---
title: "Workflow"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Workflow}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```
# Introduction

This vignette is a workflow template including import, conversion to a standard format and highlighting inter-software proteinGroup denotation differences with **flowTraceR**.

## Loading R packages

```{r setup, message = FALSE, warning = FALSE}
library(flowTraceR)
library(magrittr)
library(dplyr)
library(tidyr)
library(stringr)
library(tibble)
library(ggplot2)
library(data.table)
library(kableExtra)
```

# Import 

## Import your data
Importing the output files from each software can be easily performed with `data.table::fread()`. 

```{r import, eval=FALSE, include=TRUE}
diann <- data.table::fread("DIRECTORY/dia-nn_file.tsv")
spectronaut <- data.table::fread("DIRECTORY/spectronaut_file.tsv")
mq_evidence <- data.table::fread("DIRECTORY/maxquant_evidence.txt")
mq_proteinGroups <- data.table::fread("DIRECTORY/maxquant_proteinGroups.txt")
pd_psm <- data.table::fread("DIRECTORY/pd_PSMs.txt")
```

## Examples
Some examples are provided to explore the workflow.

```{r get example data}
#DIA-NN
diann <- flowTraceR::get_example("DIA-NN")

#Spectronaut
spectronaut <- flowTraceR::get_example("Spectronaut")

#MaxQuant
mq_evidence <- flowTraceR::get_example("MaxQuant")[["evidence"]]

mq_proteinGroups <- flowTraceR::get_example("MaxQuant")[["proteinGroups"]]

#PD
pd_psm <- flowTraceR::get_example("PD")
```

# Conversion to standardized format

The input data can be converted to a standardized output format on precursor, modified peptide and proteingroup level. The generated columns with **flowTraceR** are appended to the submitted data without any filtering performed. The generated columns are denoted with the prefix *traceR*. Note that only the modifications *UniMod:35 (Oxidation)* and *UniMod:4 (Carbamidomethyl)* are supported by **flowTraceR**. A column with the appendix *unknownMods* is generated to potentially filter modifications which are not supported: if TRUE, an unknown modification is detected.

## Precursor 

For converting the precursor level use `convert_precursor()`.

```{r precursor}
diann_precursor_converted <- convert_precursor(input_df = diann, software = "DIA-NN")
spectronaut_precursor_converted <- convert_precursor(input_df = spectronaut, software = "Spectronaut")
mq_precursor_converted <- convert_precursor(input_df = mq_evidence, software = "MaxQuant")
pd_precursor_converted <- convert_precursor(input_df = pd_psm, software = "PD")
```

## Modified Peptides

For converting the modified peptide level use `convert_modified_peptides()`.

```{r modified peptides}
diann_peptides_converted <- convert_modified_peptides(input_df = diann, software = "DIA-NN")
spectronaut_peptides_converted <- convert_modified_peptides(input_df = spectronaut, software = "Spectronaut")
mq_peptides_converted <- convert_modified_peptides(input_df = mq_evidence, software = "MaxQuant")
pd_peptides_converted <- convert_modified_peptides(input_df = pd_psm, software = "PD")
```

## ProteinGroups

For converting the proteinGroup level use `convert_proteingroups()`.

```{r proteinGroup}
diann_proteinGroups_converted <- convert_proteingroups(input_df = diann, software = "DIA-NN")
spectronaut_proteinGroups_converted <- convert_proteingroups(input_df = spectronaut, software = "Spectronaut")
mq_proteinGroups_converted <- convert_proteingroups(input_df = mq_proteinGroups, software = "MaxQuant")
pd_proteinGroups_converted <- convert_proteingroups(input_df = pd_psm, software = "PD")
```

## All Levels

For converting precursor, modified peptide and proteingroup level at once use `convert_all_levels()`.

```{r all levels}
diann_all_converted <- convert_all_levels(input_df = diann, software = "DIA-NN")
spectronaut_all_converted <- convert_all_levels(input_df = spectronaut, software = "Spectronaut")
mq_all_converted <- convert_all_levels(input_df = mq_evidence, input_MQ_pg = mq_proteinGroups, software = "MaxQuant")
pd_all_converted <- convert_all_levels(input_df = pd_psm, software = "PD")
```

