Normalization Methods in Single-cell RNA Sequencing
When
4 p.m., Feb. 19, 2019
Speaker
Abstract
Through gene sequencing experiments, researchers can analyze the genetic content of tumors or developing embryos and better understand the importance of particular genes during stages of development. Single-cell RNA-sequencing (scRNA-seq) provides a means to assess transcriptomic variations among individual cells, rather than over the tumor as a whole, giving an advantage over bulk sequencing methods that fail to detect subgroups and rare cell types. However, restrictions such as amplification bias, technical noise, and dropout events often limit the power of scRNA-seq results. To address these issues, various normalization methods have been developed that correct observed gene counts to account for existing noise and more accurately represent the true biological signal of interest. Eliminating technical noise and amplification error often involves the use of a set of exogenous genes injected into the cell in known quantities, referred to as “spike-in genes”. By statistically modeling the difference between observed gene counts and known gene counts, the resulting model can then apply to all other genes present in the cell, adjusting observed gene counts accordingly. I propose a novel scRNA-seq normalization method that normalizes between a data set’s groups while also using dropout imputation to adjust for missing values. I compare this method with existing normalization approaches, using real data sets to support my results.