pRoloc 1.45.1
This document walks users through a typical pipeline for adding
annotation information to spatial proteomics data. For a general
practical introduction to pRoloc and spatial proteomics data analysis,
readers are referred to the tutorial, available using
vignette("pRoloc-tutorial", package = "pRoloc")
.
Exploring protein annotations and defining sub-cellular localisation
markers (i.e. known residents of a specific sub-cellular niche in a
species, under a condition of interest) play important roles in the
analysis of spatial proteomics data. The latter is essential for
downstream supervised machine learning (ML) classification for protein
localisation prediction (see vignette("pRoloc-tutorial", package = "pRoloc")
and vignette("pRoloc-ml", package = "pRoloc")
for
information on available ML methods) and the former is interesting for
initial biological interpretation through matching annotations to the
data structure.
Robust protein-localisation prediction is reliant on markers that reflect the true sub-cellular diversity of the multivariate data. The validity of markers is generally assured by expert curation. This can be time consuming and difficult owing to the limited number of marker proteins that exist in databases and elsewhere. The Gene Ontology (GO) database, and in particular the cellular compartment (CC) namespace provide a good starting point for protein annotation and marker definition. Nevertheless, automatic extraction from databases, and in particular GO CC, is only a first step in sub-cellular localisation analysis and requires additional curation to counter unreliable annotation based on data that is inaccurate or out of context for the biological question under investigation.
To facilitate the above, we have developed an annotation retrieval and
management system that provides a flexible framework for the
exploration of the sub-cellular proteomics data. We have developed a
method to correlate annotation information with the multivariate data
space to identify densely annotated regions and assess cluster
tightness. Given a set of proteins that share some property e.g. a
specified GO term, a k-means clustering is used to fit the data
(testing k = 1:5
) and then for each number of k
components tested,
all pairwise Euclidean distances are calculated per component, and
then normalised. The minimum mean normalised distance is then
extracted and used as a measure of cluster tightness. This is repeated
for all protein/annotation sets. These sets are then ranked according
to minimum mean normalised distance and then can be displayed and
explored using the pRolocGUI package.
In this vignette we present a step-by-step guide showing users how to (1) how to add protein annotations, here we use the GO database as an example, and (2) rank and order information (e.g. GO terms) according to their correlation with the data structure, for the extraction of optimal data specific annotated clusters.
We will demonstrate our pipeline for adding and ranking annotation
information using a LOPIT experiment on Pluripotent Mouse Embryonic
stems
(Christoforou et al 2016),
available and documented in the pRolocdata data package
as hyperlopit2015
.
library("pRoloc")
library("pRolocdata")
## Subset data for markers for example
data("hyperLOPIT2015")
hyperLOPIT2015 <- markerMSnSet(hyperLOPIT2015)
All GO terms associated to proteins that appear in the dataset are
retrieved and used to create a binary matrix where a 1 (0) at position
\((i,j)\) indicates that term \(j\) has (not) been used to annotate
feature \(i\). This matrix is appended and stored in the feature data
slot of the MSnSet
dataset using the addGoAnnotations
function. We
first however need to prepare annotation parameters that will enable
us to query the Biomart repository using the
package, from where we are able to retrieve GO terms. The specific
Biomart repository and query will depend on the species under study
and the type of features. This can be set using the
setAnnotationParams
function.
In the code chunk below we set the annotation parameters for the
hyperLOPIT2015
dataset. As this species used was mouse and the
featureNames
of the hyperLOPIT2015
dataset are Uniprot accession
numbers the input to the function is defined as inputs = c("Mus musculus", "UniProtKB/Swiss-Prot ID")
. See ?setAnnotationParams
for
details.
params <- setAnnotationParams(inputs = c("Mouse genes",
"UniProtKB/Swiss-Prot ID"))
## Using species Mouse genes (GRCm39)
## Warning: Ensembl will soon enforce the use of https.
## Ensure the 'host' argument includes "https://"
## Using feature type UniProtKB/Swiss-Prot ID(s) [e.g. A0A087WPF7]
## Connecting to Biomart...
## Warning: Ensembl will soon enforce the use of https.
## Ensure the 'host' argument includes "https://"
Now the parameters for the search have been defined we can use the
addGoAnnotations
function to add a GO information matrix to the
featureData
slot of the dataset. The addGoAnnotations
function
takes a MSnSet
instance as input (from which the featureNames
will
be extracted) and it downloads the CC terms (the default, biological
process and the molecular function namespaces are also supported)
found for each protein in the dataset. The output MSnSet
has the CC
term binary matrix appended to the fData
, by default this is called
GOAnnotations
(and changed using the fcol
argument).
