从性别分层 X 连锁基因网络生成假设的多尺度框架
A multiscale framework for hypothesis generation from sex-stratified X-linked gene networks
一个可复现的分层基因共表达网络分析框架被提出，整合队列平衡、bootstrap 稳定化共识网络、拓扑统计、配对标签置换、多重性控制与相关性方法基准评测，用于区分稳健统计证据与探索性信号。该框架在 X 连锁基因的多癌种案例中演示，按逃逸、失活或可变逃逸 X 染色体失活分类，纳入 15 个 TCGA 癌症队列和 90 个性别分层网络。315 次配对置换检验无一通过 Benjamini–Hochberg 校正，未提供多重性校正后的证据；但探索性 graphlet 图谱显示癌症与基因类别特异的网络组织模式，可供独立重复验证。
来源：Bioinformatics Advances · doi.org
Stratified gene co-expression network analyses compare molecular organisation across biological groups; however, their interpretation is challenged by cohort imbalance, network instability, correlation-method dependence, and multiplicity. These issues are particularly relevant when comparing network structures across heterogeneous cohorts, where apparent patterns may not be reproducible. This study presents a reproducible framework for stratified gene co-expression network analysis. It integrates cohort balancing, bootstrap-stabilised consensus networks, topology-based statistics, paired label permutations, multiplicity control, and correlation-method benchmarking. Its design distinguishes robust statistical evidence from exploratory signals and their biological interpretation. The framework is illustrated through a multi-cancer case study of sex-associated co-expression network organisation among X-linked genes classified as escape, inactive, or variably escaping from X-chromosome inactivation. The analysis included 15 TCGA cancer cohorts and 90 sex-stratified networks, enabling systematic comparison of network structure across cancer types and X-inactivation gene classes. Across 315 paired permutation tests, none survived Benjamini–Hochberg correction, providing no multiplicity-adjusted evidence. Nevertheless, exploratory graphlet profiles revealed cancer- and gene-class-specific patterns of network organisation, highlighting candidate structures for independent replication. The case study illustrates how the framework can distinguish nominal or visually compelling differences from multiplicity-adjusted evidence while preserving informative exploratory patterns for structured hypothesis generation.