Understand Biology Using Single Cell RNA-Sequencing
By: Hongxu Ding
Publisher: [publisher not identified]
Published: 2018
Place of publication: [New York, N.Y.?]
Language: eng
Description: This dissertation summarizes the development of experimental and analytical tools for single cell RNA sequencing (scRNA-Seq), including 1) scPLATE-Seq, a FACS- and plate-based scRNASeq platform, which is accurate, robust, fully automated and cost-efficient; 2) metaVIPER, an algorithm for transcriptional regulator activity inference based on scRNA-Seq profiles; and 3) iterClust, a statistical framework for iterative clustering analysis, especially suitable for dissecting hierarchy of heterogeneity among single cells. Further this dissertation summarizes biological questions answered by combining these tools, including 1) understanding inter- and intra-tumor heterogeneity of human glioblastoma; 2) elucidating regulators of β-cell de-differentiation in type-2 diabetes; and 3) developing novel therapeutics targeting cell-state regulators of breast cancer stem cells.
Notes: Department: Biological Sciences.

Thesis advisor: Andrea Califano.

Thesis (Ph.D.)--Columbia University, 2018.