Proteomics Normalization: Methods and How to Choose the Best One for Your Data
A guide to proteomics normalization and methods involved to get the most out of your analysis
Published on January 16, 2025
Written by Antonino Zito
⏱ 9 min read
Introduction
Normalization in proteomics data analysis is the process of adjusting raw data to reduce technical or systematic variations, allowing for more accurate biological comparisons.
As any experimental data assay in biomedical research, proteomics experiments, which measure protein abundances across samples, also suffer from unwanted effects and biases often associated with sample preparation, instrument variability, or experimental batches. These variations can potentially mask true biological differences between samples, hindering reliable statistics and thus leading to inaccurate conclusions.
In this blog post, we’ll explore the key differences between RNA-Seq and proteomics data, with a particular focus on normalization techniques for proteomics and the methods available.
Key takeaways
- Proteomics and RNA-Seq methods differ significantly in target features, coverage, and sensitivity.
- 3 widely used normalization methods in proteomics are:
- Total Intensity Normalization: Suitable for variations in sample loading or protein content.
- Median Normalization: Robust for datasets with consistent median protein abundances.
- Reference Normalization: Best with stable reference proteins or spiked-in standards.
- The choice of method depends on experimental design, dataset, and research questions.
- Comparing multiple methods reduces false positives and negatives.
- Omics Playground lets you choose from three normalization options and key proteomics tools like imputation and batch correction, handling computations so you can focus on results.
How does proteomics differ from RNA-Seq?
Proteomics and RNA-Seq are two widely employed, yet fundamentally different, analytical approaches in biological research. These methods differ significantly in target features, coverage, and sensitivity. Each assay offers unique insights into cellular processes.
RNA-Seq primarily measures RNA transcripts, providing a snapshot of transcriptional levels. In contrast, proteomics detects and quantifies proteins, offering more direct insights into functional molecular targets for therapeutics. The protein-level analysis is highly valuable for translational research as proteins are the ultimate effectors of cellular functions.
Proteomics measurements incorporate data of post-translation modifications (PTMs), which are not typically detectable by transcriptomic assays. PTMs often play crucial regulatory functions. Intriguingly, mRNAs and proteins may exhibit substantially different half-lives. There are proteins that might perdure longer in the cell than their mRNAs, and thus they can provide a relatively more stable representation of cellular state.
Importantly, it is well known that proteomics profiles may not necessarily correlate with mRNA levels. This discordance can be attributed to several factors. For example, mRNAs and proteins may be subject to different turnovers, with transcriptional and translational processes occurring at varying rates. Moreover, PTMs and the inherently high variability in proteomic profiles among cells, can both contribute to discrepancy between mRNA and protein levels. For lowly abundant proteins, correlation with mRNAs might even be further reduced. Importantly, inferring proteins relies on predetermined references. Unfortunately, this often hinders identification of novel features.
RNA-seq is able to detect low abundance transcripts and novel isoforms. In contrast, proteomic assays typically exhibit lower sensitivity, facing challenges in accurately capturing lowly abundant features. The presence of missing values in proteomics data has been ascribed to the low sensitivity of proteomic assays. A reason underlying undetected proteins is lack of amplification: unlike nucleic acids (DNA, RNA), proteins cannot be amplified prior to quantification.
Another key difference between RNA-seq and proteomics relates to dynamic range. For instance, RNAseq CPM values show a typical Poisson or NB distribution which may fall within the 0-15 (log2 scale) range, with inflated zeros. In contrast, the amount of proteins in a cell can span several orders of magnitude, making it challenging for proteomic assays to accurately measure both low- and high-abundance features in parallel. Compared to RNA-seq, proteomic intensities may populate a much higher range, e.g., >20 (log2 scale). We have discussed this in a previous tech blog about imputation of missing values in proteomics.
Normalization of proteomics data
The fundamental principles of normalization in proteomics are analogous to those employed for RNA-seq normalization, despite the differences in molecular species being studied.
In both datatypes, normalization aims to minimize technical influences on the data that may mask real biological signals. It ensures reliable comparisons of biological signals between phenotypes of interest. Upon normalization, observed differences in protein levels are more likely to reflect genuine biological differences.
Similarly to RNA-seq data, the choice of normalization method for proteomics data should depend on the experimental design, the dataset, and the question investigated. Regardless, it is certainly a good practice evaluating distinct methods. Comparing results from distinct methods reduces the chance of false positives and false negatives results.
The Omics Playground platform incorporates three normalization methods for proteomics: max sum normalization, max median normalization, and reference normalization. The principles of these methods are well established in bioinformatics and valid for multiple data types. They are rooted in bioinformatic pipelines and were carefully selected based on extensive collaborations with academic and pharmaceutical partners specializing in proteomics data analyses.
