moloney2023: Spatial proteomics datasets from two African trypanosome...
In lgatto/pRolocdata: Data accompanying the pRoloc package
moloneyTbBSF R Documentation
Spatial proteomics datasets from two African trypanosome species
Description
Spatial proteomics datasets from a hyperLOPIT experimental design on two on
two African trypanosome species, Trypanosoma brucei and
Trypanosoma congolense, which have been mapped across two life-stages.
Usage
data(moloneyTbBSF)
data(moloneyTbPCF)
data(moloneyTcBSF)
data(moloneyTcPCF)
Format
These data are instances of class MSnSet from package
MSnbase.
Details
Protein function is intimately linked with localisation and as a consequence the
subcellular distribution of a protein provides information on its role in the
cell. We have optimised a method for resolving subcellular compartments in
Trypanosoma brucei and Trypanosoma congolense and implemented it
in the spatial proteomics strategy of hyperLOPIT (hyperplexed localisation of
organelle proteins by isotope tagging) (Christoforou et al. 2016; Mulvey et al.
2017). Between the vertebrate and insect stages of these parasites, represented
by bloodstream and procyclic forms respectively, we have detected over 7000
proteins in each species across three biological iterations. Of these, 6182
T. brucei proteins and 6324 T. congolense proteins are included in
a spatial proteome characterisation (Trotter et al., 2010). Classification to
19-23 subcellular compartments was performed using a machine learning approach
based on a T-augmented Gaussian mixture model (Crook et al. 2019; Crook et al.
2018). With 713-852 compartment marker proteins, this has yielded localisation
information for 2504-2795 proteins in each organism.
The data (expression and feature variable) contain:
MW: TriTrypDB based molecular mass (Aslett et al. 2010)
Signal_peptide: TriTrypDB based signal peptide prediction.
TM: TriTrypDB based predicted number of transmembrane domains.
Curated_GO_Processes: TriTrypDB based curated gene ontology biological
process term.
Computed_GO_Processes: TriTrypDB based computed gene ontology biological
process term.
Curated_GO_Components: TriTrypDB based curated gene ontology cellular
component term.
Computed_GO_Components: TriTrypDB based computed gene ontology cellular
component term.
PFam_Description: TriTrypDB based Pfam description.
HDBSCAN_cluster: Cluster number according to HDBSCAN unsupervised
clustering (Campello et al. 2013)
HDBSCAN_cluster_probability: Probability of membership associated with
HDBSCAN clustering.
NetGPI: Binary prediction of GPI anchor according to NetGPI (Gíslason
et al. 2021)
DeepLoc_location: Subcellular localisation predicted by DeepLoc (Almagro
Armenteros et al. 2017)
computed_pI: pI computed by pIR (Perez-Riverol et al. 2012, Audain et al.
2016)
markers: The marker set used for TAGM-MAP classification of protein
subcellular localisation.
tagm.map.allocation: The TAGM-MAP prediction for the most probable
subcellular allocation.
tagm.map.probability: The posterior probability for the master protein
subcellular allocations computed by TAGM-MAP.
tagm.map.outlier: The posterior probability for the master protein to
belong to the outlier component rather than any of the annotated components.
tagm.map.localisation.probability: The localisation probability for the
master protein to belong a subcellular class; defined as the product of the
tagm.map.probability: and 1 - tagm.map.outlier
tagm.map.localisation.prediction: The final prediction of the master
protein subcellular localisation based on its localisation probability; only
proteins with a localisation probability of greater than 99.9 percent and outlier
probability of less than 5E-5 were retained.
tagm.mcmc.allocation: The TAGM-MCMC prediction for the most probable
subcellular allocation.
tagm.mcmc.probability: The mean posterior probability for the master
protein subcellular allocations computed by TAGM-MCMC.
tagm.mcmc.probability.lowerquantile: The lower boundary to the equitailed
95-credible interval of tagm.mcmc.probability.
tagm.mcmc.probability.upperquantile: The upper boundary to the equitailed
95-credible interval of tagm.mcmc.probability.
tagm.mcmc.mean.shannon: A Monte-Carlo averaged Shannon entropy, which is a
measure of uncertainty in the allocations.
tagm.mcmc.outlier: The posterior probability for the master protein to
belong to the outlier component rather than any of the annotated components.
tagm.mcmc.joint: The posterior probability for the master protein
allocation to each of the subcellular classes determined by TAGM-MCMC.
Source
The data was generated by N. Moloney at the Cambridge Centre for Proteomics.
http://www.bio.cam.ac.uk/proteomics/.
