---
title: "qPLEXanalyzer"
author: Matthew Eldridge, Kamal Kishore, and Ashley Sawle
date: "`r format(Sys.Date(), '%m/%d/%Y')`"
output: 
  rmarkdown::html_vignette:
    toc: true
bibliography: library.bib
vignette: >
  %\VignetteIndexEntry{qPLEXanalyzer}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteKeywords{Quantitative proteomics, TMT}
  \usepackage[utf8]{inputenc}
---

```{r setup, echo=FALSE, results="hide"}
knitr::opts_chunk$set(tidy = FALSE,
                      cache = FALSE,
                      dev = "png",
                      message = FALSE, error = FALSE, warning = TRUE)
``` 

## Overview

This document provides brief tutorial of the _qPLEXanalyzer_ package, 
a toolkit with multiple functionalities, for statistical analysis of qPLEX-RIME 
proteomics data (see `?qPLEXanalyzer` at the R prompt for a brief overview). The
qPLEX-RIME approach combines the RIME method with multiplex TMT chemical 
isobaric labelling to study the dynamics of chromatin-associated protein 
complexes. The package can also be used for isobaric labelling (TMT or iTRAQ) 
based total proteome analysis.

- Import quantitative dataset: A pre-processed quantitative dataset 
generated from MaxQuant, Proteome Discoverer or any other proteomic software 
consisting of peptide intensities with associated features along with sample 
meta-data information can be imported by _qPLEXanalyzer_. 

- Quality control: Computes and displays quality control statistics plots 
of the quantitative dataset.

- Data normalization: Quantile normalization, central tendencies scaling 
and linear regression based normalization.

- Aggregation of peptide intensities into protein intensities

- Differential statistical analysis: _limma_ based analysis to 
identify differentially abundant proteins.


## Import quantitative dataset

[MSnbase](http://bioconductor.org/packages/MSnbase) [@Gatto2012; @Gatto2020]
package by Laurent Gatto provides methods to facilitate reproducible analysis of
MS-based proteomics data. The _MSnSet_ class of
[MSnbase](http://bioconductor.org/packages/MSnbase) provides architecture for
storing quantitative MS proteomics data and the experimental meta-data. In
_qPLEXanalyzer_, we store pre-processed quantitative proteomics data within this
standardized object. The `convertToMSnset` function creates an _MSnSet_ object
from the quantitative dataset of peptides/protein intensities. This dataset must
consist of peptides identified with high confidence in all the samples.

The default input dataset is the pre-processed peptide intensities from
MaxQuant, Proteome Discoverer or any other proteomic software (see
`?convertToMSnset` at the R prompt for more details). Only peptides uniquely
matching to a protein should be used as an input. Alternatively, the protein
level quantification by the aggregation of the peptide TMT intensities can also
be used as input. Peptides/Protein intensities with missing values in one or
more samples can either be excluded or included in the _MSnSet_ object. If the
missing values are kept in the _MSnSet_ object, these must be imputed either by
user defined methods or by those provided in
[MSnbase](http://bioconductor.org/packages/MSnbase) package. The downstream
functions of _qPLEXanalyzer_ expect a matrix with no missing values in the
_MSnSet_ object.

The example dataset shown below is from an ER qPLEX-RIME experiment in MCF7 
cells that was performed to compare two different ways of cell crosslinking: 
DSG/formaldehyde (double) or formaldehyde alone (single). It consists of four 
biological replicates for each condition along with two IgG samples pooled from 
replicates of each group. 

```{r libs}
library(qPLEXanalyzer)
library(gridExtra)
data(human_anno)
data(exp2_Xlink)
```

```{r Import}
MSnset_data <- convertToMSnset(exp2_Xlink$intensities,
                               metadata = exp2_Xlink$metadata,
                               indExpData = c(7:16), 
                               Sequences = 2, 
                               Accessions = 6)
MSnset_data
```

## Quality control

Once an _MSnSet_ object has been created, various descriptive statistics methods
can be used to check the quality of the dataset. 

### Peptide intensity plots

The `intensityPlot` function generates a peptide intensity distribution plot
that helps in identifying samples with outlier distributions. [Figure
1](#Figure1) shows the distribution of the log-intensity of peptides/proteins
for each sample. An outlier sample DSG.FA.rep01 can be identified from this
plot. IgG control samples representing low background intensities will have
shifted/distinct intensity distribution curve as compared to other samples and
should not be considered as outliers.

<a name="Figure1" />

```{r Filter, fig.width=6, fig.height=5, fig.cap="Figure 1: Density plots of raw intensities for TMT-10plex experiment."}
intensityPlot(MSnset_data, title = "Peptide intensity distribution")
```

The intensities can also be viewed in the form of boxplots by 
`intensityPlot`. [Figure 2](#Figure2) shows the distribution of peptides 
intensities for each sample. 

