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Showing content from https://github.com/lgatto/pRolocdata below:

lgatto/pRolocdata: Data accompanying the pRoloc package

Spatial proteomics datasets

pRolocdata is a Bioconductor experiment package (release and devel pages) that collects published (mainly, although some unpublished datasets are also available) mass spectrometry-based spatial/organelle and protein-complex dataset. The data are distributed as MSnSet instances (see the MSnbase for details) and are used throughout the pRoloc and pRolocGUI software for spatial proteomics data analysis and visualisation.

Current build status:

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("pRolocdata")

Once installed, the package needs to be loaded

Currently, there are 144 datasets available in pRolocdata. Use the pRolocdata() function to obtain a list of data names and their description.

Data Description Barylyuk2020ToxoLopit Whole-cell spatial proteome of Toxoplasma: molecular anatomy of an apicomplexan cell E14TG2aR LOPIT experiment on Mouse E14TG2a Embryonic Stem Cells from Breckels et al. (2016) E14TG2aS1 LOPIT experiment on Mouse E14TG2a Embryonic Stem Cells from Breckels et al. (2016) E14TG2aS1goCC LOPIT experiment on Mouse E14TG2a Embryonic Stem Cells from Breckels et al. (2016) E14TG2aS1yLoc LOPIT experiment on Mouse E14TG2a Embryonic Stem Cells from Breckels et al. (2016) E14TG2aS2 LOPIT experiment on Mouse E14TG2a Embryonic Stem Cells from Breckels et al. (2016) HEK293T2011 LOPIT experiment on Human Embryonic Kidney fibroblast HEK293T cells from Breckels et al. (2013) HEK293T2011goCC LOPIT experiment on Human Embryonic Kidney fibroblast HEK293T cells from Breckels et al. (2013) HEK293T2011hpa LOPIT experiment on Human Embryonic Kidney fibroblast HEK293T cells from Breckels et al. (2013) Kozik_con Small molecule enhancers of endosome-to-cytosol import augment anti-tumour immunity Kozik_pra Small molecule enhancers of endosome-to-cytosol import augment anti-tumour immunity Kozik_tam Small molecule enhancers of endosome-to-cytosol import augment anti-tumour immunity Shin2019MitoControlrep1 Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers Shin2019MitoControlrep2 Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers Shin2019MitoControlrep3 Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers Shin2019MitoGcc88rep1 Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers Shin2019MitoGcc88rep2 Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers Shin2019MitoGcc88rep3 Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers Shin2019MitoGol97rep1 Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers Shin2019MitoGol97rep2 Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers Shin2019MitoGol97rep3 Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers andreyev2010 Six sub-cellular fraction data from mouse macrophage-like RAW264.7 cells from Andreyev et al. (2009) andreyev2010activ Six sub-cellular fraction data from mouse macrophage-like RAW264.7 cells from Andreyev et al. (2009) andreyev2010rest Six sub-cellular fraction data from mouse macrophage-like RAW264.7 cells from Andreyev et al. (2009) andy2011 LOPIT experiment on Human Embryonic Kidney fibroblast HEK293T cells from Breckels et al. (2013) andy2011goCC LOPIT experiment on Human Embryonic Kidney fibroblast HEK293T cells from Breckels et al. (2013) andy2011hpa LOPIT experiment on Human Embryonic Kidney fibroblast HEK293T cells from Breckels et al. (2013) at_chloro The AT_CHLORO data base baers2018 Synechocystis spatial proteomics beltran2016HCMV120 Data from Beltran et al. 2016 beltran2016HCMV24 Data from Beltran et al. 2016 beltran2016HCMV48 Data from Beltran et al. 2016 beltran2016HCMV72 Data from Beltran et al. 2016 beltran2016HCMV96 Data from Beltran et al. 2016 beltran2016MOCK120 Data from Beltran et al. 2016 beltran2016MOCK24 Data from Beltran et al. 2016 beltran2016MOCK48 Data from Beltran et al. 2016 beltran2016MOCK72 Data from Beltran et al. 2016 beltran2016MOCK96 Data from Beltran et al. 2016 courtland_control Genetic Disruption of WASHC4 Drives