index.qmd

---
title: Proteomics Data Analysis for Data-independent acquisition (PDA-DIA) 
code-tools: false
---

This course provides comprehensive hands-on tutorials on how to apply the
`msqrob2` software for the statistical analysis of mass spectrometry
(MS)-based proteomics data. It has a particular focus on data dependent acquisition.  
The course first introduces general concepts of
statistical proteomics data analysis and `msqrob2`. Further
chapters will demonstrate the application of `msqrob2` for assessing
different biological questions starting from datasets with different
experimental designs, acquisition strategies, instruments, and search
engines. The book aims to help proteomics researchers and data analysists tailoring their statistical analysis workflow to their specific datasets and research questions.

```{r, echo = FALSE, out.width = "30%", fig.cap = "The sticker is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/)."}
knitr::include_graphics("figs/msqrob2.png")
```

# Why msqrob2? {-}

MS-based proteomics experiments often imposes a complex correlation
structure among observations. Addressing this correlation is key for
correct statistical inference and reliable biomarker discovery. This
`msqrob2` book provides a set of (mixed) model-based workflows
dedicated to differential abundance analysis for label-free as well
as labeled MS-based proteomics data. The key features of `msqrob2`
workflows are:

1. Modularity: all core functions rely on the `QFeatures` class, a
   standardised data structure, meaning that output of a function can
   be fed as input to any other function. Hence, different functions
   are assembled as modular blocks into a complete data analysis
   workflows that can be easily adapted to the peculiarities of any
   MS-based proteomics data set. Therefore, the approach extends well
   beyond the use case presented in this chapter
2. Flexibility: the `msqrob2` modelling approach relies on the
   `lme4::lmer()` model specification syntax, meaning that any linear
   model can be specified. For fixed effects, this includes modelling
   categorical and numerical variables, as well as their interaction.
   Moreover, `msqrob2` can model both sample-specific and
   feature-specific (e.g. peptide or protein) covariates, which
   unlocks the inference to experiments with arbitrarily complex
   designs as well as to correct explicitly for feature-specific
   properties.
3. Performance: thanks to the inclusion of robust ridge regression, we
   demonstrated improved performance of `msqrob2` workflows upon the
   competing software [@Goeminne2016-tr;@Sticker2020-rl;@Vandenbulcke2025-sj].

# Outline {-}

The course is divided in two parts.

# Data Processing

This parts introduces the user to the key concepts for data processing in differential proteomics data analysis and provides extensive description of the code. While this part is conceptual, the concepts are illustrated using a real spike-in study.

-  [Basic concepts](01-dataprocessing.html)
-  [Tutorial data processing](01-tutorial-data-processing.html)
-  [Wrap-up: Workflow optimisation](02-workflow-optimisation.html), [Wrap-up normalisation](02-normalisation-wrapup.html)
-  [Spectronaut - Raw MS2](01-dataprocessing-spectronaut.html), [Spectronaut - Processed MS2](01-dataprocessing-spectronaut-pn.html), [Spectronaut - Workflow Optimisation](02-workflow-optimisation-spectronaut.html)

# Statistical Inference & Design concepts

- [Basics of Statistical Inference](03-inference.html)
- [Tutorial Differential Analysis and Design Aspects](03-tutorial-Design.html)
- [Wrapup Design1](03-blocking-wrapup.html), [Wrapup Design2](mouse-diet-two-factors.html)

# Targeted audience and assumed background

The course material is targeted to either proteomics practitioners or
data analysts/bioinformaticians that would like to learn how to
analyse proteomics data. 

A working knowledge of R (R syntax, commonly used functions, basic
data structures such as data frames, vectors, matrices, ... and their
manipulation) is required. Familiarity with MS or proteomics in
general is recommended, this would allow for a better understanding of
the modelling assumptions taken throughout this book. Familiarity with
other Bioconductor omics data classes and the tidyverse syntax is
useful.

We **highly recommend** reading the [quantitative proteomics
chapter](https://rformassspectrometry.github.io/book/sec-quant.html)
of the R for mass spectrometry book.

# Setup

To install all the necessary package, please use
**R 4.5 or higher** and execute:

```{r, eval = FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(c(
   "BiocParallel",
   "BiocFileCache",
   "ComplexHeatmap",
   "dplyr",
   "ExploreModelMatrix",
   "ggpattern",
   "ggplot2",
   "ggrepel",
   "impute",
   "MsDataHub",
   "patchwork",
   "scater",
   "tidyr",
   "bookdown", 
   "iq",
   "QFeatures",
   "msqrob2",
   "kableExtra",
   "data.table",
   "ggcorrplot",
   "ggpubr"
))
```

- Useful functions that will be included in the next msqrob2 release. Source the following script in R

```{r, eval = FALSE}
source("https://raw.githubusercontent.com/statOmics/PDA-DIA/master/R/utils.R")
```


## License {-}

<a rel="license"
href="http://creativecommons.org/licenses/by-sa/4.0/"><img
alt="Creative Commons Licence" style="border-width:0"
src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br
/>This material is licensed under a <a rel="license"
href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons
Attribution-ShareAlike 4.0 International License</a>. You are free to
**share** (copy and redistribute the material in any medium or format)
and **adapt** (remix, transform, and build upon the material) for any
purpose, even commercially, as long as you give appropriate credit and
distribute your contributions under the same license as the original.