2022/11/27 by Tian Lu, Chen, Zhongren, Tian, Lu +2
Biochemistry, Genetics and Molecular Biology · Computer Science · #Applications (stat.AP) #Bayesian Methods and Mixture Models #Evolution and Genetic Dynamics #FOS: Computer and information sciences #Gene expression and cancer classification #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2211.14755
openalex publication_date 2022/11/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper is motivated by the need to quantify human immune responses to environmental challenges. Specifically, the genome of the selected cell population from a blood sample is amplified by the well-known PCR process of successive heating and cooling, producing a large number of reads. They number roughly 30,000 to 300,000. Each read corresponds to a particular rearrangement of so-called V(D)J sequences. In the end, the observation consists of a set of numbers of reads corresponding to different V(D)J sequences. The underlying relative frequencies of distinct V(D)J sequences can be summarized by a probability vector, with the cardinality being the number of distinct V(D)J rearrangements present in the blood. Statistical question is to make inferences on a summary parameter of the probability vector based on a single multinomial-type observation of a large dimension. Popular summary of the diversity of a cell population includes clonality and entropy, or more generally, is a suitable function of the probability vector. A point estimator of the clonality based on multiple replicates from the same blood sample has been proposed previously. After obtaining a point estimator of a particular function, the remaining challenge is to construct a confidence interval of the parameter to appropriately reflect its uncertainty. In this paper, we have proposed to couple the empirical Bayes method with a resampling-based calibration procedure to construct a robust confidence interval for different population diversity parameters. The method has been illustrated via extensive numerical study and real data examples.