---
title: "MSstatsLiP Workflow: An example workflow and analysis of the MSstatsLiP package"
author: "Devon Kohler (<kohler.d@northeastern.edu>)"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{MSstatsLiP Workflow: An example workflow and analysis of the MSstatsLiP package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width=8, 
  fig.height=8
)
```

## MSstatsLiP Workflow Vignette

This Vignette provides an example workflow for how to use the package 
MSstatsLiP.

**NOTE** This vignette uses a small portion of a bigger dataset, which may cause some plots to look different than they would with the full data.

## Installation

To install this package, start R (version "4.1") and enter:

``` {r, eval = FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("MSstatsLiP")
```

```{r,include=TRUE,results="hide",message=FALSE,warning=FALSE}
library(MSstatsLiP)
library(tidyverse)
library(data.table)
```

## Workflow

### 1. Preprocessing

### 1.1 Raw Data Format

The first step is to load in the raw dataset for both the PTM and Protein 
datasets. This data can then be converted into MSstatsLiP format with one of the
built in converters, or manually converted. In this case we use the converter 
for Spectronaut.

``` {r}
## Read in raw data files
head(LiPRawData)
head(TrPRawData)
```

### 1.2 Converter

``` {r}
## Run converter
MSstatsLiP_data <- SpectronauttoMSstatsLiPFormat(LiPRawData, 
                                      "../inst/extdata/ExampleFastaFile.fasta", 
                                      TrPRawData, use_log_file = FALSE, 
                                      append = FALSE)

head(MSstatsLiP_data[["LiP"]])
head(MSstatsLiP_data[["TrP"]])

## Make conditions match
MSstatsLiP_data[["LiP"]][MSstatsLiP_data[["LiP"]]$Condition == "Control", 
                         "Condition"] = "Ctrl"
MSstatsLiP_data[["TrP"]]$Condition = substr(MSstatsLiP_data[["TrP"]]$Condition, 
  1, nchar(MSstatsLiP_data[["TrP"]]$Condition)-1)

```

In the case above the SpectronauttoMSstatsLiPFormat was ran to convert the data 
into the format required for MSstatsLiP. Note that the condition names 
did not match between the LiP and TrP datasets. Here we edit the conditions in 
each dataset to match.

Additionally, it is important that the column "FULL_PEPTIDE" in the LiP dataset 
contains both the Protein and Peptide information, seperated by an underscore. 
This allows us to summarize up to the peptide level, while keeping important 
information about which protein the peptide corresponds to.


### 2. Summarization

The next step in the MSstatsLiP workflow is to summarize feature intensities per
run into one value using the dataSummarizationLiP function. This function takes 
as input the formatted data from the converter.
 
#### 2.1 Summarization Function

``` {r}
MSstatsLiP_Summarized <- dataSummarizationLiP(MSstatsLiP_data,
                                              MBimpute = FALSE, 
                                              use_log_file = FALSE, 
                                              append = FALSE)
names(MSstatsLiP_Summarized[["LiP"]])

lip_protein_data <- MSstatsLiP_Summarized[["LiP"]]$ProteinLevelData
trp_protein_data <- MSstatsLiP_Summarized[["TrP"]]$ProteinLevelData

head(lip_protein_data)
head(trp_protein_data)

```

Again the summarization function returns a list of two dataframes one each for 
LiP and TrP. Each LiP and TrP is also a list with additional summary 
information. This summarized data can be used as input into some of the
plotting functions included in the package.

#### 2.2 Tryptic barplot

MSstatsLiP has a wide variety of plotting functionality to analysis and assess 
the results of experiments. Here we plot the number of half vs fully tryptic 
peptides per replicate.

``` {r}
trypticHistogramLiP(MSstatsLiP_Summarized, 
                    "../inst/extdata/ExampleFastaFile.fasta",
                    color_scale = "bright",
                    address = FALSE)
```

#### 2.3 Run Correlation Plot

MSstatsLiP also provides a function to plot run correlation.

``` {r}
correlationPlotLiP(MSstatsLiP_Summarized, address = FALSE)
```

#### 2.4 Coefficient of Variation

Here we provide a simple script to examine the coefficient of variance between 
conditions

``` {r}
MSstatsLiP_Summarized[["LiP"]]$FeatureLevelData %>% 
  group_by(FEATURE, GROUP) %>% 
  summarize(cv = sd(INTENSITY) / mean(INTENSITY)) %>% 
  ggplot() + geom_violin(aes(x = GROUP, y = cv, fill = GROUP)) + 
  labs(title = "Coefficient of Variation between Condtions", 
       y = "Coefficient of Variation", x = "Conditon")
```

#### 2.5 QCPlot

The following plots are used to view the summarized data and check for 
potential systemic issues.

