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Bayesian-frequentist hybrid inference framework for single cell RNA-seq analyses
Gang Han1, Dongyan Yan2, Zhe Sun2
1Epidemiology & Biostatistics, 212 Adriance Lab Rd, 1266 TAMU College Station, TX 77843.
Research Square
|October 27, 2023
Summary
This study introduces a Bayesian-frequentist hybrid (BFH) framework to enhance gene discovery in single-cell RNA sequencing (scRNA-seq) data. The BFH approach improves power for identifying key genes in complex diseases like idiopathic pulmonary fibrosis.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Single-cell RNA sequencing (scRNA-seq) is vital for understanding cell-specific disease mechanisms.
- Identifying key genes in scRNA-seq data is challenging.
- Pseudo-bulk methods, while common, may lack statistical power due to small sample sizes and high costs associated with scRNA-seq.
Conclusions:
- The Bayesian-frequentist hybrid (BFH) framework offers a powerful and flexible approach for scRNA-seq data analysis.
- BFH improves the identification of biologically relevant genes, aiding in disease mechanism research.
- This method provides a valuable tool for future explorations of single-cell transcriptomics.
