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OMICfpp: a fuzzy approach for paired RNA-Seq counts
Alberto Berral-Gonzalez1, Angela L Riffo-Campos2, Guillermo Ayala3
1Grupo de Investigación Bioinformática y Genómica Funcional. Laboratorio 19. Centro de Investigación del Cáncer (CiC-IBMCC, Universidad de Salamanca-CSIC, Campus Universitario Miguel de Unamuno s/n, Salamanca, 37007, Spain.
OMICfpp offers accurate differential gene expression analysis for RNA sequencing paired data. This method uses randomized p-values for robust gene selection, improving colorectal cancer research.
Area of Science:
- Genomics
- Bioinformatics
- Statistical Genetics
Background:
- RNA sequencing (RNA-Seq) is crucial for differential expression analysis but faces challenges with measurement accuracy and pipeline variability.
- A key issue in omics data analysis is the small sample size relative to the number of variables, often neglecting experimental design.
- Existing tools inadequately address the complexities of paired experimental designs in RNA-Seq data analysis.
Purpose of the Study:
- To introduce OMICfpp, a novel statistical method for analyzing RNA-Seq data with a paired design.
- To enhance the accuracy of differential expression analysis in RNA-Seq studies with paired samples.
- To propose a robust approach for identifying and validating gene expression signatures.
Main Methods:
- OMICfpp employs a binomial test to generate p-values for each case-control pair.
- P-values are aggregated using an ordered weighted average (OWA) with a user-defined orness.
- A randomization p-value is computed by comparing original aggregated p-values to those from randomized pairs, serving as the raw p-value for differential expression testing.
Main Results:
- OMICfpp was evaluated on public RNA-Seq datasets comprising 68 sample pairs from colorectal cancer patients.
- Results were validated using bibliographic searches and simulated datasets, showing strong concordance.
- Comparison with established methods like edgeR and DESeq2 for paired samples demonstrated OMICfpp's competitive performance.
- The study identified potential new target genes for colorectal cancer gene expression signatures.
Conclusions:
- OMICfpp is presented as an accurate and reliable method for differential expression analysis in RNA-Seq data with paired designs.
- The use of randomized p-value patterns is proposed as a powerful and robust strategy for selecting target genes for experimental validation.
- OMICfpp software is publicly available for broader application in genomic research.
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