A systems approach for analysis of high content screening assay data with topic modeling.
A systems approach for analysis of high content screening assay data with topic modeling.
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DOI：
10.1186/1471-2105-14-s14-s11
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发表时间：
2013
影响因子：
3
通讯作者：
Tong W
中科院分区：
文献类型：
--
作者：
Bisgin H;Chen M;Wang Y;Kelly R;Fang H;Xu X;Tong W
高内涵筛选 (HCS) 已成为毒性评估的重要工具，部分原因在于其同时处理多个测量的优势。这种方法提供了见解并有助于理解细胞水平的系统生物学。为了充分实现这一潜力，应在概率关系中考虑同时测量的活细胞的多个端点，以评估细胞对治疗压力的反应状况，这对从这些测量中提取隐藏的知识和关系提出了巨大的挑战。在这项工作中，我们应用潜在狄利克雷分配 (LDA) 的文本挖掘方法来分析体外 HCS 测定的细胞终点，并将结果与体内组织病理学观察相关。我们测量了 122 种药物的多个 HCS 测定终点。由于LDA要求数据以文档术语格式表示，因此我们首先将测量的连续值转换为文本挖掘工具可以处理的词频。对于每种药物，我们为 4 个时间点中的每一个时间点生成了一份文档。因此，我们最终得到了 488 个文档（药物时间），每个文档对于 10 个端点具有不同的值，这些端点被视为单词。我们使用 LDA 提取了三个主题，并对这些主题进行了检查，以确定来自日本毒理学项目 (TGP) 体内实验的 45 种常见药物的诊断主题，观察它们在治疗后 6 小时和 24 小时的坏死结果。我们发现分配给特定主题的测定终点与观察到的组织病理学一致。 6 小时内显示坏死的药物与严重损伤事件有关，例如脂肪变性、DNA 断裂、线粒体电位和溶酶体质量。DNA 损伤和细胞凋亡与 24 小时内引起坏死的药物相关，表明这些药物中这两种途径之间存在相互作用。我们将没有坏死迹象的药物与细胞损失和核大小测定相关，这表明肝细胞再生。这项研究的证据表明，使用 LDA 进行主题建模可以使我们解释体外测定终点与体内组织学发现（坏死）之间的关系。这种方法的有效性可能会大大增加我们对系统生物学的理解。
High Content Screening (HCS) has become an important tool for toxicity assessment, partly due to its advantage of handling multiple measurements simultaneously. This approach has provided insight and contributed to the understanding of systems biology at cellular level. To fully realize this potential, the simultaneously measured multiple endpoints from a live cell should be considered in a probabilistic relationship to assess the cell's condition to response stress from a treatment, which poses a great challenge to extract hidden knowledge and relationships from these measurements. In this work, we applied a text mining method of Latent Dirichlet Allocation (LDA) to analyze cellular endpoints from in vitro HCS assays and related to the findings to in vivo histopathological observations. We measured multiple HCS assay endpoints for 122 drugs. Since LDA requires the data to be represented in document-term format, we first converted the continuous value of the measurements to the word frequency that can processed by the text mining tool. For each of the drugs, we generated a document for each of the 4 time points. Thus, we ended with 488 documents (drug-hour) each having different values for the 10 endpoints which are treated as words. We extracted three topics using LDA and examined these to identify diagnostic topics for 45 common drugs located in vivo experiments from the Japanese Toxicogenomics Project (TGP) observing their necrosis findings at 6 and 24 hours after treatment. We found that assay endpoints assigned to particular topics were in concordance with the histopathology observed. Drugs showing necrosis at 6 hour were linked to severe damage events such as Steatosis, DNA Fragmentation, Mitochondrial Potential, and Lysosome Mass. DNA Damage and Apoptosis were associated with drugs causing necrosis at 24 hours, suggesting an interplay of the two pathways in these drugs. Drugs with no sign of necrosis we related to the Cell Loss and Nuclear Size assays, which is suggestive of hepatocyte regeneration. The evidence from this study suggests that topic modeling with LDA can enable us to interpret relationships of endpoints of in vitro assays along with an in vivo histological finding, necrosis. Effectiveness of this approach may add substantially to our understanding of systems biology.