<a name="Figure2" />

```{r boxplot, fig.width=6, fig.height=5, fig.cap="Figure 2: Boxplot of raw intensities for TMT-10plex experiment."}
intensityBoxplot(MSnset_data, title = "Peptide intensity distribution")
```

### Relative log intensity boxplot

`rliPlot` can be used to visualise unwanted variation in a data set. It is
similar to the relative log expression plot developed for microarray analysis
[@Gandolfo2018]. Rather than examining gene expression, the RLI plot ([Figure
3](#Figure3)) uses the MS intensities for each peptide or the summarised protein
intensities.
  
<a name="Figure3" />

```{r rliplot, fig.width=6, fig.height=5, fig.cap="Figure 3: RLI of raw intensities for TMT-10plex experiment."}
rliPlot(MSnset_data, title = "Relative Peptide intensity")
```

### Sample correlation plot

A Correlation plot can be generated by `corrPlot` to visualize the level of
linear association of samples within and between groups. The plot in 
[Figure 4](#Figure4) displays high correlation among samples within each group, 
however an outlier sample is also identified in one of the groups (DSG.FA).

<a name="Figure4" />

```{r Corrplot, fig.width=6, fig.height=6, fig.cap="Figure 4: Correlation plot of peptide intensities"}
corrPlot(MSnset_data)
```

### Hierachical clustering dendrogram

Hierarchical clustering can be performed by `hierarchicalPlot` to produce a
dendrogram displaying the hierarchical relationship among samples 
([Figure 5](#Figure5)). The horizontal axis shows the dissimilarity (measured by
means of the Euclidean distance) between samples: similar samples appear on the
same branches. Colors correspond to groups. If the data set contains zeros, it
will be necessary to add a small value (e.g. 0.01) to the intentsities in order
to avoid errors while generating dendrogram.

<a name="Figure5" />

```{r hierarchicalplot, fig.width=6, fig.height=5, fig.cap="Figure 5: Clustering plot of peptide intensitites"}
exprs(MSnset_data) <- exprs(MSnset_data) + 0.01
hierarchicalPlot(MSnset_data)
```

### Principle component analysis scatterplot

A visual representation of the scaled loading of the first two dimensions of a 
PCA analysis can be obtained by `pcaPlot` ([Figure 6](#Figure6)). Co-variances 
between samples are approximated by the inner product between samples. Highly 
correlated samples will appear close to each other. The samples could be 
labeled by name, replicate, group or experiment run allowing for identification 
of potential batch effects.

<a name="Figure6" />

```{r pcaplot, fig.width=6, fig.height=5, fig.cap="Figure 6: PCA plot of peptide intensitites"}
pcaPlot(MSnset_data, labelColumn = "BioRep", pointsize = 3)
```

### Bait protein coverage plot

A plot showing regions of the bait protein covered by captured peptides can be
produced using `coveragePlot` ([Figure 7](#Figure7)). The plot shows the
location of peptides that have been identified with high confidence across the
protein sequence and the corresponding percentage of coverage. This provides a
means of assessing the efficiency of the immunoprecipitation approach in the
qPLEX-RIME method. For a better evaluation of the pull down assay we could
compare the observed bait protein coverage with the theoretical coverage from
peptides predicted by known cleavage sites.

<a name="Figure7" />

```{r coverageplot, fig.width=6, fig.height=1.5, fig.cap="Figure 7: Peptide sequence coverage plot"}
mySequenceFile <- system.file("extdata", "P03372.fasta", package = "qPLEXanalyzer")
coveragePlot(MSnset_data,
             ProteinID = "P03372", 
             ProteinName = "ESR1",
             fastaFile = mySequenceFile)
```

## Data normalization

The data can be normalized to remove experimental artifacts (e.g. differences in
sample loading variability, systemic variation) in order to separate biological
variations from those introduced during the experimental process. This would
improve downstream statistical analysis to obtain more accurate comparisons.
Different normalization methods can be used depending on the data:

- Quantiles `normalizeQuantiles`: The peptide intensities are roughly replaced
by the order statistics on their abundance. The key assumption underneath is
that there are only few changes between different groups. This normalization
technique has the effect of making the distributions of intensities from the
different samples identical in terms of their statistical properties. It is the
strongest normalization method and should be used carefully as it erases most of
the difference between the samples. We would recommend using it only for total
proteome but not for qPLEX-RIME data.

- Mean/median scaling `normalizeScaling`: In this normalization method the
central tendencies (mean or median) of the samples are aligned. The central
tendency for each sample is computed and log transformed. A scaling factor is
determined by subtracting from each central tendency the mean of all the central
tendencies. The raw intensities are then divided by the scaling factor to get
normalized ones.

