基因表达模式的最大熵模型
Maximum entropy models for patterns of gene expression
AI总结：
针对单细胞基因表达测量分析问题，本研究用最大熵原理构建适配实验统计的Ising模型，验证其高阶预测能力，并基于分布局部极大值实现细胞分类，为细胞类别定义提供定量依据与可解释标准。
摘要：
新的实验方法使得人们能够在数千甚至数百万个单个细胞的快照中同时测量众多基因的表达水平。当前分析这类实验的方法包括聚类或低维投影。本文中，我们利用最大熵原理获得一种概率描述，用以捕捉哺乳动物大脑细胞中数百个基因的mRNA存在或缺失的观测情况。我们构建了与实验均值和成对相关性相符的Ising模型，并通过证明其能对高阶统计量给出良好预测来验证该模型。我们注意到细胞状态的概率分布存在多个局部极大值。通过依据对应的极大值对细胞状态进行标记，我们得到了一种细胞分类结果，其与使用传统聚类技术的既往结果高度吻合。我们的结果为基因表达统计提供了定量描述，也为定义细胞类别提供了可解释的标准，支持了细胞类别产生于基因表达水平集体相互作用的假说。
英文摘要：
New experimental methods make it possible to measure the expression levels of many genes, simultaneously, in snapshots from thousands or even millions of individual cells. Current approaches to analyze these experiments involve clustering or low-dimensional projections. Here we use the principle of maximum entropy to obtain a probabilistic description that captures the observed presence or absence of mRNAs from hundreds of genes in cells from the mammalian brain. We construct the Ising model compatible with experimental means and pairwise correlations, and validate it by showing that it gives good predictions for higher-order statistics. We notice that the probability distribution of cell states has many local maxima. By labeling cell states according to the associated maximum, we obtain a cell classification that agrees well with previous results that use traditional clustering techniques. Our results provide quantitative descriptions of gene expression statistics and interpretable criteria for defining cell classes, supporting the hypothesis that cell classes emerge from the collective interaction of gene expression levels.
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