A novel computational algorithm for predicting immune cell types using single-cell RNA sequencing data

dc.contributor.authorJia, Shuo
dc.contributor.examiningcommitteeJones, Meaghan (Biochemistry and Medical Genetics)en_US
dc.contributor.examiningcommitteeMcManus, Kirk (Biochemistry & Medical Genetics)en_US
dc.contributor.examiningcommitteeArsenio, Janilyn (Internal Medicine)en_US
dc.contributor.supervisorHu, Pingzhao (Biochemistry and Medical Genetics)en_US
dc.degree.disciplineBiochemistry and Medical Geneticsen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractBackground: Cells from our immune system detect and kill pathogens to protect our body against many diseases. However, current methods for determining cell types have some major limitations, such as being time-consuming and with low throughput rate, etc. These problems stack up and hinder the deep exploration of cellular heterogeneity. Immune cells that are associated with cancer tissues play a critical role in revealing the stages of tumor development. Identifying the immune composition within tumor microenvironments in a timely manner will be helpful to improve clinical prognosis and therapeutic management for cancer. Single-cell RNA sequencing (scRNA-seq), an RNA sequencing (RNA-seq) technique that focuses on a single cell level, has provided us with the ability to conduct cell type classification. Although unsupervised clustering approaches are the major methods for analyzing scRNA-seq datasets, their results vary among studies with different input parameters and sizes. However, in supervised machine learning methods, information loss and low prediction accuracy are the key limitations. Methods and Results: Genes in the human genome align to chromosomes in a particular order. Hence, we hypothesize incorporating this information into our model will potentially improve the cell type classification performance. In order to utilize gene positional information, we introduce chromosome-based neural network, namely ChrNet, a novel chromosome-specific re-trainable supervised learning method based on a one-dimensional convolutional neural network (1D-CNN). The model’s performance was evaluated and compared with other supervised learning architectures. Overall, the ChrNet showed highest performance among the 3 models we benchmarked. In addition, we demonstrated the advantages of our new model over unsupervised clustering approaches using gene expression profiles from healthy, and tumor infiltrating immune cells. The codes for our model are packed into a Python package publicly available online on Github. Conclusions: We established an innovative chromosome-based 1D-CNN architecture to extract scRNA-seq expression information for immune cell type classification. It is expected that this model can become a reference architecture for future cell type classification methods.en_US
dc.description.noteMay 2020en_US
dc.rightsopen accessen_US
dc.subjectconvolutional neural networken_US
dc.subjectimmune cellsen_US
dc.subjectsupervised learningen_US
dc.titleA novel computational algorithm for predicting immune cell types using single-cell RNA sequencing dataen_US
dc.typemaster thesisen_US
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