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3DeFDR: statistical methods for identifying cell type-specific looping interactions in 5C and Hi-C data

机译:3DEFDR:用于识别5C和Hi-C数据中的特定单元格类型的循环交互的统计方法

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摘要

Overview of interaction score thresholding procedure for cell type-specific looping interaction classification. a A 5C dataset is input as a set of interaction frequency matrices, with each matrix capturing the same set of genomic contacts under a different cellular condition. b Raw 5C counts are converted to interaction scores (IS) which reflect bias-corrected, sequencing depth normalized, local expected background signal normalized, and statistically modeled interaction frequency values that are comparable within and between conditions under the assumptions of our model (detailed in the “Methods” section and Fig. 4). c Interaction scores are thresholded to allow detection and classification of looping interactions that are significantly differential across cellular conditions. d Seven looping interaction classes after a 3-way thresholding scheme on ES-2i, ES-serum, and NPC cellular states. e IS heatmaps at two selected genomic loci. Green boxes highlight regions of qualitatively apparent differences in looping signal. f Loop classification results after applying 3DeFDR-5C’s 3-way IS thresholding procedure
机译:细胞类型特定循环交互分类的交互分数阈值过程概述。输入5C数据集作为一组交互频率矩阵,每个矩阵在不同的细胞条件下捕获相同的基因组接触。 B原始5C计数被转换为相互作用分数(IS)反映偏置校正,排序深度标准化,局部预期背景信号标准化,以及在我们模型假设下和条件下的条件下和之间的统计建模的交互频率值(详述“方法”部分和图4)。 C阈值的C相互作用评分以允许在细胞条件下显着差异的循环相互作用的检测和分类。 D七个循环交互等级在ES-2I,ES-血清和NPC细胞状态下进行3路阈值方案。 E在两个选定的基因组基因座中是热量的。绿色框突出显示循环信号的定性明显差异的区域。 F循环分类结果应用3DEFDR-5C的3路之后是阈值处理程序

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