Endo-lysosomal Dysfunction and Cognitive-Movement Impairments in Mice and Humans courtland_mutant Genetic Disruption of WASHC4 Drives Endo-lysosomal Dysfunction and Cognitive-Movement Impairments in Mice and Humans davies2018ap4b1 AP-4 vesicles contribute to spatial control of autophagy via RUSC-dependent peripheral delivery of ATG9A davies2018ap4e1 AP-4 vesicles contribute to spatial control of autophagy via RUSC-dependent peripheral delivery of ATG9A davies2018wt AP-4 vesicles contribute to spatial control of autophagy via RUSC-dependent peripheral delivery of ATG9A dunkley2006 LOPIT data from Dunkley et al. (2006) dunkley2006goCC LOPIT data from Dunkley et al. (2006) fabre2015r1 Data from Fabre et al. 2015 fabre2015r2 Data from Fabre et al. 2015 foster2006 PCP data from Foster et al. (2006) groen2014cmb LOPIT experiments on Arabidopsis thaliana roots, from Groen et al. (2014) groen2014r1 LOPIT experiments on Arabidopsis thaliana roots, from Groen et al. (2014) groen2014r1goCC LOPIT experiments on Arabidopsis thaliana roots, from Groen et al. (2014) groen2014r2 LOPIT experiments on Arabidopsis thaliana roots, from Groen et al. (2014) groen2014r3 LOPIT experiments on Arabidopsis thaliana roots, from Groen et al. (2014) hall2009 LOPIT data from Hall et al. (2009) havugimana2012 Data from Havugimana et al. 2012 hirst2018 Data from Hirst et al. 2018 hyperLOPIT2015 Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016). hyperLOPIT2015_se Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016). hyperLOPIT2015goCC Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016). hyperLOPIT2015ms2 Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016). hyperLOPIT2015ms2psm Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016). hyperLOPIT2015ms3r1 Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016). hyperLOPIT2015ms3r1psm Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016). hyperLOPIT2015ms3r2 Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016). hyperLOPIT2015ms3r2psm Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016). hyperLOPIT2015ms3r3 Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016). hyperLOPITU2OS2017 2017 and 2018 hyperLOPIT on U2OS cells hyperLOPITU2OS2017b 2017 and 2018 hyperLOPIT on U2OS cells hyperLOPITU2OS2018 2017 and 2018 hyperLOPIT on U2OS cells itzhak2016helaCtrl Global, quantitative and dynamic mapping of protein subcellular localization itzhak2016helaEgf Global, quantitative and dynamic mapping of protein subcellular localization itzhak2016stcSILAC itzhak2017 Data from Itzhak et al. 2017 itzhak2017markers Data from Itzhak et al. 2017 kirkwood2013 Data from Kirkwood et al. 2013. krahmer2018pcp Subcellular Reorganization in Diet-Induced Hepatic Steatosis krahmer2018phosphopcp Subcellular Reorganization in Diet-Induced Hepatic Steatosis kristensen2012r1 Data from Kristensen et al. 2012 kristensen2012r2 Data from Kristensen et al. 2012 kristensen2012r3 Data from Kristensen et al. 2012 lopimsSyn1 LOPIMS data for the Synapter 2.0 paper lopimsSyn2 LOPIMS data for the Synapter 2.0 paper lopimsSyn2_0frags LOPIMS data for the Synapter 2.0 paper lopitdcU2OS2018 2017 and 2018 hyperLOPIT on U2OS cells lpsTimecourse_mulvey2021 Protein and PMS-level datasets from temporal abundance profiling experiments of THP-1 human leukaema cells stimulated with LPS lpsTimecourse_rep1_mulvey2021 Protein and PMS-level datasets from temporal abundance profiling experiments of THP-1 human leukaema cells stimulated with LPS lpsTimecourse_rep2_mulvey2021 Protein and PMS-level datasets from temporal abundance profiling experiments of THP-1 human leukaema cells stimulated with LPS lpsTimecourse_rep3_mulvey2021 Protein and PMS-level datasets from temporal abundance profiling experiments of THP-1 human leukaema cells stimulated with LPS moloneyTbBSF Spatial proteomics datasets from two African trypanosome species moloneyTbPCF Spatial proteomics datasets from two African trypanosome species moloneyTcBSF Spatial proteomics datasets from two African trypanosome species moloneyTcPCF Spatial proteomics datasets from two African trypanosome species