Data Analysis Department of Laboratory Medicine University of Washington.
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![Page 1: Data Analysis Department of Laboratory Medicine University of Washington.](https://reader037.fdocuments.in/reader037/viewer/2022103111/5519bdd255034660578b4a66/html5/thumbnails/1.jpg)
Data Analysis
Department of Laboratory Medicine
University of Washington
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Data Analysis
• Assess data quality– Remove artifacts
• Identify populations• Compare with normal
– Identify abnormal populations– Quantitate and evaluate immunophenotype
• Generate report
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Assess Data Quality
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Detector Optimization
Negative populations entirely on scale
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Degeneration
Increase SSDecrease FS
08-03307
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Degeneration
Decrease in intensity for many antigens
08-03307
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Viability Gate
08-03307
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Viability Gate
All cells Viable cells
08-03307
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Sample Exhaustion
Air in system gives rise to many spurious signalsEvent gate to exclude non-real events
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Laser Delay
Fluidic instability - Monitor events over time to detect
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Laser Delay
Original
Gated
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Doublet Discrimination
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Doublet Discrimination• Doublets = > one cell in laser simultaneously
– High cell concentrations– Cell aggregates, sample preparation– High sample aspiration pressure
• Doublets have composite properties
• Can exclude using height, area, or width
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Original07-04513
Example
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Time07-04513
Example
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Singlets07-04513
Example
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Viable07-04513
Example
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Determining Positivity
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Determining Positivity
Incorrect Correct
07-08661
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Population Identification
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Cell Type Identification
Lymphocyte population identified by FS/SS gating
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Cell Type Identification
Borowitz et al (1993) AJCP 100:534-40.Steltzer et al (1993) Ann NY Acad Sci 667:265-280
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Lineage Identification
– CD19 for B cells and CD3 for T cells– Assumptions that may not always be correct– Always use at least two methods of identification
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Compare with Normal
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Normal B cell Maturation
Wood and Borowitz (2006) Henry’s Laboratory Medicine
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Follicle Center B cells
08-01359
08-03324
Follicular Lymphoma
Follicular Hyperplasia
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0.1% abnormal immature B cells
ALL MRD
06-01469
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Data Analysis
• Data displayed as dot plots or histograms– Restrict to subset having high informational content
• Color discrete populations – Display information from other parameters– Allow rapid visual identification in multiple plots
• Display data in consistent manner– Pattern recognition