Modeling Heterogenous Outcomes in Functional Genetic Screens Using Single-cell Genomics as Readouts

用单细胞基因组学方法模拟功能基因筛查中的异质性结果 读数 这项补充请求旨在推进对单细胞CRISPR筛查数据集的分析 开发新的计算模型以应对不同转录组反应的挑战 在扰乱相同的基因时。它属于家长奖的范围，为以下对象开发算法 功能基因筛查，但建议跟进意外结果(单细胞CRISPR筛查)和 加强这一方向，正如初步结果所表明的那样，这是出乎意料的富有成效。 具体来说，我们的目标是：(1)建立一个模型来检测扰动序列分析中的异质性表达模式，L， 以及(2)将该模型扩展到其他数据集类型，包括池单细胞数据集和批量转录数据集 不受各种基因干扰。这项工作将大大提高我们对基因-表型的理解。 在单细胞水平上的关系，在癌症研究、病毒学和基因调控中有着广泛的应用。

Modeling Heterogenous Outcomes in Functional Genetic Screens Using Single-cell Genomics as Readouts This supplementary request seeks to advance the analysis of single-cell CRISPR screening datasets by developing novel computational models to address the challenge of heterogeneous transcriptome responses upon perturbing the same gene. It falls within the scope of the parent award to develop algorithms for functional genetic screens, but proposes to follow up on unanticipated results (single-cell CRISPR screen) and enhance this direction, which is unexpectedly productive, as is demonstrated in preliminary results. Specifically, we aim to: (1) build a model to detect heterogeneous expression patterns in Perturb-seq assays, l, and (2) extend the model to other dataset types, including pooled single-cell and bulk transcriptomics datasets from various gene perturbations. This work will significantly enhance our understanding of genotype-phenotype relationships at the single-cell level, with broad applications in cancer research, virology, and gene regulation.