Comparison of transformations for single-cell RNA-seq data.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Single-cell RNA-sequencing (scRNA-seq) data analysis relies on count tables (genes × cells).
• Preprocessing involves adjusting for variable sampling efficiency and transforming data to stabilize variance.
• These steps aim to improve the applicability of standard statistical methods to scRNA-seq data.

Purpose of the Study:

• To evaluate four distinct data transformation approaches for scRNA-seq count tables.
• To compare the strengths, weaknesses, and performance of these methods using benchmarks.

Main Methods:

• Four transformation methods were investigated: delta method, model residuals, inferred latent expression state, and factor analysis.
• Performance was benchmarked using both simulated and real-world scRNA-seq datasets.
• A simple logarithm with pseudo-count followed by principal-component analysis (PCA) was included as a baseline.

Main Results:

• While three advanced methods showed theoretical appeal, they did not consistently outperform simpler approaches.
• The straightforward logarithm with pseudo-count and PCA demonstrated comparable or superior performance in benchmarks.
• This suggests limitations in current theoretical frameworks for assessing scRNA-seq data transformation efficacy.

Conclusions:

• A simple log-transform plus PCA is a robust and effective preprocessing strategy for scRNA-seq data.
• Empirical performance benchmarks are crucial for validating theoretical assumptions in bioinformatics.
• Further research may be needed to reconcile theoretical properties with practical performance in scRNA-seq analysis.