Learn how an innovative statistical method is changing the landscape for complex diseases such as idiopathic pulmonary fibrosis, unlocking greater potential for more accurate medical discoveries.
A Hybrid Approach
Hybrid Bayesian inference: Researchers at the Texas A&M University have developed a new, state-of-the utilization statistical method called hybrid Bayesian inference. It merges several types of statistical methods in the hope of unravelling some of the many intricate diseases that have confounded biologists for decades.
This is trading off the strength and weakness of informative priors: borrowing information are strong if experts knows things; sucks when they don’t. The method makes use of the matrices that describe a quantum many-body wavefunction, and these encapsulate both frequentist-like and Bayesian inferences together in one go (in much the same sense as it took place for small sample sizes).
The approach has now been applied to 26 additional example analyses and as the lead researcher, Dr. Gang Han, claimed in an article that breaks down this achievement for Nature: “In all cases tested, a combined Bayesian-frequency inference (hybrid) significantly yields better performance than either component by itself.” “Using Ans as a hybrid is more appropriate than frequentist inference because it acknowledges prior information, and more reasonable than Bayesian since it limits the problem of bias that arises from arbitrary noninformative priors, which can be severe with small samples,” he said.
Revealing Novel Insights Into Idiopathic Pulmonary Fibrosis
In a case study done on idiopathic pulmonary fibrosis (IPF) — an obscure and challenging disease, which has bewildered the scientific community for years [9] — the model was fitted using Bayesian-frequentist hybrid framework. The efficiency of the approach was then assessed experimentally in simulations based on a semisynthetic single-cell RNA sequencing data source from mouse hypothalamus with respect to statistical power and false discovery rate relative to other analysis methods.
All three methods were used to analyse gene expression data from a lung tissue dataset, with the first two already providing some example results. More specifically, they assayed the relationship between IPF and transforming growth factor beta 1 after controlling for probability of alveolar macrophage cell calls.
The results were remarkable. The Bayesian frequentist hybrid inference, while requiring informative prior information, identified many more genes of interest (and those that were mutable to further interrogation) than both the frequentist and gene correlation analyses combined, and these genes appeared inherently biologically relevant in light of current understanding of IPF. On the other hand, a large number of differentially expressed genes were detected by the DIRP method while the frequentist or Bayesian methods failed to indicate these results.
“Most unexpectedly, this produced full-pathway analysis of the genes known to be associated with disease status in a particular cell type,” Han said. To elaborate, it refers to its ability to identify important genes from limited sample size of one type of cells concerning with a particular disease.
Conclusion
A novel hybrid of Bayesian inference created by Texas A&M University School of Public Health researchers offers a more powerful and comprehensive modeling tool for cell-to-cell variation in single-cell RNA sequencing analysis at an affordable cost. By merging the advantages of frequentist and Bayesian methods, this technique mitigates as far as possible their respective deficiencies, facilitating stronger discovery in complex diseases such as idiopathic pulmonary fibrosis. With the researchers continuing to establish more possibilities for this framework, it implies a future where we know more about different medical conditions, helping build a path for treatments customized to just as needful.
