German Cancer Research Center (DKFZ), Div, Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany.
BMC Bioinformatics. 2013 Nov 21;14:336. doi: 10.1186/1471-2105-14-336.
Discovering functional relationships of genes through cell-based phenotyping has become an important approach in functional genomics. High-throughput imaging offers the ability to quantitatively assess complex phenotypes after perturbation by RNA interference (RNAi). Such image-based high-throughput RNAi screening studies have facilitated the discovery of novel components of gene networks and their interactions. Images generated by automated microscopy are typically analyzed by extracting quantitative features of individual cells, resulting in large multidimensional data sets. Robust and sensitive methods to interpret these data sets and to derive biologically relevant information in a high-throughput and unbiased manner remain to be developed.
Here we propose a new analysis method, PhenoDissim, which computes the phenotypic dissimilarity between cell populations via Support Vector Machine classification and cross validation. Applying this method to a kinome RNAi screening data set, we demonstrate that the proposed method shows a good replicate reproducibility, separation of controls and clustering quality, and we are able to identify siRNA phenotypes and discover potential functional links between genes.
PhenoDissim is a novel analysis method for image-based high-throughput screen, relying on two parameters which can be automatically optimized without a priori knowledge. PhenoDissim is freely available as an R package.
通过细胞表型分析发现基因的功能关系已成为功能基因组学的重要方法。高通量成像技术提供了在 RNA 干扰 (RNAi) 后定量评估复杂表型的能力。这种基于图像的高通量 RNAi 筛选研究促进了基因网络及其相互作用的新组件的发现。通过自动显微镜生成的图像通常通过提取单个细胞的定量特征进行分析，从而产生大型多维数据集。仍然需要开发强大且敏感的方法来解释这些数据集并以高通量和无偏倚的方式得出生物学上相关的信息。
我们在这里提出了一种新的分析方法 PhenoDissim，它通过支持向量机分类和交叉验证计算细胞群体之间的表型差异。将该方法应用于激酶组 RNAi 筛选数据集，我们证明了所提出的方法具有良好的复制再现性、对照分离和聚类质量，并且能够识别 siRNA 表型并发现基因之间潜在的功能联系。
PhenoDissim 是一种新颖的基于图像的高通量筛选分析方法，它依赖于两个可以自动优化而无需先验知识的参数。PhenoDissim 作为 R 包免费提供。
