Preprocessing.Rmd

---
title: "Data preprocessing"
date: "`r format(Sys.time(), '%Y - %m - %d')`"
output: 
  html_document:
    toc: true
    toc_float:
      toc_collapsed: false
vignette: >
  %\VignetteEngine{knitr::knitr}
  %\VignetteIndexEntry{Data preprocessing}
  %\usepackage[UTF-8]{inputenc}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(InteRact)
data("proteinGroups_Cbl")
```

## Input data format

The input file can be of many types as long as it can be converted to a `data.frame` containing a number of columns with protein intensities and one column with protein identifiers. A protein identifier correspond to a unique entry in the UniProt database. Multiple protein identifiers can be associated with a single protein group.

## Preprocessing data

By default `InteRact()` performs several preprocessing steps using the function `preprocess_data()`. Data preprocessing consists of:

* Selecting intensity columns matching `condition$columns` (see [Metadata](Metadata.html))
* Discarding experimental conditions, biological or technical replicates flagged by parameters `filter_time`, `filter_bio` or `filter_tech`
* Discarding columns that could not be converted to numeric
* (optionnal) Filtering protein groups with a sub-threshold score (in column `Column_score`) or with no gene name associated (in column `Column_gene_name`)
* Merging protein groups with the same gene name
* Discard proteins with NA values for all conditions
* Normalize on median intensity across conditions
* Averaging protein intensities over technical replicates

```{r message=FALSE, warning=FALSE}

preprocessed_data <- preprocess_data(proteinGroups_Cbl,
                            Column_gene_name = "Gene.names",
                            Column_score = "Score",
                            Column_ID = "Protein.IDs",
                            Column_Npep = NULL,
                            Column_intensity_pattern = "^Intensity.",
                            bait_gene_name = "Cbl",
                            condition = NULL,
                            bckg_bait = "Cbl",
                            bckg_ctrl = "WT")
```
Note that if the data.frame `condition` is not specified, conditions will be mapped to intensity columns automatically using `identify_conditions()` (see [Metadata](Metadata.html)).

## Quality Check

Following preprocessing, data quality can be as assessed using several functions. 
You can assess the distribution of missing values, the correlations in protein intensities between samples, the quality of affinity purification using `plot_QC()`:

```{r message=FALSE, warning=FALSE}
library(gridExtra)
grobs <- plot_QC(preprocessed_data)$plot
gridExtra::grid.arrange(grobs = grobs, nrow = 1)
```