mulvey2015 Data from Mulvey et al. 2015 mulvey2015_se Data from Mulvey et al. 2015 mulvey2015norm Data from Mulvey et al. 2015 mulvey2015norm_se Data from Mulvey et al. 2015 nikolovski2012 Meta-analysis from Nikolovski et al. (2012) nikolovski2012imp Meta-analysis from Nikolovski et al. (2012) nikolovski2014 LOPIMS data from Nikolovski et al. (2014) orre2019a431 SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization orre2019h322 SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization orre2019hcc827 SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization orre2019hcc827gef SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization orre2019hcc827rep1 SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization orre2019hcc827rep2 SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization orre2019hcc827rep3 SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization orre2019mcf7 SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization orre2019u251 SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization psms_lpsTimecourse_rep1_mulvey2021 Protein and PMS-level datasets from temporal abundance profiling experiments of THP-1 human leukaema cells stimulated with LPS psms_lpsTimecourse_rep2_mulvey2021 Protein and PMS-level datasets from temporal abundance profiling experiments of THP-1 human leukaema cells stimulated with LPS psms_lpsTimecourse_rep3_mulvey2021 Protein and PMS-level datasets from temporal abundance profiling experiments of THP-1 human leukaema cells stimulated with LPS psms_thpLOPIT_lps_rep1_set1 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells psms_thpLOPIT_lps_rep1_set2 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells psms_thpLOPIT_lps_rep2_set1 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells psms_thpLOPIT_lps_rep2_set2 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells psms_thpLOPIT_lps_rep3_set1 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells psms_thpLOPIT_lps_rep3_set2 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells psms_thpLOPIT_unstim_rep1_set1 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells psms_thpLOPIT_unstim_rep1_set2 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells psms_thpLOPIT_unstim_rep2_set1 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells psms_thpLOPIT_unstim_rep2_set2 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells psms_thpLOPIT_unstim_rep3_set1 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells psms_thpLOPIT_unstim_rep3_set2 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells rodriguez2012r1 Spatial proteomics of human inducible goblet-like LS174T cells from Rodriguez-Pineiro et al. (2012) rodriguez2012r2 Spatial proteomics of human inducible goblet-like LS174T cells from Rodriguez-Pineiro et al. (2012) rodriguez2012r3 Spatial proteomics of human inducible goblet-like LS174T cells from Rodriguez-Pineiro et al. (2012) stekhoven2014 Data from Stekhoven et al. 2014 tan2009r1 LOPIT data from Tan et al. (2009) tan2009r1goCC LOPIT data from Tan et al. (2009) tan2009r2 LOPIT data from Tan et al. (2009) tan2009r3 LOPIT data from Tan et al. (2009) thpLOPIT_lps_mulvey2021 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells thpLOPIT_lps_rep1_mulvey2021 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells thpLOPIT_lps_rep2_mulvey2021 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells thpLOPIT_lps_rep3_mulvey2021 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells thpLOPIT_unstimulated_mulvey2021 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells thpLOPIT_unstimulated_rep1_mulvey2021 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells thpLOPIT_unstimulated_rep2_mulvey2021 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells thpLOPIT_unstimulated_rep3_mulvey2021 Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells trotter2010 LOPIT data sets used in Trotter et al. (2010) trotter2010shallow LOPIT data sets used in Trotter et al. (2010) trotter2010steep LOPIT data sets used in Trotter et al. (2010) yeast2018 Saccharomyces cerevisiae spatial proteomics (2018)

