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# PRONE - The PROteomics Normalization Evaluator <img src="man/figures/PRONE_package_logo.png" align="right" alt="" width="150" />

R Package for preprocessing, normalizing, and analyzing proteomics data

## Introduction

High-throughput omics data are often affected by systematic biases
introduced throughout all the steps of a clinical study, from sample
collection to quantification. Failure to account for these biases can
lead to erroneous results and misleading conclusions in downstream
analysis. Normalization methods aim to adjust for these biases to make
the actual biological signal more prominent. However, selecting an
appropriate normalization method is challenging due to the wide range of
available approaches. Therefore, a comparative evaluation of
unnormalized and normalized data is essential in identifying an
appropriate normalization strategy for a specific data set. This R
package provides different functions for preprocessing, normalizing, and
evaluating different normalization approaches. Furthermore,
normalization methods can be evaluated on downstream steps, such as
differential expression analysis and statistical enrichment analysis.
Spike-in data sets with known ground truth and real-world data sets of
biological experiments acquired by either tandem mass tag (TMT) or
label-free quantification (LFQ) can be analyzed.

## Installation

To install the package, run:

``` r
  # Official BioC installation instructions
  if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

  BiocManager::install("PRONE")
  
  # Load and attach PRONE 
  library("PRONE")
```

If you have troubles downloading PRONE from Bioconductor, you still have
the option to install PRONE from GitHub. However, the Bioconductor
download is recommended!

``` r
  # Install PRONE.R from github and build vignettes
  if (!requireNamespace("devtools", quietly = TRUE)){
    install.packages("devtools")
  } 
  devtools::install_github("daisybio/PRONE", build_vignettes = TRUE, 
                           dependencies = TRUE)
  
  # Load and attach PRONE 
  library("PRONE")
```

## Workflow

A six-step workflow was developed in R version 4.4 to evaluate the
effectiveness of the previously defined normalization methods on
proteomics data. The workflow incorporates a set of novel functions and
also integrates various methods adopted by state-of-the-art tools.

<img src="man/figures/Workflow_PRONE.png" width="700"/>

Following the upload of the proteomics data into a SummarizedExperiment
object, proteins with too many missing values can be removed, outlier
samples identified, and normalization carried out. Furthermore, an
exploratory analysis of the performance of normalization methods can be
conducted. Finally, differential expression analysis can be executed to
further evaluate the effectiveness of normalization methods. For data
sets with known ground truth, such as spike-in and simulated data sets,
performance metrics, such as true positives (TPs), false positives
(FPs), and area under the curve (AUC) values, can be computed. The
evaluation of DE results of real-world experiments is based on visual
quality inspection, for instance, using volcano plots, and an
intersection analysis of the DE proteins of different normalization
methods is available.

## Usage

To get familiar with the functionalities of the R package, check out the
article [Getting started with
PRONE](https://daisybio.github.io/PRONE/articles/PRONE.html).

## Citation

The preprint is available on BioRxiv: [https://doi.org/10.1101/2025.01.27.634993](https://doi.org/10.1101/2025.01.27.634993).
