Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets.
Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets.
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DOI：
10.1093/bioinformatics/btac212
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发表时间：
2022-05-13
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等位基因表达分析有助于检测遗传变异的顺式调控机制，这种机制会在杂合子中产生等位基因失衡（AI）。在缺乏时间或空间分辨率的大量数据中测量 AI 存在局限性，即细胞类型特异性 (CTS)、空间或时间依赖性 AI 信号可能会减弱或无法检测到。我们引入了一种统计方法airpart，用于从单细胞RNA测序数据中识别差异CTS AI，或从其他空间或时间解析数据集中识别动态AI。 Airpart 输出离散的数据分区，指向顺式遗传调控共同机制下的基因和细胞组。为了解决单细胞数据中的低计数问题，我们的方法使用具有二项式似然的广义融合套索通过 AI 信号划分细胞组，并使用分层贝叶斯模型进行 AI 统计推断。在模拟中，airpart 通过 AI 准确检测了细胞类型的分区，并且等位基因比率估计的均方根误差 (RMSE) 低于现有方法。在实际数据中，airpart 识别出跨细胞状态的差异等位基因不平衡模式，并可用于定义 AI 信号在空间或时间轴上的趋势。 airpart 包作为 R/Bioconductor 包提供，网址为 https://bioconductor.org/packages/airpart。 补充数据可在生物信息学在线获取。
Allelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation, which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial- or time-dependent AI signals may be dampened or not detected. We introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing data, or dynamics AI from other spatially or time-resolved datasets. airpart outputs discrete partitions of data, pointing to groups of genes and cells under common mechanisms of cis-genetic regulation. In order to account for low counts in single-cell data, our method uses a Generalized Fused Lasso with Binomial likelihood for partitioning groups of cells by AI signal, and a hierarchical Bayesian model for AI statistical inference. In simulation, airpart accurately detected partitions of cell types by their AI and had lower Root Mean Square Error (RMSE) of allelic ratio estimates than existing methods. In real data, airpart identified differential allelic imbalance patterns across cell states and could be used to define trends of AI signal over spatial or time axes. The airpart package is available as an R/Bioconductor package at https://bioconductor.org/packages/airpart. Supplementary data are available at Bioinformatics online.