Data Analysis Department of Laboratory Medicine University of Washington.

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Transcript of Data Analysis Department of Laboratory Medicine University of Washington.

Data Analysis

Department of Laboratory Medicine

University of Washington

Data Analysis

• Assess data quality– Remove artifacts

• Identify populations• Compare with normal

– Identify abnormal populations– Quantitate and evaluate immunophenotype

• Generate report

Assess Data Quality

Detector Optimization

Negative populations entirely on scale

Degeneration

Increase SSDecrease FS

08-03307

Degeneration

Decrease in intensity for many antigens

08-03307

Viability Gate

08-03307

Viability Gate

All cells Viable cells

08-03307

Sample Exhaustion

Air in system gives rise to many spurious signalsEvent gate to exclude non-real events

Laser Delay

Fluidic instability - Monitor events over time to detect

Laser Delay

Original

Gated

Doublet Discrimination

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

Original07-04513

Example

Time07-04513

Example

Singlets07-04513

Example

Viable07-04513

Example

Determining Positivity

Determining Positivity

Incorrect Correct

07-08661

Population Identification

Cell Type Identification

Lymphocyte population identified by FS/SS gating

Cell Type Identification

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

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

Compare with Normal

Normal B cell Maturation

Wood and Borowitz (2006) Henry’s Laboratory Medicine

Follicle Center B cells

08-01359

08-03324

Follicular Lymphoma

Follicular Hyperplasia

0.1% abnormal immature B cells

ALL MRD

06-01469

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