ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2014
A Bayesian Semi-parametric Approach for the Differential Analysis of Sequence Counts Data.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Machine Learning Approaches to TCR Repertoire Analysis.Frontiers in immunology · 2022Review
- powerTCR: A model-based approach to comparative analysis of the clone size distribution of the T cell receptor repertoire.PLoS computational biology · 2018Article
- Quantification of Inter-Sample Differences in T-Cell Receptor Repertoires Using Sequence-Based Information.Frontiers in immunology · 2017Article
- Bayesian Nonparametric Inference - Why and How.Bayesian analysis · 2013Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Data obtained using modern sequencing technologies are often summarized by recording the frequencies of observed sequences. Examples include the analysis of T cell counts in immunological research and studies of gene expression based on counts of RNA fragments. In both cases the items being counted are sequences, of proteins and base pairs, respectively. The resulting sequence-abundance distribution is usually characterized by overdispersion. We propose a Bayesian semi-parametric approach to implement inference for such data. Besides modeling the overdispersion, the approach takes also into account two related sources of bias that are usually associated with sequence counts data: some sequence types may not be recorded during the experiment and the total count may differ from one experiment to another. We illustrate our methodology with two data sets, one regarding the analysis of CD4+ T cell counts in healthy and diabetic mice and another data set concerning the comparison of mRNA fragments recorded in a Serial Analysis of Gene Expression (SAGE) experiment with gastrointestinal tissue of healthy and cancer patients.
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Registered trials
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