## Analyzing Conversion

Since only the modifications UniMod:35 (Oxidation) and UniMod:4 (Carbamidomethyl) are currently supported, **flowTraceR** provides functions to analyze the conversion and shows how much unknown modifications are present in the dataset with `analyze_unknown_mods()`.  

```{r analyzing conversion}
#For one software example - equivalent for others.

#Proteome Discoverer
#Reports
pd_precursor_report_unknown_mods <- analyze_unknown_mods(input_df = pd_precursor_converted, level = "precursor", plot = FALSE)
pd_peptides_report_unknown_mods <- analyze_unknown_mods(input_df = pd_peptides_converted, level = "modified_peptides", plot = FALSE)

#Plots
pd_precursor_plot_unknown_mods <- analyze_unknown_mods(input_df = pd_precursor_converted, level = "precursor", plot = TRUE, plot_characteristic = "absolute")
pd_peptides_plot_unknown_mods <- analyze_unknown_mods(input_df = pd_peptides_converted, level = "modified_peptides", plot = TRUE, plot_characteristic = "relative")
```

### Example precursor level

```{r}
kableExtra::kable(pd_precursor_report_unknown_mods)
```
<p>&nbsp;</p>
```{r conversion-plot}
pd_precursor_plot_unknown_mods
```
<p>&nbsp;</p>
# Tracing inter-software differences

For binary software comparisons **flowTraceR** allows to trace inter-software differences based on the standardized flowTraceR format. Each identification is classified as *common* - identified in both analyses or as *unique* - specific to one analysis.

## For each individual level

```{r trace individual level}
#Binary Comparison - DIA-NN vs. Spectronaut

#ProteinGroup level
traced_proteinGroups <- trace_level(input_df1 = diann_all_converted , input_df2 = spectronaut_all_converted, analysis_name1 = "DIA-NN", analysis_name2 = "Spectronaut", level = "proteinGroups", filter_unknown_mods = TRUE)

#Peptide level
traced_peptides <- trace_level(input_df1 = diann_all_converted, input_df2 = spectronaut_all_converted, analysis_name1 = "DIA-NN", analysis_name2 = "Spectronaut", level = "modified_peptides", filter_unknown_mods = TRUE)

#Precursor level
traced_precursor <- trace_level(input_df1 = diann_all_converted, input_df2 = spectronaut_all_converted, analysis_name1 = "DIA-NN", analysis_name2 = "Spectronaut", level = "precursor", filter_unknown_mods = TRUE)
```

## All levels 

```{r trace all levels}
#Binary Comparison - DIA-NN vs. Spectronaut

#trace all levels in one step
traced_all <- trace_all_levels(input_df1 = diann_all_converted, input_df2 = spectronaut_all_converted, analysis_name1 = "DIA-NN", analysis_name2 = "Spectronaut", filter_unknown_mods = TRUE)
```

## Connect traced levels
Combine two levels after categorization in unique and common entries. Possible connections are proteinGroup or modified peptide with precursor categorization.

```{r Connect flowTraceR levels}
#ProteinGroup level
DIANN_connected_proteinGroup <- connect_traceR_levels(input_df = traced_all[["DIA-NN"]], level = "proteinGroups")
Spectronaut_connected_proteinGroup <- connect_traceR_levels(input_df = traced_all[["Spectronaut"]], level = "proteinGroups")

#Peptide level
DIANN_connected_peptides <- connect_traceR_levels(input_df = traced_all[["DIA-NN"]], level = "modified_peptides")
Spectronaut_connected_peptides <- connect_traceR_levels(input_df = traced_all[["Spectronaut"]], level = "modified_peptides")
```

