Transcript BMS 631 - LECTURE 7 Flow Cytometry: Theory J.Paul Robinson

BMS 633/BME 695Y - Week 3
Detectors, Electronics, Data Analysis
J. Paul Robinson
Professor of Immunopharmacology
School of Veterinary Medicine, Purdue University
The WEB version of these slides can be found on
http://tinyurl.com/385ss
Hansen Hall, B050
Purdue University
Office: 494 0757
Fax 494 0517
email: [email protected]
WEB http://www.cyto.purdue.edu
Material is taken from the course text: Howard M. Shapiro,
Practical Flow Cytometry, 3nd edition (1994), 4th Ed (2003)
Alan R. Liss, New York.
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
3rd Ed. Shapiro p127-133
4th Ed. Shapiro p160-256
Learning goals
• Students will lean about the nature of
detection systems of flow cytometry
– Their use, characteristics, benefits and
problems
– The types of detection systems used
– The way data points are collected and used
– The principles of data analysis and reporting
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Detectors
• Light must be converted from photons into
volts to be measured
• We must select the correct detector system
according to how many photons we have
available
• In general, we use photodiodes for scatter,
and absorption and PMTs for fluorescence
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Characteristics of Light Detection
Red sensitive
PMT
UV line
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Silicon photodiodes
• A silicon photodiode produces current when photons impinge
upon it (example : solar cells)
• Does not require an external power source to operate
• Peak sensitivity is about 900 nm
• At 900 nm the responsivity is about 0.5 amperes/watt, at 500
nm it is 0.28 A/W
• Are usually operated in the photovoltaic mode (no external
voltage) (alternative is photoconductive mode with a bias
voltage)
• Have no gain so must have external amps
• quantum efficiency ()% = 100 x ((electrons out)/(photons in))
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
PMT
• Produce current at their anodes when photons impinge upon their lightsensitive cathodes
• Require external powersource
• Their gain is as high as 107 electrons out per photon in
• Noise can be generated from thermionic emission of electrons - this is called
“dark current”
• If very low levels of signal are available, PMTs are often cooled to reduce heat
effects
• Spectral response of PMTs is determined by the composition of the
photocathode
• Bi-alkali PMTs have peak sensitivity at 400 nm
• Multialkali PMTs extend to 750 nm
• Gallium Arsenide (GaAs) cathodes operate from 300-850 nm (very costly and
have lower gain)
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Signal Detection - PMTs
Secondary emission
Cathode
Anode
Amplified
Signal
Out
Photons
in
End
Window
Dynodes
• Requires Current on dynodes
• Is light sensitive
• Sensitive to specific wavelengths
• Can be end`(shown) or side window PMTs
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Photomultiplier tubes (PMT’s)
The PMTs in an Elite. 3 PMTs are shown, the other 2
have been removed to show their positions. A diode
detector is used for forward scatter and a PMT for
side scatter.
© J.Paul Robinson
© J.Paul Robinson
The Bio-Rad Bryte cytometer uses PMTs
for forward and wide angle light scatter as
well as fluorescence
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
PMT
© J.Paul Robinson
PMTs
• High voltage regulation is critical because the relationship
between the high voltage and the PMT gain is non-linear
(almost logarithmic)
• PMTs must be shielded from stray light and magnetic
fields
• Room light will destroy a PMT if connected to a power
supply
• There are side-window and end-window PMTs
• While photodiodes are efficient, they produce too small a
signal to be useful for fluorescence
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
High Voltage on PMTs
• The voltage on the PMT is applied to the dynodes
• This increases the “sensitivity” of the PMT
• A low signal will require higher voltages on the PMT to
measure the signal
• When the voltage is applied, the PMT is very sensitive
and if exposed to light will be destroyed
• Background noise on PMTs is termed “dark noise”
• PMTs generally have a voltage range from 1-2000 volts
• Changing the gain on a PMT should be linear over the
gain range
• Changing the voltage on the PMT is NOT a linear
function of response
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Diode Vs PMT
• Scatter detectors are frequently diode detectors
Sample stream
Back of Elite forward scatter detector
showing the preamp
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Front view of Elite forward scatter detector
showing the beam-dump and video camera
signal collector (laser beam is superimposed)
Spectral Imaging
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Review of Electronics
• Based on Ohm’s Law, the flow of a current of 1 Amp through a material
of resistance of R ohms () produces a drop in electrical potential or a
voltage difference of V volts across the resistance such that V=IR
• DC - direct current - the polarity of a current source remains the same
when the current is DC
• AC - Alternative current - this is generated by using a magnetic field
(generator) to convert mechanical into electrical energy - the polarity
changes with motion
V(t) = Vmax sin (2ft)
• A wire loop or coil exhibits inductance and responds to alternative
current in a frequency dependent fashion.
