#> Attaching package: ‘SummarizedExperiment’
#> The following object is masked from ‘package:Seurat’:
#> 
#>     Assays
#> The following object is masked from ‘package:SeuratObject’:
#> 
#>     Assays
sce <- SingleCellExperiment(
  assays = list(counts = matrix(1:5, 5L, 3L)),
  colData = DataFrame(cell = 1:3),
  rowData = DataFrame(gene = 1:5)
)
from_SingleCellExperiment(sce, "InMemory")
#> AnnData object with n_obs × n_vars = 3 × 5
#>     obs: 'cell'
#>     var: 'gene'