## Show software differences for modified peptide/proteinGroup denotations
Generate a report or visualize the output of connecting the flowTraceR levels on *proteinGroup_precursor* or *modified.peptides_precursor* categorization in:

* common_common
* common_unique
* unique_common
* unique_unique


```{r analyze connected levels}
#Example for proteinGroup level

#*Plots*
#upper level - proteinGroup level - how many proteingroups have a specific categorization
DIANN_plot_proteinGroups_upper <- analyze_connected_levels(input_df = DIANN_connected_proteinGroup, connected_levels = "proteinGroup_precursor",count_level = "upper", plot = TRUE, plot_characteristic = "absolute")

Spectronaut_plot_proteinGroups_upper <- analyze_connected_levels(input_df = Spectronaut_connected_proteinGroup, connected_levels = "proteinGroup_precursor", count_level = "upper", plot = TRUE, plot_characteristic = "absolute")

#lower level - precursor level - how many precursor have a specific categorization
DIANN_plot_proteinGroups_lower <- analyze_connected_levels(input_df = DIANN_connected_proteinGroup, connected_levels = "proteinGroup_precursor",count_level = "lower", plot = TRUE, plot_characteristic = "absolute")

Spectronaut_plot_proteinGroups_lower <- analyze_connected_levels(input_df = Spectronaut_connected_proteinGroup, connected_levels = "proteinGroup_precursor", count_level = "lower", plot = TRUE, plot_characteristic = "absolute")


#*Reports*
#ProteinGroup level
DIANN_report_proteinGroups <- analyze_connected_levels(input_df = DIANN_connected_proteinGroup, connected_levels = "proteinGroup_precursor",count_level = "upper", plot = FALSE)

Spectronaut_report_proteinGroups <- analyze_connected_levels(input_df = Spectronaut_connected_proteinGroup, connected_levels = "proteinGroup_precursor",count_level = "lower", plot = FALSE)
```

### Example proteinGroup level

```{r}
kableExtra::kable(DIANN_report_proteinGroups)
```
<p>&nbsp;</p>
```{r proteingroups-connected}
DIANN_plot_proteinGroups_upper
```
<p>&nbsp;</p>

## Get software difference for proteinGroup denotations
Filter for potential common precursor and unique proteinGroup connections. It is possible to trace differences in proteinGroup denotations for common precursor.

```{r get software difference}
#with string_analysis = TRUE - if protein denotation is mentioned in both proteinGroups of input_df1/_df2 are filtered out - only distinct protein denotations remain

Difference_proteinGroup <- trace_unique_common_pg(input_df1 = DIANN_connected_proteinGroup, input_df2 = Spectronaut_connected_proteinGroup, analysis_name1 = "DIA-NN", analysis_name2 = "Spectronaut", string_analysis = FALSE)

Difference_proteinGroup_reduced <- trace_unique_common_pg(input_df1 = DIANN_connected_proteinGroup, input_df2 = Spectronaut_connected_proteinGroup, analysis_name1 = "DIA-NN", analysis_name2 = "Spectronaut", string_analysis = TRUE)
```

### Results:
First, string_analysis = FALSE is used. ProteinGroups, which share similar proteins, stay in the output. For example, for the common precursor *COMMON2* has in Spectronaut a proteinGroup denotation of *EXAMPLE2* and in DIA-NN of *EXAMPLE1;EXAMPLE2*. 
<p>&nbsp;</p>
```{r echo=FALSE}
kableExtra::kable(Difference_proteinGroup, format = "pipe", caption = "Difference in proteinGroup denotation - string_analysis = FALSE")
```
<p>&nbsp;</p>
<p>&nbsp;</p>
Second, string_analysis = TRUE is applied. ProteiGroups, which have similar proteins, are filtered.
<p>&nbsp;</p>
```{r echo=FALSE}
kableExtra::kable(Difference_proteinGroup_reduced, format = "pipe", caption = "Difference in proteinGroup denotation - string_analysis = TRUE")
```