Normalization methods for proteomics data
Since technologies for proteomics profiling have been designed, the development of analytical methods for analyzing proteomics data continues.
Commonly adopted normalization methods include quantile normalization, normalization of total intensities, median-based normalization, normalizations based on linear regression or local regression, and normalization involving variance stabilization approaches.
Many of these methods and their variants were designed to normalize microarray data, with some also being used for RNA-seq data.
In this section, we focus on three types of normalization approaches for proteomics data. These are also available in the Omics Playground platform for proteomics users to address their data normalization needs.
1. Total intensity normalization
Normalization by total intensity is commonly adopted. It is based on the assumption that total protein amount is similar across samples. Typically, this approach involves scaling the intensity values within each sample by a factor to equalize the total intensity across all samples.
In Omics Playground, we perform total intensity-based normalization of the proteomic data using our own “MaxSum” normalization method. It aims to normalize the samples by the maximum value of total intensity. Specifically, it first calculates the total intensity in each sample. The maximum total intensity value is identified. Each data point in each sample is then divided by the sample’s total intensity and multiplied by the maximum total intensity value. In OPG, we then perform log2 transformation.
2. Median normalization
Normalization by median intensity is also based on the assumption that protein amount is similar across samples. Typically, this approach involves scaling the intensity values within each sample based on the median intensity across all samples.
In Omics Playground, we perform median-based normalization of the proteomic intensities using our own “MaxMedian” normalization method. It aims to normalize the samples by the maximum median value. Specifically, it first calculates the median value in each sample. The maximum median value is identified. Each data point in each sample is then divided by the sample’s median value and multiplied by the maximum median value. We then perform log2 transformation.
3. Reference normalization (reference)
Reference normalization requires an user-selected control feature. The control feature is used for internal standardization of the data. This approach aims to normalize the data to the control feature. It divides each data point in each sample by the value of the reference features in that sample. In Omics Playground, we then perform log2 transformation.
Selecting the right normalization method for your proteomics data
The choice of normalization method for proteomics datasets should depend on the specific characteristics of your data and experimental setup.
Despite working in different ways, all methods ultimately aim to make samples more comparable and highlight biologically meaningful signals.
Total Intensity normalization is well-suited when there are noticeable variations in sample loading or total protein content across samples, as it assumes that most proteins remain unchanged and scales the dataset accordingly to ensure consistent total intensity across samples.
On the other hand, median-based normalization is a robust choice when you expect a consistent median distribution of protein abundances across samples. It may be particularly useful in scenarios when it is assumed that the median intensity of a protein accurately represents the central tendency of that protein’s abundance, with relatively minor systematic biases.
Meanwhile, reference normalization is the preferred approach if you have access to stable reference proteins or spiked-in standards, as it offers a highly precise way to account for technical variability in experiments where known controls are present.
Each method has its strengths and is best applied when aligned with the specific needs of your experiment.
Analyze your proteomics data with Omics Playground
Omics Playground is a cloud-based software developed by BigOmics Analytics for the interactive exploration and analysis of omics data, including proteomics, RNA-Seq, and metabolomics. The platform empowers users to analyze and visualize their data through more than 18 robust analysis modules. These include standard features like differential expression analysis, enrichment analysis, and clustering, as well as advanced tools such as biomarker discovery and drug connectivity analysis.
The Omics Playground provides powerful, both basic and advanced methods to analyze proteomics data.
When you upload your data to Omics Playground, the platform enables you to select your preferred normalization method for proteomics data. As described above, supported normalization methods include maxMedian, maxSum, and reference normalization. By default, the platform applies maxMedian normalization. However, this setting is customizable during the QC/BC step of the data upload process, where you can choose the method of your choice.
Importantly, multiple, distinct options suited for proteomics data are available during the upload process. For example, you can impute missing data values or skip imputation, which is important as proteomics data typically have missing values. You can also treat zero values as missing values. These options are particularly useful to research scientists dedicated to drug discovery, as well as researchers interested in data modeling.
Last but not least, the platform is also fully equipped to perform batch correction.
For more details, please refer to our comprehensive guide on uploading data to Omics Playground.
You are welcome to get in touch with us – we can organize a dedicated, interactive workshop for you and your team to show how you can analyze your proteomics data in the Omics Playground!
Access more than 18 analysis modules and explore your proteomics data interactively
About the Author
Antonino is a senior bioinformatics engineer at BigOmics with a strong background in bioinformatics and biostatistics. With a PhD in genetics and bioinformatics and an MSc in biotechnology, he has made significant contributions to computational analysis in numerous projects during his previous research at Harvard Medical School and King’s College London.