Emulate Inc., Boston, MA 02210, USA.
Founders Fund, San Francisco, CA 94129, USA.
Bioinformatics. 2021 Jan 29;36(21):5194-5204. doi: 10.1093/bioinformatics/btaa654.
Recapitulating aspects of human organ functions using in vitro (e.g. plates, transwells, etc.), in vivo (e.g. mouse, rat, etc.), or ex vivo (e.g. organ chips, 3D systems, etc.) organ models is of paramount importance for drug discovery and precision medicine. It will allow us to identify potential side effects and test the effectiveness of new therapeutic approaches early in their design phase, and will inform the development of better disease models. Developing mathematical methods to reliably compare the 'distance/similarity' of organ models from/to the real human organ they represent is an understudied problem with important applications in biomedicine and tissue engineering.
We introduce the Transcriptomic Signature Distance (TSD), an information-theoretic distance for assessing the transcriptomic similarity of two tissue samples, or two groups of tissue samples. In developing TSD, we are leveraging next-generation sequencing data as well as information retrieved from well-curated databases providing signature gene sets characteristic for human organs. We present the justification and mathematical development of the new distance and demonstrate its effectiveness and advantages in different scenarios of practical importance using several publicly available RNA-seq datasets.
The computation of both TSD versions (simple and weighted) has been implemented in R and can be downloaded from https://github.com/Cod3B3nd3R/Transcriptomic-Signature-Distance.
dimitris.manatakis@emulatebio.com.
Supplementary data are available at Bioinformatics online.
使用体外（例如平板、Transwell 等）、体内（例如小鼠、大鼠等）或体外（例如器官芯片、3D 系统等）器官模型来重现人类器官功能的各个方面对于药物发现和精准医学至关重要。这将使我们能够在设计阶段早期识别潜在的副作用，并测试新治疗方法的有效性，并为更好的疾病模型的开发提供信息。开发可靠地比较代表真实人体器官的器官模型之间的“距离/相似性”的数学方法是一个研究不足的问题，在生物医学和组织工程中有重要的应用。
我们引入了转录组特征距离（TSD），这是一种用于评估两个组织样本或两组组织样本转录组相似性的信息论距离。在开发 TSD 时，我们利用了下一代测序数据以及从精心整理的数据库中检索到的信息，这些数据库提供了人类器官特征基因集。我们提出了新距离的合理性和数学发展，并使用几个公开可用的 RNA-seq 数据集展示了其在不同实际重要场景中的有效性和优势。
TSD 的两种版本（简单和加权）的计算已在 R 中实现，并可从 https://github.com/Cod3B3nd3R/Transcriptomic-Signature-Distance 下载。
dimitris.manatakis@emulatebio.com。
补充数据可在 Bioinformatics 在线获取。
