--- 
title: "Annotating spatial proteomics data" 
author: "Lisa Breckels and Laurent Gatto" 
output:  
  BiocStyle::html_document
vignette: >
  %\VignetteIndexEntry{Annotating spatial proteomics data} 
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r env, echo=FALSE}
library("BiocStyle")
```
# Foreword

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")`.

# Introduction

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 `r Biocpkg("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.

# Loading the data 

We will demonstrate our pipeline for adding and ranking annotation
information using a LOPIT experiment on Pluripotent Mouse Embryonic
stems
([Christoforou et al 2016](http://www.nature.com/ncomms/2016/160112/ncomms9992/full/ncomms9992.html)),
available and documented in the `r Biocpkg("pRolocdata")` data package
as `hyperlopit2015`.

```{r loadData, echo = TRUE, message = FALSE, warning = FALSE}
library("pRoloc")
library("pRolocdata")

## Subset data for markers for example
data("hyperLOPIT2015")
hyperLOPIT2015 <- markerMSnSet(hyperLOPIT2015)
```

# Adding sub-cellular localisation information

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 \Biocpkg{biomaRt}
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.

```{r queryparams}
params <- setAnnotationParams(inputs = c("Mus musculus", 
                                         "UniProtKB/Swiss-Prot ID"))
```

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).

```{r addGO, echo = TRUE, message = FALSE, warning = FALSE, eval = TRUE}
cc <- addGoAnnotations(hyperLOPIT2015, params, 
                       namespace = "cellular_component")
fvarLabels(cc)
```

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.

```{r filterGO, echo = TRUE, message = FALSE, warning = FALSE, eval = TRUE}
## 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)
```

# Correlating and ordering annotation information

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:

1. Extracts all instances (proteins) with the specified term
2. Fits `k` component clusters to this subset using the `kmeans`
   algorithm (the default to test is `k = 1:5`).
3. Calculates all pairwise Euclidean distances per component cluster
4. Normalises each component by the cubed root of the number of
   instances per component (this was set heuristically through
   individual tests and can be set using the argument `p`)
5. Orders the annotation information in `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.

```{r orderMarkers, eval = TRUE, verbose = FALSE}
## Extract markers can use n to specify to select top n terms
res <- orderGoAnnotations(cc, k = 1:3, p = 1/3, verbose = FALSE)
```

```{r viewGO, eval=FALSE}
library("pRolocGUI")
pRolocVis(res, fcol = "GOAnnotations")
```

## Examining distances

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.

```{r clusterDist, eval = TRUE, verbose = FALSE}
## Now calculate distances
dd <- clustDist(cc, fcol = "GOAnnotations", k = 1:3, verbose = FALSE)
dd[[1]]
```

We can use the `plotClustDist` and `plotComponents` to visualise these results. 

```{r visualiseRes, fig.width=12, eval = TRUE}
## Plot normalised distances 
plot(dd, p = 1/3)

## Examine kmeans clustering
plot(dd[[1]], cc)
```

The output of `plotClustDist` is a boxplot of the normalised distances
per term and the output of `plotComponents` is a set of principal
components analysis (PCA) plots, one for each `k` tested, highlighting
the component clusters found according to the kmeans algorithm.

The `getNormDist` function can be used to extract a `vector` of
normalised distances. Which can then be used to rank and order the
terms in the `GOAnnotations` matrix, as per the code chunk below.

```{r minRank, eval = FALSE}
## Normalise by n^1/3
minDist <- getNormDist(dd, p = 1/3)

## Get new order according to lowest distance
o <- order(minDist)

## Re-order `GOAnnotations` matrix in `fData`
fData(cc)$GOAnnotations <- fData(cc)$GOAnnotations[, o]
```

Finally, we can use the `pRolocVis` function in `pRolocGUI` to
visualise our clusters.

```{r visAgain, eval=FALSE}
pRolocVis(cc, fcol = "GOAnnotations")
```
