%0 Journal Article
%T Statistical Analysis of Gene Expression Data Using Bayesian Inference Methods in Biotechnology Research
%A Vladimiro Ibañez Quispe
%A Angel Javier Quispe Carita
%A Fidel Ernesto Ticona Yanqui
%A Renan Abelardo Palli Mamani
%A Godofredo Quispe Mamani
%A Percy Huata Panca
%A Juan Carlos Juarez Vargas
%J Journal of Biochemical Technology
%@ 0974-2328
%D 2026
%V 17
%N 1
%R 10.51847/eLzHwgTGtu
%P 147-156
%X Bayesian inference offers a principled way to quantify uncertainty in gene expression studies, especially when biotechnology experiments have few biological replicates and noisy measurements. In practice, RNA-seq data often contain uneven library sizes, donor effects, low-count genes, outlier samples, and uncertain dispersion estimates. These problems are not minor technical details because they directly affect differential-expression calls. A realistic Bayesian analysis must therefore report both what the model estimates and where the model struggles. Many demonstrations of Bayesian transcriptomic methods are cleaner than the data encountered in small biotechnology experiments. Simulations are useful for understanding operating characteristics, but they cannot fully reproduce imperfect sample preparation, donor heterogeneity, hidden technical variation, or weakly expressed genes. Real datasets also force uncomfortable decisions about filtering, normalization, convergence failure, and prior sensitivity. These issues are often underreported even though they strongly influence downstream biological interpretation. This manuscript applies Bayesian hierarchical modeling to a real public RNA-seq dataset, GEO accession GSE52778, rather than to simulated data. The dataset contains human airway smooth muscle cells measured under untreated and dexamethasone-treated conditions, with four paired donor-derived cell lines and therefore only four replicates per condition. The goal is not to prove that Bayesian inference is superior, but to evaluate how it behaves when the data are small, noisy, and biologically heterogeneous. A standard frequentist analysis is used as a comparison rather than as a presumed gold standard. Raw gene-level counts were analyzed after low-count filtering, median-of-ratios normalization, and exploratory quality control. A negative-binomial Bayesian hierarchical model was specified with sample size factors, treatment effects, gene-specific dispersion, and donor blocking. Shrinkage priors were used for treatment effects to stabilize fold-change estimation under small n. DESeq2 and edgeR were fitted to the same filtered count matrix, and posterior diagnostics, prior sensitivity, and model failures were recorded rather than suppressed. The selected dataset was visibly imperfect: library sizes varied by approximately two-fold, more than half of the original gene rows were dominated by zeros or near-zero counts, and PCA showed strong donor structure in addition to treatment separation. After filtering, 24,159 genes were retained from the original 64,102 annotated gene rows, leaving a count matrix still affected by low expression and donor heterogeneity. MCMC convergence was adequate for most modeled genes, but a non-trivial subset of low-count or high-dispersion genes showed poor mixing, wide credible intervals, or unstable posterior treatment effects. Agreement between Bayesian posterior decision rules and DESeq2 was moderate rather than complete, especially for weakly expressed genes with large apparent fold changes. Bayesian inference helped make uncertainty visible, particularly for genes whose estimated treatment effects looked large but were poorly supported by sparse counts. It did not eliminate the consequences of small n, donor effects, low expression, or imperfect model fit. The analysis supports a cautious workflow in which Bayesian and frequentist results are compared, disagreements are reported, and convergence diagnostics are treated as substantive results. The main conclusion is that Bayesian methods are useful for real transcriptomic data only when uncertainty, failure, and prior dependence are made explicit.
%U https://jbiochemtech.com/article/statistical-analysis-of-gene-expression-data-using-bayesian-inference-methods-in-biotechnology-resea-go0w8dhml6r2t7n