cypress: an R/Bioconductor package for cell-type-specific differential expression analysis power assessment.

Area of Science:

• Computational biology
• Genomics
• Bioinformatics

Background:

• Advances in computational signal deconvolution allow bulk transcriptome analysis at a finer cell-type level.
• Identifying cell-type-specific differentially expressed (csDE) genes is crucial for clinical applications but faces practical challenges in experimental design.
• Existing methods lack dedicated tools for experimental design and statistical power analysis in csDE gene detection.

Purpose of the Study:

• To introduce cypress, the first tool for experimental design and statistical power analysis specifically for csDE gene identification.
• To provide researchers with a high-fidelity simulator for bulk RNA sequencing (RNA-seq) convolution and deconvolution processes.
• To aid in optimizing experimental design and conducting power analyses for csDE gene studies.

Main Methods:

• cypress models purified cell-type-specific (CTS) profiles, cell-type compositions, and biological/technical variations.
• It functions as a simulator for bulk RNA-seq convolution and deconvolution.
• The tool evaluates the impact of various influencing factors using statistical metrics.

Main Results:

• cypress enables reliable modeling of complex biological and technical variations in transcriptome data.
• It provides a robust simulation environment for assessing experimental designs.
• The tool facilitates the evaluation of statistical power for detecting csDE genes under different conditions.

Conclusions:

• cypress addresses critical needs in experimental design for csDE gene identification.
• It empowers researchers to optimize their studies and improve the reliability of findings.
• This tool enhances the practical application of deconvolution methods in clinical genomics.