Theoretical framework for the difference of two negative binomial distributions and its application in comparative analysis of sequencing data
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Summary
This summary is machine-generated.We introduce DEGage, a new method for detecting differentially expressed genes (DEGs) in single-cell RNA sequencing (scRNA-seq) data. DEGage outperforms existing tools, offering robust and sensitive analysis for high-throughput sequencing applications.
Area Of Science
- Genomics
- Computational Biology
- Statistical Genetics
Background
- High-throughput sequencing (HTS) is crucial for biological research at bulk and single-cell levels.
- Comparative analysis of HTS data often uses the difference of two negative binomial distributions (DOTNB), but theoretical results are limited.
- Existing methods for detecting differentially expressed genes (DEGs) in single-cell RNA sequencing (scRNA-seq) data have limitations.
Purpose Of The Study
- To derive theoretical results for DOTNB and examine its asymptotic properties.
- To introduce DEGage, a novel computational method for DEG detection in scRNA-seq data.
- To validate DEGage's performance against existing DEG analysis tools.
Main Methods
- Derivation of basic analytical results and examination of asymptotic properties for DOTNB.
- Development of DEGage, a computational tool utilizing DOTNB for DEG identification in scRNA-seq data.
- Extensive validation using simulated and real scRNA-seq datasets, comparing DEGage with DEGseq2, DEsingle, edgeR, Monocle3, and scDD.
Main Results
- DEGage demonstrates superior performance compared to five popular DEG analysis tools.
- The method is robust against high dropout rates and shows enhanced sensitivity for both balanced and imbalanced datasets, even with small sample sizes.
- DEGage successfully identified marker genes in prostate cancer and potential memory-related genes in mouse neurons.
Conclusions
- DEGage offers a powerful and reliable approach for DEG analysis in scRNA-seq data.
- The theoretical advancements in DOTNB and the DEGage software have broad applicability for HTS data analysis.
- This work facilitates comparative analyses of dispersed count data and addresses significant research questions in genomics and beyond.
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