AI 中文总结
该研究提出了一种联合划分评估者与项目的贝叶斯Plackett-Luce潜在块模型，通过模拟验证其不确定性表征能力，将其应用于TCGA泛癌基因排序，揭示组织驱动的样本结构并支持生物学解释。
AI 中文摘要
我们引入了一种贝叶斯潜在块模型，该模型在Plackett-Luce观测模型下同时对评估者和项目进行划分。评估者被分配到C个簇，项目被分配到K个块；每个评估者簇内的块内项目共享一个共同的强度参数，从而产生简约的C×K共聚类表示。独立的Gnedin先验用于推断C和K。数据增广提供共轭吉布斯更新和具有分裂合并移动的易处理MCMC采样器。模拟在信号、排序深度和组平衡变化时表征恢复和后验不确定性。将该模型应用于癌症基因图谱（TCGA）泛癌前500个基因表达排序，揭示了组织驱动的样本结构，同时将基因水平异质性压缩为可解释的块。基于排序的GSEA对后验基因评分支持生物学解释。
英文摘要
We introduce a Bayesian latent block model that jointly partitions assessors and items under a Plackett--Luce observation model. Assessors are assigned to $C$ clusters and items to $K$ blocks; items in a block share a common strength parameter within each assessor cluster, yielding a parsimonious $C\times K$ co-clustering representation. Independent Gnedin priors infer $C$ and $K$. Data augmentation gives conjugate Gibbs updates and a tractable MCMC sampler with split-merge moves. Simulations characterize recovery and posterior uncertainty as signal, ranking depth, and group balance vary. Applied to the cancer gene atlas (TCGA) pan-cancer top-500 gene-expression rankings, the model reveals tissue-driven sample structure while compressing gene-level heterogeneity into interpretable blocks. Rank-based GSEA of posterior gene scores supports biological interpretation.
Comments53 pages, 12 figures
