Single-cell genomics, rooted in single-cell sequencing, has great potential for providing insight into fundamental questions in biomedical science and drive new health science discoveries, such as: How many cell types and functional states are there in a given tissue? What is the range of natural variation within a cell type and how is such variability affected by genetic and environmental factors? What happens at the single-cell level during cell fate determination in the developmental process? How does cellular heterogeneity within a tumor affect response to therapy and how can we address this with precision medicine? The list is endless. However, the explosive growth of single-cell sequencing technologies also brings new computational challenges. One major challenge is the “sparse read counts data”: because of the minuscule amount of genetic material in a single cell, fragments of the genome are often missing from the sequencing read-out, and existing tools are ineffective in addressing this missing-data problem and piecing together reliable genomic information.
The research team will establish a Michigan Center for Single-Cell Genomic Data Analytics, and connect mathematicians and data scientists with biological researchers to develop, evaluate, and implement a variety of cutting-edge methodologies in sparse data analysis. These methodologies will address issues in data normalization, batch effect detection and correction, marker selection, classification, rare class identification, differential expression, network and phylogenetic inference, develop tools for cyclic or time-series data, and enable information integration across data types. The team will apply these methodologies to four biological questions to test their utility: 1) Intra-tumor heterogeneity, cancer stem cells in metastasis and treatment resistance, cancer genome evolution; 2) spermatogenesis as a model for cell fate determination during development; 3) transcriptional complexity and gene regulation at the single-cell level; 4) molecular changes at the single-cell level as a result of environmental exposures and windows of susceptibility.
The outcome from this research project will have much broader impact on biomedical research beyond the four research areas that will be used as test cases. Sparse data analytics also has wide application beyond health sciences. For example, Electronic Health Records are inherently sparse, as are consumer data (purchasing, rating, or video viewing habits), location and usage data of mobile devices, connectivity in social networks, medical imaging or land imaging by satellites. In short, this line of research is conceptually connected with many areas of active research in data science and will produce general-purpose tools for many research areas.
2019 Schedule of Events- All meetings are in Weiser Hall 619, 2-3:30 pm, unless indicted otherwise
|May 24, 2019||Hongjiu Isoform imputation for single-cell RNAseq data using Seekmer|
|May 31, 2019||
Qianhui Huang, Evaluation of Computational Methods to Deconvolute Cell Types in Single-Cell Transcriptomics data
|June 7, 2019||Justin, High-content imaging of single cells|
|June 14, 2019||Lulu (Xiang’s group), Leveraging Gene Co-expression Pattern to Infer Trait-Relevant Tissues in Genome-wide Association Studies|
|June 21, 2019||Daniel (Yang Chen’s group), Probabilistic Single-Cell Data Integration|
|June 28, 2019||Alex (Anna’s group), Comparison of marker selection methods for high throughput scRNA-seq data|
|July 5, 2019||Yutong Wang, Integration of spatial and dissociated single-cell data for estimating anatomical information|
|July 19, 2019||Tasha (Justin’s group), Characterizing differences in normal breast stem cell biology between African American and European American women using single-cell analyses|
|July 26, 2019||Umang (Anna’s group), A Paucity of Data in Machine Learning: Applications in Single-Cell RNA Sequencing and Ranking|
|August 2, 2019||Mark Robinson seminar at 2 pm, Forum Hall, Statistical methods for flexible differential analysis of cross-sample single-cell RNA-seq datasets|
|September 6, 2019||Xianing Zheng, journal blub (Bonnie Berger papers)|
|September 20, 2019||Adrienne Shami “Comparative analysis of human, macaque, and mouse testes reveals conserved and divergent features of mammalian spermatogenesis at single cell resolution”
and Jun Li “Single-cell spatial analysis program”
|September 27, 2019||Yutong Wang, Journal club (Smita’s publications)|
|October 4, 2019||Smita Krishnaswamy (Yale), 3 pm, Forum Hall, Individual meetings on Oct 7.|
|October 10, 2019||Hanchuan Peng, Allen Institute for Brain Science, LSI Seminars, “Industrial-level full neuron morphology screening of whole brains”, noon Forum Hall. Individual meetings on Oct 11.|
|October 11, 2019||Jeff Regier (https://regier.stat.lsa.umich.edu), new faculty in Statistics|
|October 18, 2019||Can meet if someone runs it (Jun away, ASHG 2019)|
|October 25, 2019||Hengshi Yu (student in Josh Welch lab)|
|November 1, 2019||Julie Deeke (student in Johann Gagnon-Bartsch lab)|
|November 8, 2019||Can meet if someone runs it (Jun away to Houston)|
|November 15, 2019||No meeting, MIDAS Symposium on 11/14-15|
|November 19 (not a Friday)||Gerald Guon (UC Davis), 2 pm, Forum Hall|
|November 22, 2019||Hojae Lee (student in Josh Welch lab)|
|November 29, 2019||Day after Thanksgiving, No meeting|
|December 6, 2019||Nigel Michki (student in Dawen Cai’s group)|
|December 13, 2019||Hyun Min Kang, faculty in Biostatistics|
|December 20, 2019||Likely no meeting: too close to end of semester|
Wang, Yutong, Tasha Thong, Venkatesh Saligrama, Justin Colacino, Laura Balzano, and Clayton Scott. 2019. “A Gene Filter for Comparative Analysis of Single-Cell RNA-Sequencing Trajectory Datasets.” BioRxiv, May, 637488. https://doi.org/10.1101/637488.
Thong, Tasha, Yutong Wang, Michael D. Brooks, Christopher T. Lee, Clayton Scott, Laura Balzano, Max S. Wicha, and Justin A. Colacino. 2020. “Hybrid Stem Cell States: Insights Into the Relationship Between Mammary Development and Breast Cancer Using Single-Cell Transcriptomics.” Frontiers in Cell and Developmental Biology 8. https://doi.org/10.3389/fcell.2020.00288.
Dr. Sue Hammoud and the Michigan Center for Single- Cell Genomic Data Analytics team received grants from the Open Philanthropy Project ($2.5 million) and Chan Zuckerberg Initiative ($1.2 million).
- 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
- 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.
- 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.
The Center holds annual symposia to highlight research in single-cell data analytics going on at U-M and around the world:
3D density map of 13,000 germ cells, as distributed in their gene expression PC1-PC2 space. Regions of higher cell density are shown as taller peaks. By Sue Hammoud, Chris Green, Qianyi Ma, Jun Li.