References
Christoforou, A., Mulvey, C.M., Breckels, L.M., Geladaki, A., Hurrell, T.,
Hayward, P.C., Naake, T., Gatto, L., Viner, R., Martinez Arias, A., and Lilley,
K.S. (2016). A draft map of the mouse pluripotent stem cell spatial proteome.
Nat Commun 7, 8992. 10.1038/ncomms9992.
Crook, O.M., Breckels, L.M., Lilley, K.S., Kirk, P.D.W., and Gatto, L. (2019). A
Bioconductor workflow for the Bayesian analysis of spatial proteomics.
F1000Research 8, 446. 10.12688/f1000research.18636.1.
Crook, O.M., Mulvey, C.M., Kirk, P.D.W., Lilley, K.S., and Gatto, L. (2018). A
Bayesian mixture modelling approach for spatial proteomics. PLOS Computational
Biology 14, e1006516. 10.1371/journal.pcbi.1006516.
Mulvey, C.M., Breckels, L.M., Geladaki, A., Britovsek, N.K., Nightingale,
D.J.H., Christoforou, A., Elzek, M., Deery, M.J., Gatto, L., and Lilley, K.S.
(2017). Using hyperLOPIT to perform high-resolution mapping of the spatial
proteome. Nat Protoc 12, 1110-1135. 10.1038/nprot.2017.026.
Trotter, M.W., Sadowski, P.G., Dunkley, T.P., Groen, A.J., and Lilley, K.S.
(2010). Improved sub‐cellular resolution via simultaneous analysis of organelle
proteomics data across varied experimental conditions. Proteomics 10, 4213-4219.
Examples
## load the data
data(moloneyTbBSF)
## View a summary of the data MSnSet data container
moloneyTbBSF
## PCA plot of the data
library("pRoloc")
plot2D(moloneyTbBSF, fcol = "tagm.map.localisation.prediction",
main = "PCA plot map of T. Brucei bloodstream form")
## Pass pre-computed t-SNE coords and plot the data
tsne_coords <- as.matrix(fData(moloneyTbBSF)[, c("Dim.1", "Dim.2")])
plot2D(tsne_coords, fcol = "tagm.map.localisation.prediction",
method = "none", methargs = list(moloneyTbBSF),
main = "t-SNE map of T. Brucei bloodstream form")
lgatto/pRolocdata documentation built on Nov. 27, 2024, 12:12 a.m.
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Spatial proteomics datasets from two African trypanosome species
Description
Spatial proteomics datasets from a hyperLOPIT experimental design on two on two African trypanosome species, Trypanosoma brucei and Trypanosoma congolense, which have been mapped across two life-stages.
Usage
data(moloneyTbBSF)
data(moloneyTbPCF)
data(moloneyTcBSF)
data(moloneyTcPCF)
Format
These data are instances of class MSnSet from package
MSnbase.
Details
Protein function is intimately linked with localisation and as a consequence the subcellular distribution of a protein provides information on its role in the cell. We have optimised a method for resolving subcellular compartments in Trypanosoma brucei and Trypanosoma congolense and implemented it in the spatial proteomics strategy of hyperLOPIT (hyperplexed localisation of organelle proteins by isotope tagging) (Christoforou et al. 2016; Mulvey et al. 2017). Between the vertebrate and insect stages of these parasites, represented by bloodstream and procyclic forms respectively, we have detected over 7000 proteins in each species across three biological iterations. Of these, 6182 T. brucei proteins and 6324 T. congolense proteins are included in a spatial proteome characterisation (Trotter et al., 2010). Classification to 19-23 subcellular compartments was performed using a machine learning approach based on a T-augmented Gaussian mixture model (Crook et al. 2019; Crook et al. 2018). With 713-852 compartment marker proteins, this has yielded localisation information for 2504-2795 proteins in each organism.
The data (expression and feature variable) contain:
MW: TriTrypDB based molecular mass (Aslett et al. 2010)
Signal_peptide: TriTrypDB based signal peptide prediction.
TM: TriTrypDB based predicted number of transmembrane domains.
Curated_GO_Processes: TriTrypDB based curated gene ontology biological process term.
Computed_GO_Processes: TriTrypDB based computed gene ontology biological process term.
Curated_GO_Components: TriTrypDB based curated gene ontology cellular component term.
Computed_GO_Components: TriTrypDB based computed gene ontology cellular component term.
PFam_Description: TriTrypDB based Pfam description.
