Microbial growth characteristics have long been used to investigate fundamental questions of biology. Colony-based high-throughput screens enable parallel fitness estimation of thousands of individual strains using colony growth as a proxy for fitness. However, fitness estimation is complicated by spatial biases affecting colony growth, including uneven nutrient distribution, agar surface irregularities, and batch effects. Analytical methods that have been developed to correct for these spatial biases rely on the following assumptions: i) that fitness effects are normally distributed, and ii) that most genetic perturbations lead to minor changes in fitness. Although reasonable for many applications, these assumptions are not always warranted and can limit the ability to detect small fitness effects. Beneficial fitness effects, in particular, are notoriously difficult to detect under these assumptions. Here, we developed the linear interpolation-based detector (LI Detector) framework to enable sensitive colony-based screening without making prior assumptions about the underlying distribution of fitness effects. The LI Detector uses a grid of reference colonies to assign a relative fitness value to every colony on the plate. We show that the LI Detector is effective in correcting for spatial biases and equally sensitive towards increase and decrease in fitness. LI Detector offers a tunable system that allows the user to identify small fitness effects with unprecedented sensitivity and specificity. LI Detector can be utilized to develop and refine gene-gene and gene-environment interaction networks of colony-forming organisms, including yeast, by increasing the range of fitness effects that can be reliably detected.

中文翻译：

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LI Detector：适用于敏感菌落筛选的框架，无论适应性效应的分布如何

长期以来，微生物的生长特性一直被用来研究生物学的基本问题。基于菌落的高通量筛选可以使用菌落生长作为适应度的代理来并行评估数千个单独菌株的适应度。但是，适应性估计由于影响菌落生长的空间偏差而变得复杂，其中包括营养成分分布不均，琼脂表面不规则和批次效应。为纠正这些空间偏差而开发的分析方法依赖于以下假设：i）适应性效应呈正态分布，并且ii）大多数遗传扰动导致适应性发生细微变化。尽管对于许多应用而言是合理的，但并不总是必须保证这些假设，并且这些假设可能会限制检测较小适应性效果的能力。有益的健身效果，在这些假设下，很难检测到。在这里，我们开发了基于线性插值的检测器（LI Detector）框架，以实现基于敏感菌落的筛选，而无需事先对适应性效应的基本分布进行假设。LI检测器使用参考菌落网格为平板上的每个菌落分配相对适应度值。我们表明，LI检测器可以有效地校正空间偏差，并且对适应性的提高和降低同样敏感。LI Detector提供了一种可调系统，使用户能够以前所未有的灵敏度和特异性识别出小的健身效果。LI Detector可用于开发和完善集落形成生物（包括酵母菌）的基因-基因和基因-环境相互作用网络，