Spatial proteomics datasets
The pRolocdata package
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pages) that collects published (mainly, although some unpublished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:
Installation
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("pRolocdata")Once installed, the package needs to be loaded
library("pRolocdata")Available datasets
Currently, there are 110 datasets available in
pRolocdata. Use the pRolocdata() function to obtain a list of data
names and their description.
pRolocdata()| 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). |
| 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 | Data from Itzhak et al. (2016) |
| 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 |
| mulvey2015 | Data from Mulvey et al. 2015 |
| mulvey2015norm | 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 |
| 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) |
| 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) |
Loading data
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
data(dunkley2006)To get more information about a given dataset, see its manual page
?dunkley2006
## or
help("dunkley2006")The datasets
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.
Suggested metadata
Additional metadata is available with the pRolocmetadata() function
as detailed below.
Species
Documented in experimentData(.)@samples$species
Tissue
Documented in experimentData(.)@samples$tissue. If tissue is Cell,
then details about the cell line are available in
experimentData(.)@samples$cellLine.
PMID
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 ismarkers. - PredictionCol (
$prediction.fcol) name of the localisation prediction feature data.
Example
experimentData(dunkley2006)@samples## $species
## [1] "Arabidopsis thaliana"
##
## $tissue
## [1] "Callus"
pubMedIds(dunkley2006)## [1] "16618929"
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
Adding new data
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, saySmith_expA.csv,Smith_expB.csvandSmith_expC.csv(the name should ideally be the same as the original files), and the files and provenance is documented ininst/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 toinst/scripts/. That script reads the files above and saves the corresponding (compressed) MSnSet objects directly in data, typically calledSmith2016a.rda,Smith2016a.rda, ..., and the objects themselves would be namedSmith2016a,Smith2016b, ... -
Write a
man/Smith2016.Rddocumentation 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, send me an
email at lg390<AT>cam<dot>ac<dot>uk with the source csv files and
appropriate metadata and I will add it for you.