Mechanistic gene networks inferred from single-cell data with an outlier-insensitive method.
Mechanistic gene networks inferred from single-cell data with an outlier-insensitive method.
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DOI：
10.1016/j.mbs.2021.108722
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发表时间：
2021-12
影响因子：
4.3
通讯作者：
Periwal V
中科院分区：
文献类型：
--
作者：
Han J;Perera S;Wunderlich Z;Periwal V
随着单细胞技术的进步，在细胞分辨率上测量基因动态已经变得可行。相比之下，数据的复杂性增加，使得在计算上解开潜在的生物机制更具挑战性。因此，它是至关重要的，以开发新的计算方法，能够处理这样的复杂性，并提供预测扣除这些数据。已经开发了许多方法来应对这些挑战，每种方法都有自己的优点和局限性。我们提出了一种迭代回归算法，用于从单细胞数据推断一个机械基因网络，特别适合于克服测量离群值所带来的问题。利用该回归模型，我们推断出果蝇胚盘胚胎中基因动态的发育模型。我们的研究结果表明，推断模型的预测能力高于其他模型推断的最小二乘和岭回归。作为一个基线的机制模型应该如何预期执行，我们发现，模型预测的基因动态比预测不同的架构和复杂性的神经网络。即使在小样本量的限制下也是如此。我们比较预测各种基因敲除与发表的实验结果，发现大量的定性协议。我们还对各种基因网络扰动下的基因动态进行了预测，这在非机械模型中是不可能的。
With advances in single-cell techniques, measuring gene dynamics at cellular resolution has become practicable. In contrast, the increased complexity of data has made it more challenging computationally to unravel underlying biological mechanisms. Thus, it is critical to develop novel computational methods capable of dealing with such complexity and of providing predictive deductions from such data. Many methods have been developed to address such challenges, each with its own advantages and limitations. We present an iterative regression algorithm for inferring a mechanistic gene network from single-cell data, especially suited to overcoming problems posed by measurement outliers. Using this regression, we infer a developmental model for the gene dynamics in Drosophila melanogaster blastoderm embryo. Our results show that the predictive power of the inferred model is higher than that of other models inferred with least squares and ridge regressions. As a baseline for how well a mechanistic model should be expected to perform, we find that model predictions of the gene dynamics are more accurate than predictions made with neural networks of varying architectures and complexity. This holds true even in the limit of small sample sizes. We compare predictions for various gene knockouts with published experimental results, finding substantial qualitative agreement. We also make predictions for gene dynamics under various gene network perturbations, impossible in non-mechanistic models.
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影响因子：
14.9
作者：
Larkin A;Marygold SJ;Antonazzo G;Attrill H;Dos Santos G;Garapati PV;Goodman JL;Gramates LS;Millburn G;Strelets VB;Tabone CJ;Thurmond J;FlyBase Consortium
通讯作者：
FlyBase Consortium
DOI：
10.1126/science.aaw3381
发表时间：
2020-02-14
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Weinreb C;Rodriguez-Fraticelli A;Camargo FD;Klein AM
通讯作者：
Klein AM
影响因子：
5.8
作者：
van Someren, EP;Vaes, BLT;Reinders, MJT
通讯作者：
Reinders, MJT
构建遗传调控网络的新多元回归方法
影响因子：
7.5
作者：
Zhang, Shu-Qin;Ching, Wai-Ki;Guo, Dianjing
通讯作者：
Guo, Dianjing
影响因子：
64.8
作者：
Nitzan, Mor;Karaiskos, Nikos;Rajewsky, Nikolaus
通讯作者：
Rajewsky, Nikolaus