Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
2024年3月1日
Here, we introduce a protocol for converting transcriptomic data into a mqTrans view, enabling the identification of dark biomarkers. While not differentially expressed in conventional transcriptomic analyses, these biomarkers exhibit differential expression in the mqTrans view. The approach serves as a complementary technique to traditional methods, unveiling previously overlooked biomarkers.
Our research focuses on the intergenetic interactions of disease. We found that many genes have complex intertwined relationships with each other. By exploring these relationships, we aim in making more precise diagnosis in disease treatment and management.
We found that many dark biomarkers with traditional combinational methods ignored, are supported by many medical literatures. This indicates that approach can further assist biologists in reducing time cycle for biomarker screening. The biggest challenge during the experiment was addressing the issue of small sample sizes of different disease types.
To overcome this difficulty, we collected as many healthy samples as possible to train a reference model, which we called Health Model. To begin, create a new virtual environment named Health Model with Python version 3.7. in the Slurm Cluster supercomputing platform, execute the module load anaconda command.
Once the command is executed, a confirmation prompt appears on the screen. Enter Y to proceed and wait for the process to complete. Then activate the virtual environment following platform specific instructions.
Next, run the command to install PyTorch 1.13.1. Install additional packages for torch geometric, such as torch underscore scatter, torch underscore sparse, torch underscore cluster, and torch underscore spline underscore convulsion, following the installation guidelines. Then install the torch geometric package version 2.2.0.
Download the code and the pre-trained Health Model from the Health Informatics Lab website. Decompress the file to a desired path. Then change the working directory in the command line to the Health Model MQ trans folder.
Execute the command to generate MQ trans features and obtain the outputs. The MQ trans features will be generated as output MQ targets CSV, and the label file will be received as output label CSV. Additionally, original expression values of the MRNA genes will be extracted as file output test targets CSV.
Next, use the feature selection algorithm for selecting MQ trans features. If selecting MQ trans features or original features without combining them, set combine to false. Select 800 original features and split the dataset into 0.8 to 0.2 for training and testing.
To combine MQ trans features with the original expression values for feature selection, set combine to true. Dark biomarkers with differential MQ trans values, but undifferential mRNA expression were identified. Among 3, 062 features, 221 dark biomarkers were detected.
A general scarcity of dark biomarkers was observed in comparison to traditional biomarkers across most cancer types except BRCA, MESO, and TGCT.
概述
本研究探讨了与疾病相关的基因之间复杂的相互作用，重点在于识别传统方法常忽略的“暗”生物标志物。所提出的mqTrans视角有助于深入理解这些生物标志物，其表达水平相较于传统转录组学分析表现出差异性表达。
关键研究组成部分
研究领域
- 与疾病相关的遗传相互作用。
- 生物标志物的鉴定与分析。
- 诊断方法的改进。
背景
- 基因在疾病背景下表现出复杂的相互依赖关系。
- 传统方法常常遗漏隐性生物标志物。
- 现有的医学文献支持这些生物标志物的普遍存在。
使用的方法
- 在 Python 中创建名为 Health Model 的虚拟环境。
- 使用在健康样本上训练的参考模型。
- 对 mqTrans 特征实施特征选择算法。
主要结果
- 从 3,062 个特征中鉴定出 221 个暗生物标志物。
- 暗生物标志物表现出差异性的 mqTrans 值，但无差异性 mRNA 表达。
- 该方法成功凸显了传统生物标志物检测中的不足之处。
结论
- 该研究提供了一种揭示暗生物标志物的新方法。
- 该方法可提高生物标志物筛选的效率与准确性。
常见问题
行业重要性概述
将多任务图注意力网络与转录组学数据整合，能够检测出传统差异表达分析所忽略的调控特征和隐性生物标志物。该方法通过揭示隐藏的调控信号，提高了早期发现阶段的预测置信度，有助于更稳健地进行靶点验证以及基于风险调整的研发组合决策。HealthModel 框架解决了小规模数据集的挑战，拓展了转录组谱在生物制药研发管线中的应用价值。
生物制药研发中的策略性应用
早期发现与靶点验证
- 能够解析复杂的基因间调控网络，用于功能靶点的验证。
- 通过发现标准分析所遗漏的隐性生物标志物，支持生物学风险的降低。
- 在小样本情况下，提高靶点选择与优先排序的预测可信度。
筛选与检测方法开发
- 生成定量的 mqTrans 特征，用于标准化的下游筛选工作流程。
- 通过整合调控数据与表达数据，促进可重复的特征筛选及检测就绪性。
- 实现对化合物评价中新 biomarker 候选物的可扩展性识别。
转化与临床前研究
- 将转录组调控特征与疾病相关系统对齐，以实现转化连续性。
- 通过将隐性生物标志物检测整合到临床前模型中，支持基于风险调整的推进。
- 通过调控网络分析提供机制层面的风险降低。
流程与工作流整合
HealthModel 和 mqTrans 工作流程通过从转录组数据生成调控特征视图，连接了早期发现、靶点验证和临床前研究。
- 发现生物学：通过调控网络建模支持假设检验和通路解析。
- 筛选：提供可重复、定量的mqTrans输出，用于稳健的检测方法开发。
- 分析：提供统计输出和特征选择，用于比较基于调控和表达的生物标志物。
- 转化研究：通过识别与疾病相关的调控特征，实现从发现到临床前验证的连续性。
- 企业级复用：提供可复用的计算流程，可适应多种转录组数据集。
运营与企业影响
- 科学价值：提高生物标志物发现的预测置信度，降低机制上的模糊性。
- 操作价值：标准化调控特征提取流程，支持跨数据集的可重复性。
- 战略价值：通过揭示隐藏的调控信号，优化决策流程中的立项或终止判断，提升资本使用效率。
- 项目组合影响：将“隐性生物标志物”检测整合到研发管线决策中，实现基于风险调整的优先级排序。
实施注意事项
- 需要具备计算生物学和转录组数据分析的专业知识。
- 需要具备 Python、PyTorch 和 Torch Geometric 基础设施以执行模型。
- 要求跨团队在特征选择和数据预处理方面实现标准化。
- 可适用于多种疾病模型，但依赖于健康参考样本的可获得性。
- 受限于样本量较小以及对强大计算资源的需求。
