Projects

*

An unsupervised manifold alignment algorithm, MMD-MA, for integrating multiple measurements carried out on disjoint aliquots of a given population of single cells.

The first computational embedding method for single cells in terms of their chromatin organization.

A graphical model based multiple testing procedure which captures dependence among the hypotheses.

Our results show that radiologists can potentially use genetic variants (SNPs) to improve personalized breast cancer diagnosis (Marco Ramoni Distinguished Paper Award).

Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies (Supported by Moore-Sloan Data Science Environment Fellowship).

Publications

(2019). Jointly embedding multiple single-cell omics measurements. In WABI.

Preprint Project

(2019). Systematic proteomics of endogenous human cohesin reveals an interaction with diverse splicing factors and RNA binding proteins required for mitotic progression. In Journal of Biological Chemistry.

PDF Project

(2018). Unsupervised embedding of single-cell Hi-C data. In ISMB.

Project Source Document

(2017). Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies. In Scientific Reports.

PDF Code Project

People

Principal Invistigator

Avatar

Jie Liu

Assistant Professor

Students

Avatar

Fan Feng

PhD student
DCMB

Avatar

Shuze Wang

PhD student
DCMB
co-advised with Dr. Joerg Waldhaus

Avatar

Yuanhao Huang

Master student
DCMB

Avatar

Mingyu Du

Master student
Biostatistics

Avatar

Yufeng Zhang

Master student
Biostatistics

Avatar

Yujuan Fu

undergraduate student
EECS

Avatar

Zheyu Zhang

Visiting undergraduate student
Statistics
CUHK Shenzhen

Contact