Multiparametric flow cytometry analyses provide single-cell measurements that are critical for understanding the cellular heterogeneity both within individual tumors and across tumors. Even as the cytometry technology is rapidly advancing, approaches for analyzing complex single-cell data remain inadequate. The existing cytometry data analysis approaches are often subjective and labor-intensive processes that require users'deep understanding of the underlying cellular phenotypes. This limitation has become a critical bottleneck of single-cell analysis. My long-term goal is to develop novel computational approaches to enable objective analysis of high- -dimensional single-cell data. The overall objective of this application is to develop and apply topological methods to objectively identify the cellular hierarchy underlying flow cytometry data, and infer the dysregulation of cellular hierarchy and the drug response in patients with AML. The central idea is to consider a flow cytometry dataset as a high-dimensional point cloud of cells, and use topological methods to computationally extract the shape of the cloud. Based on the preliminary data, this shape can be used to infer the phenotypic hierarchy underlying heterogeneous populations of cells.
The specific aims are (1) develop methods to objectively identify the cellular hierarchy underlying individual AML samples;(2) develop methods to model the treatment responses of patients with AML using the changes in their cellular compositions. The proposed aims are expected to produce novel analytical methods for single-cell data, and provide insight into the dysregulation of differentiation and drug response in AML. The proposed research is innovative, because it views cellular heterogeneity as a continuum of phenotypic and functional changes with branchings, and aims to identify this continuum without prior knowledge of well-defined cell types. The proposed research is significant because it removes the bottleneck in the computational aspect of high-throughput single-cell analysis. The proposed research is expected to reshape the way in which cytometry data are analyzed, and promote the use of cytometry analysis to study the cellular heterogeneity in diverse fields.

Public Health Relevance

The proposed research is relevant to public health because it is expected to increase the understanding of cellular heterogeneity and drug response of AML. Moreover, the project will provide novel computational methods to study cellular heterogeneity using single-cell data, which is applicable to other cancers.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Biodata Management and Analysis Study Section (BDMA)
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Li, Jerry
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University of Texas MD Anderson Cancer Center
Biostatistics & Other Math Sci
Other Domestic Higher Education
United States
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Spainhour, John Christian Givhan; Qiu, Peng (2016) Identification of gene-drug interactions that impact patient survival in TCGA. BMC Bioinformatics 17:409
Aghaeepour, Nima; Chattopadhyay, Pratip; Chikina, Maria et al. (2016) A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry A 89:16-21
Han, Lina; Qiu, Peng; Zeng, Zhihong et al. (2015) Single-cell mass cytometry reveals intracellular survival/proliferative signaling in FLT3-ITD-mutated AML stem/progenitor cells. Cytometry A 87:346-56
Qiu, Peng (2015) Computational prediction of manually gated rare cells in flow cytometry data. Cytometry A 87:594-602
Xu, Yan; Qiu, Peng; Roysam, Badrinath (2015) Unsupervised Discovery of Subspace Trends. IEEE Trans Pattern Anal Mach Intell 37:2131-45
Kvistborg, Pia; Gouttefangeas, Cécile; Aghaeepour, Nima et al. (2015) Thinking outside the gate: single-cell assessments in multiple dimensions. Immunity 42:591-2
Transtrum, Mark K; Qiu, Peng (2014) Model reduction by manifold boundaries. Phys Rev Lett 113:098701
Qiu, Peng (2014) Unfold High-Dimensional Clouds for Exhaustive Gating of Flow Cytometry Data. IEEE/ACM Trans Comput Biol Bioinform 11:1045-51
Zhu, Yitan; Qiu, Peng; Ji, Yuan (2014) TCGA-assembler: open-source software for retrieving and processing TCGA data. Nat Methods 11:599-600
Wang, Zixing; San Lucas, F Anthony; Qiu, Peng et al. (2014) Improving the sensitivity of sample clustering by leveraging gene co-expression networks in variable selection. BMC Bioinformatics 15:153

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