Flow cytometry is a data-rich technology that plays a critical role in basic research and clinical diagnostics for a variety of human diseases. Traditionally, the majority of cytometry experiments have been analyzed visually, either by serial inspection of one or two dimensions (markers) at a time (a process termed """"""""gating"""""""", with boundaries or """"""""gates"""""""" defining cell populations of interest), or by very basic comparisons of summary statistics. Technological advances in cytometry based on atomic mass spectrometry will soon allow researchers to query up to 50 markers (as opposed to about 10 with current technology), making traditional analysis approaches untenable. This new mass cytometry technology will generate high-throughput high-dimensional datasets, opening up new avenues for single--cell biology. As a consequence, it is essential that analytical tools and statistical methods take part in this revolution to harness the full potential of the technology. We are proposing novel computational methods and software tools for both flow and mass cytometry. The impact of these tools will be to provide researchers with a set of tools that will become essential to extract meaningful information from such data. We will apply our methods to a number of different scenarios such as the identification of immune correlate of protections for HIV and malaria vaccines, the identification of genetic mechanisms of homeostasis, and the clinical prediction of chronic inflammatory conditions.
The aims of this project are to develop statistical and computational methods for the analysis of flow and mass cytometry data and apply these to large datasets issued from clinical trials. The impact of these tools will be to provide researcher with a set of tools that will become essential to extract meaningful information from such data. The domain of applications spans all diseases, and our current application covers multiple diseases including HIV, malaria and inflammatory conditions.
|Finak, Greg; Langweiler, Marc; Jaimes, Maria et al. (2016) Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium. Sci Rep 6:20686|
|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|
|Slichter, Chloe K; McDavid, Andrew; Miller, Hannah W et al. (2016) Distinct activation thresholds of human conventional and innate-like memory T cells. JCI Insight 1:|
|Hsiao, Chiaowen; Liu, Mengya; Stanton, Rick et al. (2016) Mapping cell populations in flow cytometry data for cross-sample comparison using the Friedman-Rafsky test statistic as a distance measure. Cytometry A 89:71-88|
|Spidlen, Josef; Moore, Wayne; ISAC Data Standards Task Force et al. (2015) ISAC's Gating-ML 2.0 data exchange standard for gating description. Cytometry A 87:683-7|
|O'Neill, Kieran; Aghaeepour, Nima; Parker, Jeremy et al. (2015) Deep profiling of multitube flow cytometry data. Bioinformatics 31:1623-31|
|Finak, Greg; McDavid, Andrew; Yajima, Masanao et al. (2015) MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol 16:278|
|Seshadri, Chetan; Lin, Lin; Scriba, Thomas J et al. (2015) T Cell Responses against Mycobacterial Lipids and Proteins Are Poorly Correlated in South African Adolescents. J Immunol 195:4595-603|
|Kvistborg, Pia; Gouttefangeas, CÃ©cile; Aghaeepour, Nima et al. (2015) Thinking outside the gate: single-cell assessments in multiple dimensions. Immunity 42:591-2|
|Lin, Lin; Finak, Greg; Ushey, Kevin et al. (2015) COMPASS identifies T-cell subsets correlated with clinical outcomes. Nat Biotechnol 33:610-6|
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