This vignette will guide you through how accessing and manipulating
the SCoPE2 data sets available from the SingleCellMultimodal
package.
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("SingleCellMultiModal")
library(SingleCellMultiModal) library(MultiAssayExperiment)
SCoPE2 is a mass spectrometry (MS)-based single-cell proteomics protocol to quantify the proteome of single-cells in an untargeted fashion. It was initially developed by @Specht2021-pm.
The user can see the available data set by using the default options.
SCoPE2("macrophage_differentiation", mode = "*", version = "1.0.0", dry.run = TRUE)
Or by simply running:
SCoPE2("macrophage_differentiation")
Currently, only the macrophage_differentiation
is available.
You can retrieve the actual data from ExperimentHub
by setting
dry.run = FALSE
. This example retrieves the complete data set
(transcriptome and proteome) for the macrophage_differentiation
project:
scope2 <- SCoPE2("macrophage_differentiation", modes = "rna|protein", dry.run = FALSE) scope2
This data set has been acquired by the Slavov Lab (@Specht2021-pm). It contains single-cell proteomics and single-cell RNA sequencing data for macrophages and monocytes. The objective of the research that led to generate the data is to understand whether homogeneous monocytes differentiate in the absence of cytokines to macrophages with homogeneous or heterogeneous profiles. The transcriptomic and proteomic acquisitions are conducted on two separate subset of similar cells (same experimental design). The cell type of the samples are known only for the proteomics data. The proteomics data was retrieved from the authors' website and the transcriptomic data was retrieved from the GEO database (accession id: GSE142392).
For more information on the protocol, see @Specht2021-pm.
Only version 1.0.0
is currently available.
The macrophage_differentiation
data set in this package contains two
assays: rna
and protein
.
The single-cell proteomics data contains cell type annotation
(celltype
), sample preparation batch (batch_digest
and
batch_sort
), chromatography batch (batch_chromatography
), and the
MS acquisition run (batch_MS
). The single-cell transcriptomics data
was acquired in two batches (batch_Chromium
). Note that because the
cells that compose the two assays are distinct, there is no common
cell annotation available for both proteomics and transcriptomics. The
annotation were therefore filled with NA
s accordingly.
colData(scope2)
You can extract and check the transcriptomic data through subsetting:
scope2[["rna"]]
The data is rather large and is therefore stored on-disk using the HDF5 backend. You can verify this by looking at the assay data matrix. Note that the counts are UMI counts.
assay(scope2[["rna"]])[1:5, 1:5]
The protein
assay contains MS-based proteomic data.
The data have been passed sample and feature quality control,
normalized, log transformed, imputed and batch corrected. Detailed
information about the data processing is available in
another vignette. You can extract the proteomic data similarly to the
transcriptomic data:
scope2[["protein"]]
In this case, the protein data have reasonable size and are loaded
directly into memory. The data matrix is stored in logexprs
. We
decided to not use the traditional logcounts
because MS proteomics
measures intensities rather than counts as opposed to scRNA-Seq.
assay(scope2[["protein"]])[1:5, 1:5]
sessionInfo()
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