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National Immune Monitoring Laboratory

National Immune Monitoring Laboratory. University of Montreal, Quebec, Canada Biosystemix Ltd. Biosystemix, Ltd. Our Mission. To make a distinctive contribution to the health and well-being of people afflicted by chronic and progressive diseases.

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National Immune Monitoring Laboratory

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  1. National Immune Monitoring Laboratory University of Montreal, Quebec, Canada Biosystemix Ltd. Biosystemix, Ltd.

  2. Our Mission • To make a distinctive contribution to the health and well-being of people afflicted by chronic and progressive diseases. • To build and manage a national, world-class GLP core facility equipped with state-of-the-art technology platforms to investigate the immune status of patients at various stages of their disease. • To support national and international clinical trials by performing in-depth analysis of immune responses to novel vaccines and immunotherapies against viral and malignant diseases. • To identify correlates of immune protection. • To participate in the development of break-through therapeutic products and cutting-edge bioanalytical technologies.

  3. View of the Problem • Scaling to voluminous amounts of data. • FACS DIVA buggy, poor workflow, can it scale? • More complex experimental designs need more complex analysis.

  4. BD FACS DIVA What is working: • Acquisition • Applying a template, 10 colours (will it work for 18 colours ?) What is troubling: • GUI WORKFLOW - working with the data poor, not efficient for experiments with large numbers of tubes. • EXPORTING the data - very inefficient, batch export has trouble: number of events, graphics, slow, transformations are not well represented in the XML. • BD tech support - DIVA software support is poor.

  5. General workflow e.g., time series of 20 patients, 5 time-points, 10 stimulations at 10 colors: 1000 points x n Parameters. Main Workflow: • Acquisition (DIVA) • Apply Gating • Exporting Data (counts, statistics) • Archive Data • Organize data: rearrange data into tables for processing. • QC Report (Negative and Positive Controls etc) • Patient Analysis I: single time point, analysis between 10 stimulations • Patient Analysis II: time-series analysis for each stimulation • Summary Report I: Patient Summary • Global Analysis I: time-series analysis for 10 stimulations 20 patients 5 time-points n Parameters. • Global Analysis II: cross-platform analysis with genomics, proteomics, public data. • Summary Report II: Global Report

  6. Resolving the Problem General • New version of DIVA ????? • New tools in R, expand rflowcyt • Create libraries in biopython, bioperl,biojava, Matlab/Octave • Applications for 64 bit OS Linux/Unix or Windows 64 bit Immediate Needs • FACS DIVA, acquisition, apply template, Batch export (turn all graphs off). • Parse data into R, create data structures • R-script for applying analysis • R-script for generating report Long Term Needs • FACS DIVA only for acquisition and writing fcs files (up to 18 colours) • Read from raw FCS with another application (like flowcyt). • If Gating can be done in DIVA, use XML • If Gating in flowcyt or other R package, then gating template must be made outside. • More precise statistically supported gating strategies. • Analysis ….

  7. Quantification and discrimination of multiple cell populations in 2-dimensional FACS data using realistic assumptions of distributional properties for high-throughput applications at the Canadian NIML • For its high-throughput FACS applications, the NIML could benefit from • precise and statistically supported identification and quantitation of cell populations • definition of curved cell-sorting separation boundaries. • This should increase the quality and speed of the boundary decision process, and automate quantitation of cell numbers in each population. • For 2-color analysis, we could benefit from quantitative methods that correctly capture the 2-dimensional, usually bell-shaped or Gaussian distributions of cells. • Biosystemix can provide algorithms that can fit multiple distributions for multiple cell types, and for accurately quantitating numbers of cells under each distribution. • Using, e.g., QDA (quadratic discriminant analysis), we can also draw the boundaries for cell sorting at a higher resolution, instead of using the oversimplification of horizontal and vertical lines as currently practiced. • The issue of data processing speed will become more acute not only with large sample numbers, but particularly with increasing numbers of sorting dimensions (markers and colors). Biosystemix, Ltd.

  8. Biggest Frustrations • FACS DIVA, Tech-support. • How to make use of the tools • run by bioinformaticians • train FACS operators to use R • new GUIs need to be created. • Scaling up. • Need for up to date analysis methods

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