According to the National Cancer Institute, there were 9.5 million cancer-related deaths in 2018. A challenge in improving treatment is resistance in genetically unstable cells. The purpose of this study is to evaluate unsupervised machine learning on classifying treatment-resistant phenotypes in heterogeneous tumors through analysis of single cell RNA sequencing(scRNAseq) data with a pipeline and evaluation metrics. scRNAseq quantifies mRNA in cells and characterizes cell phenotypes. One scRNAseq dataset was analyzed (tumor/non-tumor cells of different molecular subtypes and patient identifications). The pipeline consisted of data filtering, dimensionality reduction with Principal Component Analysis, projection with Uniform Manifold Approximation and Projection, clustering with nine approaches (Ward, BIRCH, Gaussian Mixture Model, DBSCAN, Spectral, Affinity Propagation, Agglomerative Clustering, Mean Shift, and K-Means), and evaluation. Seven models divided tumor versus non-tumor cells and molecular subtype while six models classified different patient identification (13 of which were presented in the dataset); K-Means, Ward, and BIRCH often ranked highest with ~80% accuracy on the tumor versus non-tumor task and ~60% for molecular subtype and patient ID. An optimized classification pipeline using K-Means, Ward, and BIRCH models was evaluated to be most effective for further analysis. In clinical research where there is currently no standard protocol for scRNAseq analysis, clusters generated from this pipeline can be used to understand cancer cell behavior and malignant growth, directly affecting the success of treatment.
翻译:据国家癌症研究所称,2018年有950万人死于癌症。改善治疗的挑战在于基因不稳定细胞的抗抗药性。本研究的目的是通过分析单细胞RNA测序(scRNAseq)数据,用管道和评价指标来分析单细胞RNA测序(scRNAseq)数据,对异种肿瘤中抗治疗性动物类型进行分类,评估未经监督的机器学习情况。对细胞中的 mRNA 定量为 scRNA, 并定性细胞类型。分析了一个 scRNAseq 数据集(不同分子亚型和病人识别的脉冲/非脉冲细胞细胞/非细胞细胞细胞细胞细胞细胞细胞)。管道包括数据过滤、用主构件分析来降低维度,用统一马氏吸附和预测,将九种方法(Sward、BIRCH、Gassian Mixturt、Spectalalalalalal、Agrodudeal 和Memal IMI)进行数据过滤,目前六种模型用不使用直位模型进行直位模型分析,用于直径DNA和直径DNA分析。