Data is loaded into the R session using the load function; for instance, to get the data from Dunkley et al (2006), one would type

To get more information about a given dataset, see its manual page

?dunkley2006
## or
help("dunkley2006")

Each data object in pRolocdata is available as an MSnSet instance. The instances contain the actual quantitative data, sample and features annotations (see pData and fData respectively). Additional MIAPE data [1, 2] experimental data is available in the experimentData slot, as described in section Required metadata below.

The source of these data is generally one or several text-based spreadsheet (csv, tsv) produced by a third-party application. These original files are often distributed as supplementary material to the research paper and used to generate the R objects. These source spreadsheets are available in the package's inst/extdata directory. The R script files, that read the spreadsheets and create the R data is distributed in the inst/scripts directory.

Additional metadata is available with the pRolocmetadata() function as detailed below.

Documented in experimentData(.)@samples$species

Documented in experimentData(.)@samples$tissue. If tissue is Cell, then details about the cell line are available in experimentData(.)@samples$cellLine.

Documented in pubMedIds(.).

Spatial proteomics experiment annotation

Documented in experimentData(.)@other:

MS ($MS) type of mass spectrometry experiment: iTRAQ8, iTRAQ4, TMT6, LF, SC, ...
Experiment ($spatexp) type of spatial proteomics experiment: LOPIT, LOPIMS, subtractive, PCP, other, PCP-SILAC, ...
MarkerCol ($markers.fcol) name of the markers feature data. Default is markers.
PredictionCol ($prediction.fcol) name of the localisation prediction feature data.

experimentData(dunkley2006)@samples

## $species
## [1] "Arabidopsis thaliana"
## 
## $tissue
## [1] "Callus"

otherInfo(experimentData(dunkley2006))

## $MS
## [1] "iTRAQ4"
## 
## $spatexp
## [1] "LOPIT"
## 
## $markers.fcol
## [1] "pd.markers"
## 
## $prediction.fcol
## [1] "pd.2013"

## all at once
pRolocmetadata(dunkley2006)

## pRoloc experiment metadata:
##  Species: Arabidopsis thaliana
##  Tissue: Callus
##  CellLine: NA
##  PMID: 16618929
##  MS: iTRAQ4
##  Experiment: LOPIT
##  MarkerCol: pd.markers
##  PredictionCol: pd.2013

The procedure to data in pRolocdata is as follows. Here, we assume that 3 new data files are available from the manuscript of Smith et al. 2017, and these files will be added to pRolocdata as three MSnSet objects.

the original data (often from supplementary material) are added to inst/extdata, say Smith_expA.csv, Smith_expB.csv and Smith_expC.csv (the name should ideally be the same as the original files), and the files and provenance is documented in inst/extdata/README. If the data files are really big, then they should be compressed. If they are too big (for example don't fit on github or would substantially increase the size of the package), then we might decide not to added them, but they should still be documented in the README file and the script (see point 2) should still assume they are there.
A script, typically called Smith2017.R, is added to inst/scripts/. That script reads the files above and saves the corresponding (compressed) MSnSet objects directly in data, typically called Smith2016a.rda, Smith2016a.rda, ..., and the objects themselves would be named Smith2016a, Smith2016b, ...
Write a man/Smith2016.Rd documentation file documenting all relevant data objects, providing some information about the experiment and data provenance, and a reference to the original paper.
Build and check the package and, if successful, send a github pull request.

If you do not have the R expertise to prepare the data, please open an issue in the pRolocdata Github repo or send me an email at laurent.gatto<AT>uclouvain<dot>be with the source csv files and appropriate metadata and I will add it for you.

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