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.
# Official BioC installation instructions
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("PRONE")# Load and attach PRONE
library(PRONE)A six-step workflow was developed in R version 4.2.2 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.