Michigan Center for Single-Cell Genomic Data Analytics

Summer 2018

• This Center has 10 founding members, including five biomedical research groups, and five statisticians and mathematicians, with the overall goal of building a strong data science infrastructure to examine biological heterogeneity at the single-cell level.
• One of the Center’s goals is to construct stable analysis pipelines and pass them to the bioinformatics service units. The Center aims to keep the analysis portfolio up to date, upholding scientific rigor for all studies using this technology.
• The single-cell biology community at U-M is bolstered by the recruitment of new faculty members Joshua Welch and Lana Garmire.
• The teams are currently supported by four grants from NIH (Colacino, Zhou, Li, Hammoud), and have received another four awards from the Chan Zuckerberg Foundation (Gilbert, Scott, Zhou, Welch).
• The Center will develop
  • New theoretical foundation for sparse data
  • New algorithms/methodologies
  • Best practices for reproducible data exploration
  • Standardized method evaluation and workflow engineering
  • Application on many exciting biological problems

February 2018

• The team is developing statistical methods to perform clustering for identifying cell subpopulations in single-cell RNA sequencing studies, and methods to integrate single-cell expression data into genome-wide association studies to both identify complex-trait-relevant single cell populations and use such information to improve association mapping power.
• The team is using drop-seq technique and isoform data analysis on single cell RNASeq data for normal breast cells, breast cancer cells, and other types of cancer cells.
• To unravel the molecular program of spermatogenesis, the team has adopted the drop-seq technique and has been collecting single cell data from the adult testis of mouse, monkey and human. By analyzing 33,000 cells from the mouse testis, they have identified novel cell types and rare and key developmental transitions during germ cell development.
• The team has received multiple external grants to sustain and expand their work that was jumped started by the support from MIDAS. Among these grants, four are from the Chen-Zuckerberg Foundation.

Summer 2017

• The center held a symposium Aug. 10, 2017 in Palmer Commons. Please see the symposium webpage for more details.
• Members of this research team come from 10 research labs.  Several students are now involved in the study. They have made progress to define challenges for each lab.
• In one study, the team has analyzed single-cell RNAseq data for ~13,000 mouse germ cells.  Using principal component analysis and known developmental markers to group cells, they were able to interpret the major cell types involved in spermatogenesis.  This global survey of cellular heterogeneity led to a finer-grained classification of both common and rare cell subtypes, and the known and newly discovered markers provide a tool for future experiments.
• The team has submitted three grant proposals to federal funding agencies and a private foundation.
• The team presented its work related to the MIDAS Challenge Award at the Computational Biology Workshop in Statistical Challenges in Single-Cell Biology and the Keystone Symposium in Single Cell Omics.