RAMultiomeData: Mouse gastrulation joint ATAC/RNA data

In MarioniLab/MouseGastrulationData: Single-Cell -omics Data across Mouse Gastrulation and Early Organogenesis

Mouse gastrulation joint ATAC/RNA data

Description

Obtain the processed counts for the mouse gastrulation "multi-omics" dataset.

Usage

RAMultiomeData(type = c("all", "rna", "peaks", "tss"), samples = NULL)

Arguments

type	String specifying the type of data to obtain, see Details. Default behaviour is to return all three data types.
samples	Integer or character vector specifying the samples for which data (processed or raw) should be obtained. If NULL (default), data are returned for all (11) samples.

Details

This function downloads the data for the embryo atlas from Argelaguet et al. (2022). The dataset contains 11 10X Genomics multiome samples.

The column metadata contains columns from the following set, depending on modality:

barcode:

Character: cell barcode from the 10X Genomics experiment (with appended "-1" from Cellranger).

sample:

Integer: index of the sample from which the cell was taken.

sample_name:

Character: descriptive name of the sample from which the cell was taken.

stage:

Character: stage of the mouse embryo at which the sample was taken.

genotype:

Character: cell genotype, wild type (WT) or Brachyury KO (T_KO)

celltype:

Character: cell type to which the cell was assigned by mapping to RNA atlas.

nFeature_RNA:

Integer: number of genes detected in RNAseq data for the cell.

nCount_RNA:

Integer: number of RNA molecules detected in RNAseq data for the cell.

mitochondrial_percent_RNA:

Numeric: percent of RNA molecules detected from mitochondrial genome for the cell.

ribosomal_percent_RNA:

Numeric: percent of RNA molecules detected from ribosomal genes for the cell.

nFrags_atac:

Numeric: number of ATAC fragments detected per cell.

TSSEnrichment_atac:

Numeric: Quality control metric that represents the ratio of ATAC peaks near the transcription start site relative to the flanking regions. Derived from the ArchR package.

doublet_score:

Numeric: doublet score for each cell calculated using the cxds_bcds_hybrid function from the scds package.

doublet_call:

Logical: doublet call for each cell calculated from the "doublet_score" column. Cells with a doublet score larger than 1.25 are assumed to be doublets and thus were removed from downstream analysis.

Reduced dimension representations of the data are also available in the reducedDims slot of the SingleCellExperiment object. These are UMAPs calculated either across all the data, or per stage (perstage). Those labelled either rna or atac alone were calculated from the processed count matrices of these modalities; rna_atac-labelled UMAPs were calculated from the MOFA factors calculated cross-modality.

For the RNA and TSS gene score data, the row metadata contains the Ensembl ID and MGI symbol for each gene. The ATAC peak row metadata contains information for each of those peaks Unlike other datasets in MouseGastrulationData, the rownames for these objects are gene symbols.

Value

If type="all", a SingleCellExperiment object is returned containing processed data from selected samples for all data types. RNA-seq data is in the primary assay slot, while the other data types are in the altExp slot. The default counts slot on the first level of the SingleCellExperiment object will be occupied by the RNA data. The other modalities can be accessed using SingleCellExperiment::altExp, where the counts slot will again be occupied by the data for each modality for compatability with many function defaults.

If type="rna", type="peaks", or type="tss", a SingleCellExperiment object is returned containing information for a single data type. Each assay will be in the primary counts slot. RNA data corresponds to RNA-seq read counts. Peak data corresponds to read counts from ATAC-seq quantified over peaks defined using ArchR's peak calling strategy. TSS data corresponds to read counts from ATAC-seq quantified over transcriptions start sites using ArchR's Gene Scores model.

Author(s)

Jonathan Griffiths

References

Argelaguet R et al. (2022). Decoding gene regulation in the mouse embryo using single-cell multi-omics. bioRxiv 2022.06.15.496239

Examples

RA_rna <- RAMultiomeData(samples=1, type = "rna")

MarioniLab/MouseGastrulationData documentation built on Jan. 31, 2024, 11:01 a.m.