Pitfalls in analysis and interpretation of single-cell RNA-seq data in cancer

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Single-cell and single-nuclei RNA-sequencing (sc/snRNA-seq) are pivotal in cancer research.
• Numerous computational tools exist for sc/snRNA-seq data analysis.
• Expertise is crucial for accurate sc/snRNA-seq data interpretation; inadequate methods can yield unreliable findings.

Purpose of the Study:

• To identify common pitfalls in cancer sc/snRNA-seq data analysis.
• To provide guidance on overcoming these analytical challenges.
• To promote robust analysis and careful interpretation of sc/snRNA-seq datasets in oncology.

Main Methods:

• Review of common computational methods and their limitations in sc/snRNA-seq analysis.
• Discussion of potential errors in statistical analysis, chromosomal aberration inference, trajectory analysis, and signature-based bulk RNA-seq analysis.
• Identification of strategies to mitigate analytical errors and improve data interpretation.

Main Results:

• Common errors in statistical analysis can lead to misleading conclusions.
• Inaccurate inference of chromosomal aberrations may obscure tumor heterogeneity.
• Trajectory and signature-based analyses require careful validation to avoid spurious findings.

Conclusions:

• Recognizing the limitations of sc/snRNA-seq data and analytical methods is essential.
• Implementing robust analytical strategies can prevent common pitfalls.
• This review aims to enhance the informative value and reliability of cancer sc/snRNA-seq studies.