cc <- addGoAnnotations(hyperLOPIT2015, params,
namespace = "cellular_component")
fvarLabels(cc)
## [1] "entry.name" "protein.description"
## [3] "peptides.rep1" "peptides.rep2"
## [5] "psms.rep1" "psms.rep2"
## [7] "phenodisco.input" "phenodisco.output"
## [9] "curated.phenodisco.output" "markers"
## [11] "svm.classification" "svm.score"
## [13] "svm.top.quartile" "final.assignment"
## [15] "first.evidence" "curated.organelles"
## [17] "cytoskeletal.components" "trafficking.proteins"
## [19] "protein.complexes" "signalling.cascades"
## [21] "oct4.interactome" "nanog.interactome"
## [23] "sox2.interactome" "cell.surface.proteins"
## [25] "markers2015" "TAGM"
## [27] "GOAnnotations"
The addGoAnnotations
function by defualt does not do any filtering
of the terms evidence codes unless specified in the evidence
argument, see ?addGoAnnotations
for more details.
With many well-annotated species and datasets containing typically
thousands of proteins, we often find many CC terms, of which many may
not be particularly meaningful. These such terms can be filtered out
using the filerMinMarkers
and filterMaxMarkers
functions.
## Next we filter the GO term matrix removing any terms that have
## have less than `n` proteins or greater than `p` % of total proteins
## in the dataset (this removes terms that only have very few proteins
## and very general terms)
cc <- filterMinMarkers(cc)
cc <- filterMaxMarkers(cc)
Now we have extracted and filtered annotation information for our
dataset we re-order the GOAnnotations
matrix of terms according to
their correlation with the dataset structure. To do this we use the
orderGoAnnotations
function.
For each piece of annotation information, e.g. for each GO CC term in the matrix, the function:
k
component clusters to this subset using the kmeans
algorithm (the default to test is k = 1:5
).p
)GOAnnotations
according to
the minimum normalised Euclidean distance.We find that high density clusters have the low mean normalised
Euclidean distances. In the below chunk we test try fitting k = 1:3
component clusters per term and normalise by p = 1/3
. The ordered
terms can be displayed using the pRolocVis
function in the
pRolocGUI
package.
## Extract markers can use n to specify to select top n terms
res <- orderGoAnnotations(cc, k = 1:3, p = 1/3, verbose = FALSE)
## Calculating GO cluster densities
library("pRolocGUI")
pRolocVis(res, fcol = "GOAnnotations")
Instead of using the orderGoAnnotations
function which is a wrapper
for steps 1 - 5 above, it is possible to calculate the Euclidean
distances manually using the clustDist
function. The input is a
MSnSet
dataset with the matrix of markers e.g. GOAnnotations
appended to the fData
slot. The output is a "ClustDistList"
. The
"ClustDist"
and "ClustDistList"
class summarises the algorithm
information such as the number of k’s tested for the kmeans, and mean
and normalised pairwise Euclidean distances per numer of component
clusters tested.
## Now calculate distances
dd <- clustDist(cc, fcol = "GOAnnotations", k = 1:3, verbose = FALSE)
dd[[1]]
## Object of class "ClustDist"
## fcol = GOAnnotations
## term = cytoskeleton
## id = cytoskeleton
## nrow = 32
## k's tested: 1 2 3
## Size: 32
## Size: 24
## Size: 15, 11
## Clusters info:
## ks.mean mean ks.norm norm
## k = 1 1 0.4208 1 0.13253
## k = 2 1 *0.2104 1 *0.07293
## k = 3 2 0.2181 2 0.09381
We can use the plotClustDist
and plotComponents
to visualise these results.
## Plot normalised distances
plot(dd, p = 1/3)