Cell Ranger RNA-seq data set

Contains UMI droplet-based single-cell RNA-seq data.

Usage

CellRanger(
  dir,
  filtered = TRUE,
  organism = NULL,
  ensemblRelease = NULL,
  genomeBuild = NULL,
  gffFile = NULL,
  refdataDir = NULL,
  samples = NULL,
  censorSamples = NULL,
  sampleMetadataFile = NULL,
  transgeneNames = NULL,
  interestingGroups = "sampleName"
)

Arguments

dir: character(1). Directory path to Cell Ranger output.
filtered: logical(1). Use filtered (recommended) or raw counts. Note that raw counts still contain only whitelisted cellular barcodes.
organism: character(1). Full Latin organism name (e.g. "Homo sapiens").
ensemblRelease: integer(1). Ensembl release version (e.g. 100). We recommend setting this value if possible, for improved reproducibility. When left unset, the latest release available via AnnotationHub/ensembldb is used. Note that the latest version available can vary, depending on the versions of AnnotationHub and ensembldb in use.
genomeBuild: character(1). Ensembl genome build assembly name (e.g. "GRCh38"). If set NULL, defaults to the most recent build available. Note: don't pass in UCSC build IDs (e.g. "hg38").
gffFile: character(1). GFF/GTF (General Feature Format) file. Generally, we recommend using GTF (GFFv2) instead of GFFv3.
refdataDir: character(1) or NULL. Directory path to Cell Ranger reference annotation data.
samples: character. Sample identifiers.
censorSamples: character. Specify a subset of samples to censor.
sampleMetadataFile: character(1). Sample metadata file path. CSV or TSV is preferred, but Excel worksheets are also supported. Check the documentation for conventions and required columns.
transgeneNames: character. Vector indicating which assay rows denote transgenes (e.g. EGFP, TDTOMATO).
interestingGroups: character. Groups of interest to use for visualization. Corresponds to factors describing the columns of the object.

Value

CellRanger.

Details

Read 10x Genomics Cell Ranger output for a Chromium data set into a SingleCellExperiment object.

Currently supports loading of a single genome.

Note

Updated 2022-06-07.

Directory structure for multiple samples

Cell Ranger can vary in its output directory structure, but we're requiring a single, consistent directory structure for datasets containing multiple samples that have not been aggregated into a single matrix with aggr.

Cell Ranger v3 output:

| <dir>/
|-- <sampleName>/
|---- SC_RNA_COUNTER_CS/
|---- outs/
|------ filtered_feature_bc_matrix/
|-------- barcodes.tsv.gz
|-------- features.tsv.gz
|-------- matrix.mtx.gz
|------ filtered_feature_bc_matrix.h5
|------ metrics_summary.csv
|------ molecule_info.h5
|------ possorted_genome_bam.bam
|------ possorted_genome_bam.bam.bai
|------ raw_feature_bc_matrix/
|-------- barcodes.tsv.gz
|-------- features.tsv.gz
|-------- matrix.mtx.gz
|------ raw_feature_bc_matrix.h5
|------ web_summary.html

Cell Ranger v2 output:

| <dir>/
|-- <sampleName>/
|---- SC_RNA_COUNTER_CS/
|---- outs/
|------ filtered_gene_bc_matrices/
|-------- <genomeBuild>/
|---------- barcodes.tsv
|---------- genes.tsv
|---------- matrix.mtx
|------ filtered_gene_bc_matrices_h5.h5
|------ metrics_summary.csv
|------ molecule_info.h5
|------ possorted_genome_bam.bam
|------ possorted_genome_bam.bam.bai
|------ raw_gene_bc_matrices/
|-------- <genomeBuild>/
|---------- barcodes.tsv
|---------- genes.tsv
|---------- matrix.mtx
|------ raw_gene_bc_matrices_h5.h5

Sample metadata

A user-supplied sample metadata file defined by sampleMetadataFile is required for multiplexed datasets. Otherwise this can be left NULL, and minimal sample data will be used, based on the directory names.

Reference data

We strongly recommend supplying the corresponding reference data required for Cell Ranger with the refdataDir argument. It will convert the gene annotations defined in the GTF file into a GRanges object, which get slotted in rowRanges(). Otherwise, the function will attempt to use the most current annotations available from Ensembl, and some gene IDs may not match, due to deprecation in the current Ensembl release.

Examples

dir <- system.file("extdata", "cellranger_v3", package = "Chromium")
x <- CellRanger(dir)
#> → Importing Chromium single-cell RNA-seq run.
#> ℹ 1 sample detected:
#> • pbmc
#> → Importing counts from matrix.mtx.gz file.
#> → Importing /private/var/folders/l1/8y8sjzmn15v49jgrqglghcfr0000gn/T/RtmpCg1NZ7/temp_libpath15e75769d13a0/Chromium/extdata/cellranger_v3/pbmc/outs/filtered_feature_bc_matrix/matrix.mtx.gz using Matrix::`readMM()`.
#> → Importing /private/var/folders/l1/8y8sjzmn15v49jgrqglghcfr0000gn/T/RtmpCg1NZ7/temp_libpath15e75769d13a0/Chromium/extdata/cellranger_v3/pbmc/outs/filtered_feature_bc_matrix/features.tsv.gz using base::`read.table()`.
#> → Importing /private/var/folders/l1/8y8sjzmn15v49jgrqglghcfr0000gn/T/RtmpCg1NZ7/temp_libpath15e75769d13a0/Chromium/extdata/cellranger_v3/pbmc/outs/filtered_feature_bc_matrix/barcodes.tsv.gz using base::`read.table()`.
#> ! Slotting empty ranges into `rowRanges()`.
#> ℹ Filtered zero count rows and columns:
#> - 46 / 100 rows (46%)
#> - 92 / 100 columns (92%)
#> → Calculating 92 sample metrics.
#> ! Calculating metrics without biotype information.
#> `rowData()` required to calculate: "nCoding", "nMito", "mitoRatio".
#> ℹ Prefilter: 55 / 92 samples (60%).
#> ✔ Chromium single-cell RNA-seq run imported successfully.
print(x)
#> class: CellRanger 
#> dim: 46 55 
#> metadata(21): allSamples call ... umiType wd
#> assays(1): counts
#> rownames(46): ENSG00000008128 ENSG00000008130 ... ENSG00000272106
#>   ENSG00000272512
#> rowData names(0):
#> colnames(55): AAACCCAAGGCCTAGA AAACCCATCGTGCATA ... AACCTGAGTCGAGCTC
#>   AACCTGAGTCTGCAAT
#> colData names(8): sampleId sampleName ... log10FeaturesPerCount
#>   mitoRatio
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):