specht2019v3 | R Documentation |
Single cell proteomics data acquired by the Slavov Lab. This is the version 3 of the data released in October 2020. It contains quantitative information of macrophages and monocytes at PSM, peptide and protein level.
specht2019v3
A QFeatures object with 179 assays, each assay being a SingleCellExperiment object:
Assay 1-63: PSM data for SCoPE2 sets acquired with a TMT-11plex protocol, hence those assays contain 11 columns. Columns hold quantitative information from single-cell channels, carrier channels, reference channels, empty (blank) channels and unused channels.
Assay 64-177: PSM data for SCoPE2 sets acquired with a TMT-16plex protocol, hence those assays contain 16 columns. Columns hold quantitative information from single-cell channels, carrier channels, reference channels, empty (blank) channels and unused channels.
peptides
: peptide data containing quantitative data for 9208
peptides and 1018 single-cells.
proteins
: protein data containing quantitative data for 2772
proteins and 1018 single-cells.
The colData(specht2019v2())
contains cell type annotation and
batch annotation that are common to all assays. The description of
the rowData
fields for the PSM data can be found in the
MaxQuant
documentation.
The data were acquired using the following setup. More information
can be found in the source article (see References
).
Cell isolation: flow cytometry (BD FACSAria I).
Sample preparation performed using the SCoPE2 protocol. mPOP cell lysis + trypsin digestion + TMT-11plex or 16plex labeling and pooling.
Separation: online nLC (DionexUltiMate 3000 UHPLC with a 25cm x 75um IonOpticksAurora Series UHPLC column; 200nL/min).
Ionization: ESI (2,200V).
Mass spectrometry: Thermo Scientific Q-Exactive (MS1 resolution = 70,000; MS2 accumulation time = 300ms; MS2 resolution = 70,000).
Data analysis: DART-ID + MaxQuant (1.6.2.3).
The PSM data were collected from a shared Google Drive folder that
is accessible from the SlavovLab website (see Source
section).
The folder contains the following files of interest:
ev_updated_v2.txt
: the MaxQuant/DART-ID output file
annotation_fp60-97.csv
: sample annotation
batch_fp60-97.csv
: batch annotation
We combined the sample annotation and the batch annotation in
a single table. We also formatted the quantification table so that
columns match with those of the annotation and filter only for
single-cell runs. Both table are then combined in a single
QFeatures object using the scp::readSCP()
function.
The peptide data were taken from the Slavov lab directly
(Peptides-raw.csv
). It is provided as a spreadsheet. The data
were formatted to a SingleCellExperiment object and the sample
metadata were matched to the column names (mapping is retrieved
after running the SCoPE2 R script) and stored in the colData
.
The object is then added to the QFeatures object (containing the
PSM assays) and the rows of the peptide data are linked to the
rows of the PSM data based on the peptide sequence information
through an AssayLink
object.
The protein data (Proteins-processed.csv
) is formatted similarly
to the peptide data, and the rows of the proteins were mapped onto
the rows of the peptide data based on the protein sequence
information.
Since version 2, a serious bug in the data were corrected
for TMT channels 12 to 16. Many more cells are therefore contained
in the data. Version 2 is maintained for backward compatibility.
Although the final version of the article was published in 2021,
we have kept specht2019v3
as the data set name for consistency
with the previous data version specht2019v2
.
The data were downloaded from the
Slavov Lab website via a
shared Google Drive
folder.
The raw data and the quantification data can also be found in the
massIVE repository MSV000083945
:
ftp://massive.ucsd.edu/MSV000083945.
Specht, Harrison, Edward Emmott, Aleksandra A. Petelski, R. Gray Huffman, David H. Perlman, Marco Serra, Peter Kharchenko, Antonius Koller, and Nikolai Slavov. 2021. "Single-Cell Proteomic and Transcriptomic Analysis of Macrophage Heterogeneity Using SCoPE2." Genome Biology 22 (1): 50. (link to article).
specht2019v3()
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