  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.