HDBSCAN_cluster: Cluster number according to HDBSCAN unsupervised clustering (Campello et al. 2013)
HDBSCAN_cluster_probability: Probability of membership associated with HDBSCAN clustering.
NetGPI: Binary prediction of GPI anchor according to NetGPI (Gíslason et al. 2021)
DeepLoc_location: Subcellular localisation predicted by DeepLoc (Almagro Armenteros et al. 2017)
computed_pI: pI computed by pIR (Perez-Riverol et al. 2012, Audain et al. 2016)
markers: The marker set used for TAGM-MAP classification of protein subcellular localisation.
tagm.map.allocation: The TAGM-MAP prediction for the most probable subcellular allocation.
tagm.map.probability: The posterior probability for the master protein subcellular allocations computed by TAGM-MAP.
tagm.map.outlier: The posterior probability for the master protein to belong to the outlier component rather than any of the annotated components.
tagm.map.localisation.probability: The localisation probability for the master protein to belong a subcellular class; defined as the product of the
tagm.map.probability: and 1 - tagm.map.outlier
tagm.map.localisation.prediction: The final prediction of the master protein subcellular localisation based on its localisation probability; only proteins with a localisation probability of greater than 99.9 percent and outlier probability of less than 5E-5 were retained.
tagm.mcmc.allocation: The TAGM-MCMC prediction for the most probable subcellular allocation.
tagm.mcmc.probability: The mean posterior probability for the master protein subcellular allocations computed by TAGM-MCMC.
tagm.mcmc.probability.lowerquantile: The lower boundary to the equitailed 95-credible interval of tagm.mcmc.probability.
tagm.mcmc.probability.upperquantile: The upper boundary to the equitailed 95-credible interval of tagm.mcmc.probability.
tagm.mcmc.mean.shannon: A Monte-Carlo averaged Shannon entropy, which is a measure of uncertainty in the allocations.
tagm.mcmc.outlier: The posterior probability for the master protein to belong to the outlier component rather than any of the annotated components.
tagm.mcmc.joint: The posterior probability for the master protein allocation to each of the subcellular classes determined by TAGM-MCMC.
Source
The data was generated by N. Moloney at the Cambridge Centre for Proteomics. http://www.bio.cam.ac.uk/proteomics/.
References
Christoforou, A., Mulvey, C.M., Breckels, L.M., Geladaki, A., Hurrell, T., Hayward, P.C., Naake, T., Gatto, L., Viner, R., Martinez Arias, A., and Lilley, K.S. (2016). A draft map of the mouse pluripotent stem cell spatial proteome. Nat Commun 7, 8992. 10.1038/ncomms9992.
Crook, O.M., Breckels, L.M., Lilley, K.S., Kirk, P.D.W., and Gatto, L. (2019). A Bioconductor workflow for the Bayesian analysis of spatial proteomics. F1000Research 8, 446. 10.12688/f1000research.18636.1.
Crook, O.M., Mulvey, C.M., Kirk, P.D.W., Lilley, K.S., and Gatto, L. (2018). A Bayesian mixture modelling approach for spatial proteomics. PLOS Computational Biology 14, e1006516. 10.1371/journal.pcbi.1006516.
Mulvey, C.M., Breckels, L.M., Geladaki, A., Britovsek, N.K., Nightingale, D.J.H., Christoforou, A., Elzek, M., Deery, M.J., Gatto, L., and Lilley, K.S. (2017). Using hyperLOPIT to perform high-resolution mapping of the spatial proteome. Nat Protoc 12, 1110-1135. 10.1038/nprot.2017.026.
Trotter, M.W., Sadowski, P.G., Dunkley, T.P., Groen, A.J., and Lilley, K.S. (2010). Improved sub‐cellular resolution via simultaneous analysis of organelle proteomics data across varied experimental conditions. Proteomics 10, 4213-4219.
Examples
## load the data
data(moloneyTbBSF)
## View a summary of the data MSnSet data container
moloneyTbBSF
## PCA plot of the data
library("pRoloc")
plot2D(moloneyTbBSF, fcol = "tagm.map.localisation.prediction",
main = "PCA plot map of T. Brucei bloodstream form")
## Pass pre-computed t-SNE coords and plot the data
tsne_coords <- as.matrix(fData(moloneyTbBSF)[, c("Dim.1", "Dim.2")])
plot2D(tsne_coords, fcol = "tagm.map.localisation.prediction",
method = "none", methargs = list(moloneyTbBSF),
main = "t-SNE map of T. Brucei bloodstream form")
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