This function allows to export a SingleCellExperiment (SCE)
object to visualize in Cerebro.
exportFromSCE(
object,
assay = "logcounts",
file,
experiment_name,
organism,
groups,
cell_cycle = NULL,
nUMI = "nUMI",
nGene = "nGene",
add_all_meta_data = TRUE,
use_delayed_array = FALSE,
verbose = FALSE
)
Arguments
| object |
SingleCellExperiment (SCE) object.
|
| assay |
Assay to pull expression values from; defaults to
logcounts. It is recommended to use sparse data (such as
log-transformed or raw counts) instead of dense data (such as the 'scaled'
slot) to avoid performance bottlenecks in the Cerebro interface. |
| file |
Where to save the output. |
| experiment_name |
Experiment name. |
| organism |
Organism, e.g. hg (human), mm (mouse), etc. |
| groups |
Names of grouping variables in meta data
(colData(object)), e.g. c("sample","cluster"); at least one
must be provided; defaults to NULL. |
| cell_cycle |
Names of columns in meta data (colData(object)) that#
contain cell cycle information, e.g. c("Phase"); defaults to
NULL. |
| nUMI |
Column in colData(object) that contains information about
number of transcripts per cell; defaults to nUMI. |
| nGene |
Column in colData(object) that contains information about
number of expressed genes per cell; defaults to nGene. |
| add_all_meta_data |
If set to TRUE, all further meta data columns
will be extracted as well. |
| use_delayed_array |
When set to TRUE, the expression matrix will
be stored as an RleMatrix (see DelayedArray package). This can
be useful for very large data sets, as the matrix won't be loaded into memory
and instead values will be read from the disk directly, at the cost of
performance. Note that it is necessary to install the DelayedArray
package. If set to FALSE (default), the expression matrix will be
copied from the input object as is. It is recommended to use a sparse format,
such as dgCMatrix from the Matrix package. |
| verbose |
Set this to TRUE if you want additional log messages;
defaults to FALSE. |
Value
No data returned.
Examples
#> Loading required package: 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
#> Loading required package: parallel
#>
#> Attaching package: ‘BiocGenerics’
#> The following objects are masked from ‘package:parallel’:
#>
#> clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
#> clusterExport, clusterMap, parApply, parCapply, parLapply,
#> parLapplyLB, parRapply, parSapply, parSapplyLB
#> The following objects are masked from ‘package:stats’:
#>
#> IQR, mad, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
#>
#> anyDuplicated, append, as.data.frame, basename, cbind, colnames,
#> dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
#> grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
#> order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
#> rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
#> union, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#>
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:base’:
#>
#> expand.grid
#> Loading required package: IRanges
#> 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
#> [17:53:23] Initializing Cerebro object...
#> Warning: the condition has length > 1 and only the first element will be used
#> [17:53:23] Collecting available meta data...
#> [17:53:23] Extracting all meta data columns...
#> [17:53:23] Extracting dimensional reductions...
#> [17:53:23] Will export the following dimensional reductions: UMAP
#> [17:53:23] No trajectories to extract...
#> [17:53:23] Overview of Cerebro object:
#> class: Cerebro_v1.3
#> cerebroApp version: 1.3.1
#> experiment name: PBMC
#> organism: hg
#> date of analysis:
#> date of export: 2021-03-12
#> number of cells: 80
#> number of genes: 230
#> grouping variables (2): sample, cluster
#> cell cycle variables (0):
#> projections (1): UMAP
#> trees (0):
#> most expressed genes:
#> marker genes:
#> enriched pathways:
#> trajectories:
#> extra material:
#> [17:53:23] Saving Cerebro object to: pbmc_SCE.crb
#> [17:53:23] Done!