- Row scaling `rowScaling`: In this normalization method each peptide/protein
intensity is divided by the mean/median of its intensity across all samples and
log2 transformed.

It is imperative to check the intensity distribution plot and PCA plot before
and after normalization to verify its effect on the dataset.

In qPLEX-RIME data, the IgG (or control samples) should be normalized separately
from the bait protein pull-down samples. As IgG samples represent the low
background intensity, their intensity distribution profile is different from
bait pull-downs. Hence, normalizing the two together would result in
over-correction of the IgG intensity resulting in inaccurate computation of
differences among groups. To this end we provide `groupScaling`, the additional
parameter _groupingColumn_ defines a category for grouping the samples, scaling
is then carried out within each group independently.

If no normalization is necessary, skip this step and move to aggregation of 
peptides. 

For this dataset, an outlier sample was identified by quality control plots and
removed from further analysis. [Figure 8](#Figure8) displays the effect of
various normalization methods on the peptide intensities distribution.

<a name="Figure8" />

```{r norm, fig.width=7, fig.height=7, fig.cap="Figure 8: Peptide intensity distribution with various normalization methods"}
MSnset_data <- MSnset_data[, -5]
p1 <- intensityPlot(MSnset_data, title = "No normalization")

MSnset_norm_q <- normalizeQuantiles(MSnset_data)
p2 <- intensityPlot(MSnset_norm_q, title = "Quantile")

MSnset_norm_ns <- normalizeScaling(MSnset_data, scalingFunction = median)
p3 <- intensityPlot(MSnset_norm_ns, title = "Scaling")

MSnset_norm_gs <- groupScaling(MSnset_data, 
                               scalingFunction = median, 
                               groupingColumn = "SampleGroup")
p4 <- intensityPlot(MSnset_norm_gs, title = "Within Group Scaling")

grid.arrange(p1, p2, p3, p4, ncol = 2, nrow = 2)

```

## Aggregation of peptide intensities into protein intensities

The quantitative dataset could consist of peptide or protein intensities. If the
dataset consists of peptide information, they can be aggregated to protein
intensities for further analysis.

An annotation file consisting of proteins with unique ID must be provided. An
example file can be found with the package corresponding to uniprot annotation
of human proteins. It consists of four columns: 'Accessions', 'Gene',
'Description' and 'GeneSymbol'. The columns 'Accessions'and 'GeneSymbol' are
mandatory for successful downstream analysis while the other two columns are
optional. The [UniProt.ws](http://bioconductor.org/packages/niProt.ws) package
provides a convenient means of obtaining these annotations using Uniprot protein
accessions, as shown in the section below. The `summarizeIntensities` function
expects an annotation file in this format.

```{r annotation, eval=FALSE}
library(UniProt.ws)
library(dplyr)
proteins <- unique(fData(MSnset_data)$Accessions)[1:10]
columns <- c("ENTRY-NAME", "PROTEIN-NAMES", "GENES")
hs <- UniProt.ws::UniProt.ws(taxId = 9606)
first_ten_anno <- UniProt.ws::select(hs, proteins, columns, "UNIPROTKB") %>%
  as_tibble() %>%
  mutate(GeneSymbol = gsub(" .*", "", GENES)) %>%
  select(Accessions = "UNIPROTKB", 
         Gene = "ENTRY-NAME",
         Description = "PROTEIN-NAMES", 
         GeneSymbol) %>% 
  arrange(Accessions)
head(first_ten_anno)
```

```{r annotationReal, echo=FALSE}
library(dplyr)
proteins <- unique(fData(MSnset_data)$Accessions)[1:10]
filter(human_anno, Accessions%in%proteins) %>%
    as_tibble() %>%
    arrange(Accessions) %>%
    head()
```

The aggregation can be performed by calculating the sum, mean or median of the
raw or normalized peptide intensities. The summarized intensity for a selected
protein could be visualized using `peptideIntensityPlot`. It plots all peptides
intensities for a selected protein along with summarized intensity across all
the samples ([Figure 9](#Figure9)).

<a name="Figure9" />

```{r summarize}
MSnset_Pnorm <- summarizeIntensities(MSnset_norm_gs, 
                                     summarizationFunction = sum, 
                                     annotation = human_anno)
```

```{r pepIntensity, fig.width=6, fig.height=5, fig.cap="Figure 9: Summarized protein intensity"}
peptideIntensityPlot(MSnset_data,
                     combinedIntensities = MSnset_Pnorm,
                     ProteinID = "P03372", 
                     ProteinName = "ESR1")
```

Phosphopeptide data is usually analysed at peptide level instead of protein.
This is achieved by either performing analysis separately at each peptide or
merging identical peptides (having phospho modification) belonging to same
protein into single peptide intensity. The downstream analysis is then carried
out on these merged peptides. The `mergePeptides` function performs this merging
of peptides intensities.

