Transcript National Immune Monitoring Laboratory

National Immune Monitoring
Laboratory
University of Montreal, Quebec, Canada
Biosystemix Ltd.
National Immune Monitoring Laboratory
Biosystemix, Ltd.
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 indepth 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.
National Immune Monitoring Laboratory
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.
National Immune Monitoring Laboratory
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.
National Immune Monitoring Laboratory
General workflow
e.g., time series of 20 patients, 5 time-points, 10 stimulations at 10 colors:
1000 points x n Parameters.
Main Workflow:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
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 timepoints n Parameters.
11. Global Analysis II: cross-platform analysis with genomics, proteomics, public
data.
12. Summary Report II: Global Report
National Immune Monitoring Laboratory
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 ….
National Immune Monitoring Laboratory
Quantification and discrimination of multiple cell populations in 2-dimensional
FACS data using realistic assumptions of distributional properties for highthroughput 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.
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
National Immune Monitoring Laboratory