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Dr. Yu “Max” Qian is an associate professor specializing in single cell cytometry informatics. After PhD graduation, he joined the University of Texas Southwestern Medical Center at Dallas (UT Southwestern) as a postdoctoral senior research associate, where he was trained on immunology informatics, bioinformatics, and clinical informatics. He was appointed as an assistant professor of Department of Clinical Sciences and Department of Pathology of UT Southwestern in 2010. Before joining JCVI in 2013, he was leading the natural language processing project at the Parkland Center for Clinical Innovation (PCCI), Parkland Health and Hospital System at Dallas on real-time disease identification for re-admission reduction.
As an informatics researcher, Dr. Qian leads and co-develops a suite of standards, models, algorithms, and software systems for computational cytometry data analysis, including FLOCK/ImmPort FLOCK, FCSTrans, FuGEFlow, MIFlowCyt, and GenePattern FCM suite. These systems have been used by informatics researchers and immunologists to improve the management of experiment metadata, explore cellular phenotypic profiles, identify novel cell subsets, and quantify immune responses to clinical treatments. Collaborating with UC San Diego, Stanford, UC Irvine, and San Diego Supercomputer Center, he is leading the technical development of a computational infrastructure for improving precision diagnostics of certain types of blood cancers through identifying cell-based biomarkers from polychromatic flow cytometry (FCM) experiment data. He was one of the previous leading developers of the FCM component of ImmPort, the NIAID/DAIT-funded immunology database and analysis portal. He has been customizing analytical pipelines and performing computational analytics of big FCM data for multiple NIH-funded research projects, including the Respiratory Pathogens Research Center (RPRC) at University of Rochester and the Human Immunology Project Consortium (HIPC) center at La Jolla Institute for Allery and Immunology.
Dr. Qian had been a teaching assistant of UT Dallas for Computer Science classes on computer graphics, machine learning and natural language processing, software architecture, and computer architecture. He also taught classes at the Pathology Department of UT Southwestern on applied bioinformatics and DNA microarray data analysis.
Dr. Qian received his PhD degree in Computer Science from the University of Texas at Dallas (UT Dallas) in 2006, and an ME and a BS degree in Computer Science from Nanjing University, in 2001 and 1998, respectively.
Patents
- Patent US9147041 B2: Clinical dashboard user interface system and method, Parkland Center for Clinical Innovation, with Amarasingham R., et al., filed on 13 Sept 2012, granted on 29 Sept 2015.
- Patent US9536052 B2: Clinical predictive and monitoring system and method, Parkland Center for Clinical Innovation, with Amarasingham R., et al., filed on 13 Sept 2012, granted on 03 Jan 2017.
Related Information
Research Priorities
- Computational methods for single cell flow, imaging, and mass cytometry data processing and analysis
- Design and implementation of cyberinfrastructures for immunology data management and analysis
- Integration of cytometry and RNA-seq data for cell type specific gene expression profiling
- Text mining and data classification methods for disease identification from clinical notes
- Computational methods to automatically scrub protected health information from clinical notes
- Graph-based management and exploration of cell type knowledge for biomarker discovery
Publications
Publications
Bioinformatics (Oxford, England). 2022-10-14; 38.20: 4735-4744.
FastMix: a versatile data integration pipeline for cell type-specific biomarker inference
JCI insight. 2021-04-08; 6.7:
A system-view of Bordetella pertussis booster vaccine responses in adults primed with whole-cell versus acellular vaccine in infancy
Cytometry. Part A : the journal of the International Society for Analytical Cytology. 2020-03-01; 97.3: 296-307.
Machine Learning of Discriminative Gate Locations for Clinical Diagnosis
Cytometry. Part A : the journal of the International Society for Analytical Cytology. 2018-06-01; 93.6: 597-610.
DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data
The Journal of allergy and clinical immunology. 2018-02-01; 141.2: 775-777.e6.
Allergen-specific immunotherapy modulates the balance of circulating Tfh and Tfr cells
Journal of immunology (Baltimore, Md. : 1950). 2017-02-15; 198.4: 1748-1758.
An Integrated Workflow To Assess Technical and Biological Variability of Cell Population Frequencies in Human Peripheral Blood by Flow Cytometry
Frontiers in immunology. 2017-01-01; 8.858.
Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility Complex Multimer-Binding T Cells
Scientific reports. 2016-02-10; 6.20686.
Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium
Cytometry. Part A : the journal of the International Society for Analytical Cytology. 2016-01-01; 89.1: 16-21.
A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes
Cytometry. Part A : the journal of the International Society for Analytical Cytology. 2016-01-01; 89.1: 71-88.
Mapping cell populations in flow cytometry data for cross-sample comparison using the Friedman-Rafsky test statistic as a distance measure
Source code for biology and medicine. 2013-07-03; 8.1: 14.
GenePattern flow cytometry suite
Nature methods. 2013-03-01; 10.3: 228-38.
Critical assessment of automated flow cytometry data analysis techniques
Cytometry. Part A : the journal of the International Society for Analytical Cytology. 2012-05-01; 81.5: 353-356.
FCSTrans: an open source software system for FCS file conversion and data transformation
Frontiers in immunology. 2012-01-01; 3.302.
Advances in human B cell phenotypic profiling
Cytometry. Part B, Clinical cytometry. 2010-01-01; 78 Suppl 1.S69-82.
Elucidation of seventeen human peripheral blood B-cell subsets and quantification of the tetanus response using a density-based method for the automated identification of cell populations in multidimensional flow cytometry data
BMC bioinformatics. 2009-06-16; 10.184.
FuGEFlow: data model and markup language for flow cytometry
Cytometry. Part A : the journal of the International Society for Analytical Cytology. 2008-10-01; 73.10: 926-30.
MIFlowCyt: the minimum information about a Flow Cytometry Experiment
Related Information
Research Priorities
- Computational methods for single cell flow, imaging, and mass cytometry data processing and analysis
- Design and implementation of cyberinfrastructures for immunology data management and analysis
- Integration of cytometry and RNA-seq data for cell type specific gene expression profiling
- Text mining and data classification methods for disease identification from clinical notes
- Computational methods to automatically scrub protected health information from clinical notes
- Graph-based management and exploration of cell type knowledge for biomarker discovery
Projects
Discriminative Machine Learning for Blood Cancer Precision Diagnostics
Our goal is to improve and utilize machine intelligence for elucidating cancer heterogeneity and disease endotypes for supporting cancer precision medicine.