New Cell Type Detection via a Universal Single-Cell Gene Expression Algorithm

Overview

Detecting rare cell types would allow very early disease diagnosis (e.g., cancer, infection), identification of new cell types, and deepen our understanding of cell differentiation. We developed a universal algorithm to identify rare cell types from single-cell gene expression data. Our algorithm discovered a putative new hippocampal (brain) cell type. The state-of-the-art computational method to analyze single-cell gene expression data has serious limitations, including that it requires data from a very large number of genes. Our method overcomes this and other limitations. We validated our algorithm on three datasets which highlight the universality of the method: single-cell qPCR data from mouse hematopoietic cells, single-cell RNA-seq data from human glioblastoma tumors cells, and seq-FISH data from mouse hippocampus cells. The latter could not be analyzed using any existing algorithm, since the dataset contained only a small number of genes. Ours is the first algorithm able to analyze this data, in which we detected a putative new hippocampal cell type. Our validated method can now be applied to a wide range of genomic and transcription data to detect the early onset of diseases and discover new cell types.

Competition history

• ISEF 2018 Computational Biology and Bioinformatics · Entry CBIO023

Resources

• ISEF abstract page ↗

Related projects

Closest projects by meaning, across every fair and year in the corpus.