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- W4387303900 abstract "Abstract Background : Single cell RNA sequencing technology (scRNA-seq) has been proven useful in understanding cell-specific disease mechanisms. However, identifying genes of interest remains a key challenge. Pseudo-bulk methods that pool scRNA-seq counts in the same biological replicates have been commonly used to identify differentially expressed genes. However, such methods may lack power due to the limited sample size of scRNA-seq datasets, which can be prohibitively expensive. Results : Motivated by this, we proposed to use the Bayesian-frequentist hybrid (BFH) framework to increase the power. Conclusion : In our idiopathic pulmonary fibrosis (IPF) case study, we demonstrated that with a proper informative prior, the BFH approach identified more genes of interest. Furthermore, these genes were reasonable based on the current knowledge of IPF. Thus, the BFH offers a unique and flexible framework for future scRNA-seq analyses." @default.
- W4387303900 created "2023-10-04" @default.
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- W4387303900 date "2023-10-03" @default.
- W4387303900 modified "2023-10-05" @default.
- W4387303900 title "Bayesian-frequentist hybrid inference framework for single cell RNA-seq analyses" @default.
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- W4387303900 doi "https://doi.org/10.21203/rs.3.rs-3384541/v1" @default.
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