Normalization of gene counts affects principal components-based exploratory analysis of RNA-sequencing data

Henk J. van Lingen, Maria Suarez-Diez, Edoardo Saccenti*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Normalization of gene expression count data is an essential step of in the analysis of RNA-sequencing data. Its statistical analysis has been mostly addressed in the context of differential expression analysis, that is in the univariate setting. However, relationships among genes and samples are better explored and quantified using multivariate exploratory data analysis tools like Principal Component Analysis (PCA). In this study we investigate how normalization impacts the PCA model and its interpretation, considering twelve different widely used normalization methods that were applied on simulated and experimental data. Correlation patterns in the normalized data were explored using both summary statistics and Covariance Simultaneous Component Analysis. The impact of normalization on the PCA solution was assessed by exploring the model complexity, the quality of sample clustering in the low-dimensional PCA space and gene ranking in the model fit to normalized data. PCA models upon normalization were interpreted in the context gene enrichment pathway analysis. We found that although PCA score plots are often similar independently form the normalization used, biological interpretation of the models can depend heavily on the normalization method applied.

Original languageEnglish
Article number195058
JournalBiochimica et Biophysica Acta - Gene Regulatory Mechanisms
Volume1867
Issue number4
DOIs
Publication statusPublished - Dec 2024

Keywords

  • Clustering
  • Correlation
  • Gene enrichment analysis
  • KEGG pathways
  • Pathway analysis
  • Silhouette widths

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