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Data Analysis Department of Laboratory Medicine University of Washington.

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Presentation on theme: "Data Analysis Department of Laboratory Medicine University of Washington."— Presentation transcript:

1 Data Analysis Department of Laboratory Medicine University of Washington

2 Data Analysis Assess data quality –Remove artifacts Identify populations Compare with normal –Identify abnormal populations –Quantitate and evaluate immunophenotype Generate report

3 Assess Data Quality

4 Detector Optimization Negative populations entirely on scale

5 Degeneration Increase SS Decrease FS 08-03307

6 Degeneration Decrease in intensity for many antigens 08-03307

7 Viability Gate 08-03307

8 Viability Gate All cellsViable cells 08-03307

9 Sample Exhaustion Air in system gives rise to many spurious signals Event gate to exclude non-real events

10 Laser Delay Fluidic instability - Monitor events over time to detect

11 Laser Delay Original Gated

12 Doublet Discrimination

13 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

14 Original 07-04513 Example

15 Time 07-04513 Example

16 Singlets 07-04513 Example

17 Viable 07-04513 Example

18 Determining Positivity

19 Incorrect Correct 07-08661

20 Population Identification

21 Cell Type Identification Lymphocyte population identified by FS/SS gating

22 Cell Type Identification Borowitz et al (1993) AJCP 100:534-40. Steltzer et al (1993) Ann NY Acad Sci 667:265-280

23 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

24 Compare with Normal

25 Normal B cell Maturation Wood and Borowitz (2006) Henrys Laboratory Medicine

26 Follicle Center B cells 08-01359 08-03324 Follicular Lymphoma Follicular Hyperplasia

27 0.1% abnormal immature B cells ALL MRD 06-01469

28 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

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