## Regression Analysis

To correct for the potential dependency of immunoprecipitated proteins (in
qPLEX-RIME) on the bait protein, a linear regression method is available in
_qPLEXanalyzer_. The `regressIntensity` function performs a regression analysis
in which bait protein levels is the independent variable (x) and the profile of
each of the other protein is the dependent variable (y). The residuals of the
y=ax+b linear model represent the protein quantification profiles that are not
driven by the amount of the bait protein.

The advantage of this approach is that proteins with strong dependency on the
target protein are subjected to significant correction, whereas proteins with
small dependency on the target protein are slightly corrected. In contrast, if a
standard correction factor were used, it would have the same magnitude of effect
on all proteins. The control samples (such as IgG) should be excluded from the
regression analysis. The `regressIntensity` function also generates the plot
displaying the correlation between bait and other protein before and after
applying this method ([Figure 10](#Figure10)).

The example dataset shown below is from an ER qPLEX-RIME experiment carried out
in MCF7 cells to investigate the dynamics of the ER complex assembly upon
4-hydroxytamoxifen (OHT) treatment at 2h, 6h and 24h or at 24h post-treatment
with the vehicle alone (ethanol). It consists of six biological replicates for
each condition spanned across three TMT experiments along with two IgG mock pull
down samples in each experiment.

<a name="Figure10" />

```{r regress, fig.width=6, fig.height=4, fig.cap="Figure 10: Correlation between bait protein and enriched proteins before and after regression"}
data(exp3_OHT_ESR1)
MSnset_reg <- convertToMSnset(exp3_OHT_ESR1$intensities_qPLEX2,
                              metadata = exp3_OHT_ESR1$metadata_qPLEX2,
                              indExpData = c(7:16), 
                              Sequences = 2, 
                              Accessions = 6)
MSnset_P <- summarizeIntensities(MSnset_reg, 
                                 summarizationFunction = sum, 
                                 annotation = human_anno)
MSnset_P <- rowScaling(MSnset_P, scalingFunction = mean)
IgG_ind <- which(pData(MSnset_P)$SampleGroup == "IgG")
Reg_data <- regressIntensity(MSnset_P, 
                             controlInd = IgG_ind,
                             ProteinId = "P03372")
```

## Differential statistical analysis

A statistical analysis for the identification of differentially regulated or
bound proteins is carried out using
[limma](http://bioconductor.org/packages/limma) [@Ritchie2015]. It uses linear 
models to assess differential expression in the context of multifactor designed
experiments. Firstly, a linear model is fitted for each protein where the model
includes variables for each group and MS run. Then, log2 fold changes between
comparisons are estimated using `computeDiffStats`. Multiple testing correction
of p-values are applied using the Benjamini-Hochberg method to control the false
discovery rate (FDR). Finally, `getContrastResults` is used to get contrast
specific results.

The qPLEX-RIME experiment can consist of IgG mock samples to discriminate
non-specific binding. The controlGroup argument within `getContrastResults`
function allows you to specify this group (such as IgG). It then uses the mean
intensities from the fitted linear model to compute log2 fold change between IgG
and each of the groups. The maximum log2 fold change over IgG control from the
two groups being compared is reported in the _controlLogFoldChange_ column. This
information can be used to filter non-specific binding. A _controlLogFoldChange_
more than 1 can be used as a filter to discover specific interactors.

The results of the differential protein analysis can be visualized using
`maVolPlot` function. It plots average log2 protein intensity to log2 fold
change between groups compared. This enables quick visualization 
([Figure 11](#Figure11)) of significantly abundant proteins between groups.
`maVolPlot` could also be used to view differential protein results in a volcano
plot ([Figure 12](#Figure12)) to compare the size of the fold change to the
statistical significance level.


```{r diffexp}
contrasts <- c(DSG.FA_vs_FA = "DSG.FA - FA")
diffstats <- computeDiffStats(MSnset_Pnorm, contrasts = contrasts)
diffexp <- getContrastResults(diffstats, 
                              contrast = contrasts,
                              controlGroup = "IgG")
```


<a name="Figure11" />

```{r MAplot, fig.width=6,fig.height=4, fig.cap="Figure 11: MA plot of the quantified proteins"}
maVolPlot(diffstats, contrast = contrasts, plotType = "MA", title = contrasts)
```

<a name="Figure12" />

```{r volcano, fig.width=6, fig.height=4, fig.cap="Figure 12: Volcano plot of the quantified proteins"}
maVolPlot(diffstats, contrast = contrasts, plotType = "Volcano", title = contrasts)
```


## Session Information

```{r info,echo=TRUE}
sessionInfo()
```

