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BALLI: Bartlett-adjusted likelihood-based linear model approach for identifying differentially expressed genes with RNA-seq data
- Source :
- BMC Genomics, BMC Genomics, Vol 20, Iss 1, Pp 1-14 (2019)
- Publication Year :
- 2019
- Publisher :
- BioMed Central, 2019.
-
Abstract
- Background Transcriptomic profiles can improve our understanding of the phenotypic molecular basis of biological research, and many statistical methods have been proposed to identify differentially expressed genes (DEGs) under two or more conditions with RNA-seq data. However, statistical analyses with RNA-seq data are often limited by small sample sizes, and global variance estimates of RNA expression levels have been utilized as prior distributions for gene-specific variance estimates, making it difficult to generalize the methods to more complicated settings. We herein proposed a Bartlett-Adjusted Likelihood-based LInear mixed model approach (BALLI) to analyze more complicated RNA-seq data. The proposed method estimates the technical and biological variances with a linear mixed-effects model, with and without adjusting small sample bias using Bartlkett’s corrections. Results We conducted extensive simulations to compare the performance of BALLI with those of existing approaches (edgeR, DESeq2, and voom). Results from the simulation studies showed that BALLI correctly controlled the type-1 error rates at various nominal significance levels and produced better statistical power and precision estimates than those of other competing methods in various scenarios. Furthermore, BALLI was robust to variation of library size. It was also successfully applied to Holstein milk yield data, illustrating its practical value. Conclusions; BALLI is statistically more efficient and valid than existing methods, and we conclude that it is useful for identifying DEGs in RNA-seq analysis. Electronic supplementary material The online version of this article (10.1186/s12864-019-5851-6) contains supplementary material, which is available to authorized users.
- Subjects :
- 0106 biological sciences
Mixed model
Linear mixed model
lcsh:QH426-470
lcsh:Biotechnology
Value (computer science)
RNA-Seq
Biology
01 natural sciences
Statistical power
03 medical and health sciences
Random Allocation
lcsh:TP248.13-248.65
Statistics
Genetics
Animals
030304 developmental biology
0303 health sciences
Likelihood Functions
Basis (linear algebra)
Models, Genetic
Sequence Analysis, RNA
Methodology Article
Bartlett’s correction
Gene Expression Profiling
Linear model
Computational Biology
RNA sequencing
Variance (accounting)
lcsh:Genetics
Differentially expressed genes
Milk
Sample Size
Linear Models
Cattle
Female
Transcriptome
Software
010606 plant biology & botany
Biotechnology
Subjects
Details
- Language :
- English
- ISSN :
- 14712164
- Volume :
- 20
- Database :
- OpenAIRE
- Journal :
- BMC Genomics
- Accession number :
- edsair.doi.dedup.....a9765fe8524d1e19970f878ce8689f11