Theses Doctoral

Understand Biology Using Single Cell RNA-Sequencing

Ding, Hongxu

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.


  • thumnail for DING_columbia_0054D_14878.pdf DING_columbia_0054D_14878.pdf application/pdf 11.9 MB Download File

More About This Work

Academic Units
Biological Sciences
Thesis Advisors
Califano, Andrea
Ph.D., Columbia University
Published Here
October 10, 2018