``` {r}
## Quality Control Plot
dataProcessPlotsLiP(MSstatsLiP_Summarized,
                    type = 'QCPLOT',
                    which.Peptide = "allonly",
                    address = FALSE)
```

#### 2.6 Profile Plot

``` {r}

dataProcessPlotsLiP(MSstatsLiP_Summarized,
                    type = 'ProfilePlot',
                    which.Peptide = c("P14164_ILQNDLK"),
                    address = FALSE)
```

#### 2.7 PCA Plot

In addition, Priciple Component Analysis can also be done on the summarized 
dataset. Three different PCA plots can be created one each for: Percent of 
explained variance per component, PC1 vs PC2 for peptides, and PC1 vs PC2 for 
conditions.

``` {r}

PCAPlotLiP(MSstatsLiP_Summarized,
           bar.plot = FALSE,
           protein.pca = FALSE,
           comparison.pca = TRUE,
           which.comparison = c("Ctrl", "Osmo"),
           address=FALSE)

PCAPlotLiP(MSstatsLiP_Summarized,
           bar.plot = FALSE,
           protein.pca = TRUE,
           comparison.pca = FALSE,
           which.pep = c("P14164_ILQNDLK", "P17891_ALQLINQDDADIIGGRDR"),
           address=FALSE)

```

#### 2.8 Calculate Trypticity

Finally, the trypticity of a peptide can also be calculated and added to any 
dataframe with the ProteinName and PeptideSequence column.

``` {r}

feature_data <- data.table::copy(MSstatsLiP_Summarized$LiP$FeatureLevelData)
data.table::setnames(feature_data, c("PEPTIDE", "PROTEIN"), 
                     c("PeptideSequence", "ProteinName"))
feature_data$PeptideSequence <- substr(feature_data$PeptideSequence, 1, 
                                       nchar(as.character(
                                         feature_data$PeptideSequence)) - 2)

calculateTrypticity(feature_data, "../inst/extdata/ExampleFastaFile.fasta")


MSstatsLiP_Summarized$LiP$FeatureLevelData%>%
  rename(PeptideSequence=PEPTIDE, ProteinName=PROTEIN)%>%
  mutate(PeptideSequence=substr(PeptideSequence, 1, 
                                nchar(as.character(PeptideSequence))-2)
         ) %>% calculateTrypticity("../inst/extdata/ExampleFastaFile.fasta")

  
```

### 3. Modeling

The modeling function groupComparisonLiP takes as input the output of the 
summarization function dataSummarizationLiP.

#### 3.1 Function

```{r}

MSstatsLiP_model <- groupComparisonLiP(MSstatsLiP_Summarized,
                               fasta = "../inst/extdata/ExampleFastaFile.fasta",
                               use_log_file = FALSE, 
                               append = FALSE)

lip_model <- MSstatsLiP_model[["LiP.Model"]]
trp_model <- MSstatsLiP_model[["TrP.Model"]]
adj_lip_model <- MSstatsLiP_model[["Adjusted.LiP.Model"]]

head(lip_model)
head(trp_model)
head(adj_lip_model)

## Number of significant adjusted lip peptides
adj_lip_model %>% filter(adj.pvalue < .05) %>% nrow()

```

The groupComparisonLiP function outputs a list with three separate models. These
models are as follows: LiP model, TrP model, and adjusted LiP model.

#### 3.2 Volcano Plot

``` {r}
groupComparisonPlotsLiP(MSstatsLiP_model, 
                        type = "VolcanoPlot", 
                        address = FALSE)
```

#### 3.3 Heatmap

``` {r}
groupComparisonPlotsLiP(MSstatsLiP_model,
                        type = "HEATMAP",
                        numProtein=50,
                        address = FALSE)
```

#### 3.4 Barcode

Here we show a barcode plot, showing the coverage of
LiP and TrP peptides. This function requires the data in MSstatsLiP format and 
the path to a fasta file.

```{r}
StructuralBarcodePlotLiP(MSstatsLiP_model, 
                         "../inst/extdata/ExampleFastaFile.fasta", 
                         model_type = "Adjusted", which.prot = c("P53858"),
                         address = FALSE)

```

#### 3.5 Calculate proteolytic resistance ratios

Proteolytic resistance ratios are calculated as the ratio of the intensity of fully tryptic peptides in the LiP condition to the TrP condition. In general, a low protease resistance value is indicative of high extent of cleavage, while high protease resistance values indicate low cleavage extent.

```{r calculate accessibility, message=FALSE, warning=FALSE, echo=TRUE,include=TRUE}

Accessibility = calculateProteolyticResistance(MSstatsLiP_Summarized, 
                                               "../inst/extdata/ExampleFastaFile.fasta", 
                                               differential_analysis = TRUE)

Accessibility$RunLevelData

```

```{r Barplot of protease resistance of aSynuclein (monomer - M and fibril - F), message=FALSE, warning=FALSE, fig.height=2, fig.width=10,echo=TRUE,include=TRUE}

ResistanceBarcodePlotLiP(Accessibility,
                         "../inst/extdata/ExampleFastaFile.fasta",
                         which.prot = "P14164",
                         which.condition = "Osmo",
                         address = FALSE)

ResistanceBarcodePlotLiP(Accessibility,
                         "../inst/extdata/ExampleFastaFile.fasta",
                         differential_analysis = TRUE,
                         which.prot = "P53858",
                         which.condition = "Osmo",
                         address = FALSE)

```
