Package: scpdata 1.19.2

Christophe Vanderaa

scpdata: Single-Cell Proteomics Data Package

The package disseminates mass spectrometry (MS)-based single-cell proteomics (SCP) datasets. The data were collected from published work and formatted using the `scp` data structure. The data sets contain quantitative information at spectrum, peptide and/or protein level for single cells or minute sample amounts.

Authors:Christophe Vanderaa [aut, cre], Laurent Gatto [aut], Enes Ayar [dtc], Samuel Grégoire [dtc]

scpdata_1.19.2.tar.gz
scpdata_1.19.2.zip(r-4.7)scpdata_1.19.2.zip(r-4.6)scpdata_1.19.2.zip(r-4.5)
scpdata_1.19.2.tgz(r-4.6-any)scpdata_1.19.2.tgz(r-4.5-any)
scpdata_1.19.2.tar.gz(r-4.7-any)scpdata_1.19.2.tar.gz(r-4.6-any)
scpdata_1.19.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
scpdata/json (API)

# Install 'scpdata' in R:
install.packages('scpdata', repos = c('https://uclouvain-cbio.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/uclouvain-cbio/scpdata/issues

On CRAN:

Conda:

experimentdataexpressiondataexperimenthubreproducibleresearchmassspectrometrydataproteomesinglecelldatapackagetypedata

5.95 score 9 stars 47 scripts 32 exports 111 dependencies

Last updated from:c80a5e17d0. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK840
source / vignettesOK407
linux-release-x86_64OK1727
macos-release-arm64OK853
macos-oldrel-arm64OK517
windows-develOK884
windows-releaseOK1017
windows-oldrelOK892
wasm-releaseOK174

Exports:ai2025abrunner2022cong2020ACderks2022dou2019_boostingdou2019_lysatesdou2019_mousegregoire2023_mixCTRLguise2024hu2023_K562hu2023_oocytekhan2023krull2024leduc2022leduc2022_plexDIAleduc2022_pSCoPEliang2020_helapetrosius2023_AstralAMLpetrosius2023_mESschoof2021scpdataspecht2019v2specht2019v3williams2020_lfqwilliams2020_tmtwoo2022_lungwoo2022_macrophagezhu2018MCPzhu2018NC_helazhu2018NC_isletszhu2018NC_lysateszhu2019EL

Dependencies:abindAnnotationDbiAnnotationFilterAnnotationHubaskpassbase64encBiobaseBiocBaseUtilsBiocFileCacheBiocGenericsBiocManagerBiocVersionBiostringsbitbit64blobbslibcachemcliclueclustercpp11crayoncrosstalkcurldata.tableDBIdbplyrDelayedArraydigestdplyrevaluateExperimentHubfarverfastmapfilelockfontawesomefsgenericsGenomicRangesggplot2gluegtablehighrhtmltoolshtmlwidgetshttrhttr2igraphIRangesisobandjquerylibjsonliteKEGGRESTknitrlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemimeMsCoreUtilsMultiAssayExperimentopensslotelpillarpkgconfigplotlyplyrpngpromisesProtGenericspurrrQFeaturesR6rappdirsRColorBrewerRcppreshape2rlangrmarkdownRSQLiteS4ArraysS4VectorsS7sassscalesSeqinfoSingleCellExperimentSparseArraystringistringrSummarizedExperimentsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunXVectoryaml

Contribution guidelines
Getting started with GitHub | What do we expect? | QFeatures object | Feature data | Sample annotations | Experiment description | Data source information | Folder structure | inst/scripts/ | R/ | man/ | Workflow | 1. Collect data | 2. Create the QFeatures object | 3. Document the dataset | 4. Update metadata | 5. Create a pull request | 6. Almost done!
Rendered fromcontribution_guidelines.Rmdusingknitr::rmarkdown

Last update: 2024-03-11
Started: 2024-02-15

Single Cell Proteomics data sets
The scpdata package | Load data from ExperimentHub | Data sets information | Data manipulation | Session information | License
Rendered fromscpdata.Rmdusingknitr::rmarkdown

Last update: 2024-02-15
Started: 2019-10-15

Readme and manuals

Help Manual

Help pageTopics
Ai et al., Cardiomyocytes Single Cell Proteomics, MCP (2025)ai2025a
Brunner et al. 2022 (Mol. Syst. Biol.): cell cycle state studybrunner2022
Cong et al. 2020 (Ana. Chem.): HeLa single cellscong2020AC
Derks et al. 2022 - plexDIA (Nat. Biotechnol.): PDAC vs melanoma cells vs monocytesderks2022
Dou et al. 2019 (Anal. Chem.): testing boosting ratiosdou2019_boosting
Dou et al. 2019 (Anal. Chem.): HeLa lysatesdou2019_lysates
Dou et al. 2019 (Anal. Chem.): murine cell linesdou2019_mouse
Grégoire et al. 2023 - mixCTRL (arXiv): benchmark using monocytes/macrophagesgregoire2023_mixCTRL
Guise et al. 2020 (Cell Rep.): postmortem ALS spinal moto neuronsguise2024
Hu et al, 2023 (The Journal of Physical Chemistry B): Correlated protein moduleshu2023 hu2023_K562 hu2023_oocyte
Khan et al, 2023 (biorRxiv): Epithelial–Mesenchymal Transitionkhan2023
Krull et al, 2024 (Nature Communications): IFN-gamma responsekrull2024
Deprecated leduc2022 datasetleduc2022
Leduc et al. 2022 - plexDIA (biorRxiv): melanoma cellsleduc2022_plexDIA
Leduc et al. 2022 - pSCoPE (biorRxiv): melanoma cells vs monocytesleduc2022_pSCoPE
Liang et al. 2020 (Anal. Chem.): HeLa cells (MaxQuant preprocessing)liang2020_hela
Petrosius et al. 2023 (bioRxiv): AML hierarchy on Astral.petrosius2023_AstralAML
Petrosius et al, 2023 (Nat. Comm.): Mouse embryonic stem cell (mESC) in different culture conditionspetrosius2023_mES
Schoof et al. 2021 (Nat. Comm.): acute myeloid leukemia differentiationschoof2021
Single-Cell Proteomics Data Packagescpdata-package scpdata
Specht et al. 2019 - SCoPE2 (biorRxiv): macrophages vs monocytes (version 2)specht2019v2
Specht et al. 2019 - SCoPE2 (biorRxiv): macrophages vs monocytes (version 3)specht2019v3
Williams et al. 2020 (Anal. Chem.): MCF10A cell linewilliams2020_lfq
Williams et al. 2020 (Anal. Chem.): 3 AML cell linewilliams2020_tmt
Woo et al. 2022 (Cell Syst.): 26 primary human lung cellswoo2022_lung
Woo et al. 2022 (Cell Syst.): LPS-treated macrophageswoo2022_macrophage
Wu et al., 2026 (Nature Biotechnology): single-cell proteomic landscape of the developing human brainwu2026
Zhu et al. 2018 (Mol. Cel. Prot.): rat brain laser dissectionszhu2018MCP
Zhu et al. 2018 (Nat. Comm.): HeLa titrationzhu2018NC_hela
Zhu et al. 2018 (Nat. Comm.): human pancreatic isletszhu2018NC_islets
Zhu et al. 2018 (Nat. Comm.): HeLa lysateszhu2018NC_lysates
Zhu et al. 2019 (eLife): chicken utricle cellszhu2019EL