• AC produces a changing magnetic field - generates a voltage opposite
in polarity to the applied voltage
• In an inductance of 1 Henry (H) on a voltage of 1 volt is induced by a
current changing at the rate of 1 Amp/second - this property is called
reactance
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Review of Electronics
• Reactance like resistance provides an impediment to the flow of current, but
unlike resistance is dependent on the frequency of the current
• If a DC current is applied to a capacitor a transient current flows but stops
when the potential difference between the conductors equals the potential of
the source
• The capacitance measured in Farads (F) is equal to the amount of charge on
either electrode in Coulombs divided by the potential difference between the
electrodes in volts - 1 Farad = 1 coulomb/volt
• DC current will not flow “through” a capacitor - AC current will and the
higher the frequency the better the conduction
• In a circuit that contains both inductance and capacitance, one cancels the
other out
• The combined effect of resistance, inductive reactance and capacitive
reactance is referred to as impedance (Z) of the circuit
• Impedance is not the sum of resistance and reactance
• z=(R2+(Xl-Xc)2)½ (Xl = inductive reactance, Xc = capacitive reactance)
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
The Coulter Principle
• Cells are relatively poor conductors
• Blood is a suspension of cells in plasma which is a
relatively good conductor
• Previously it was known that the cellular fraction of
blood could be estimated from the conductance of
blood
• As the ratio of cells to plasma increases the
conductance of blood decreases
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
The Coulter Principle
•2 chambers filled with a conductive
saline fluid are separated by a small
orifice (100m or less)
•Thus, most of the resistance or
impedance is now in the orifice.
•By connecting a constant DC
current between 2 electrodes (one in
each chamber), the impedance
remains constant. If a cell passes
through the orifice, it displaces an
equivalent volume of saline and so
increases the impedance.
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Electrical Opacity
• This is similar to impedance, except that you use
an AC current across the electrodes of a coulter
cell
• When the frequency used is in the radio frequency
range (RF) the parameter measured is known as
electrical opacity
• This reflects the AC impedance of cells and is
dependent on cellular structure and less on size
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Linear and Log circuits
•
•
•
•
Linear circuits
Logarithmic circuits
Dynamic range
Fluorescence compensation
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Why use linear amps?
• The problem with compensation is that it needs to be performed on
linear data, not logarithmic data. Thus, either the entire electronics
must be built in linear electronics, which requires at least 16 bit A-D
converters, or a supplementary system must be inserted between the
preamp and the display.
• We need the dynamic range for immunologic type markers, but we
can’t calculate the compensation easily using log amps - certainly not
without complex math.
• Flow cytometers amplify signals to values ranging between 0-10V
before performing a digital conversion.
• Assuming this, with 4 decades and a maximum signal of 10 V we
have:
Factor reduction 10
pulse output
1v
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
100
1000
10000
100mv 10mv 1mv
Why use linear amps?
• The problem with compensation is that it needs to be performed on
linear data, not logarithmic data. Thus, either the entire electronics
must be built in linear electronics, which requires at least 16 bit A-D
converters, or a supplementary system must be inserted between the
preamp and the display.
• We need the dynamic range for immunologic type markers, but we
can’t calculate the compensation easily using log amps - certainly not
without complex math.
• Flow cytometers amplify signals to values ranging between 0-10V
before performing a digital conversion.
• Assuming this, with 4 decades and a maximum signal of 10 V we
have:
Factor reduction 10
pulse output
1
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
100
100mv
1000
10mv
10000
1mv
How many bits?
• Assume we convert linear analog signals using an 8 bit
ADC - we have 256 channels of range (2n) (28-256)
corresponding to the range 0-10 V
• Channels difference is 10/256=40mV per channel
1V
10V
100mV
0
50
100
150
Channels
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
200
250
Ideal log amp
1V
10 V
100 mV
Linear
0
Log amp
1 mV
50
100
10 mV
150
100 mV
200
1V
250
10 V
Log
0
50
100
150
Channels
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
200
250
Log amps & dynamic range
Compare the data plotted on a linear scale (above) and a 4 decade log scale (below). The date are
identical, except for the scale of the x axis. Note the data compacted at the lower end of the the
linear scale are expanded in the log scale.
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Log/lin display
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Ratio circuits
• Ratio circuits are analog circuits which produce an output
proportional to the ratio of the 2 input signals.
• They are usually made from modules called analog multipliers.
• Examples are calculation of surface density or antigenic receptor
sites by dividing the number of bound molecules by the cell
surface area.
• e.g. Could use 2/3 power of volume to obtain surface area - but
few cytometers make this parameter so can use the square of the
cell diameter of scatter instead to approximate.
• pH can also be measured using ratio circuits
• Calcium ratio (using Indo-1 we can ratio the long and short l)
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Data Acquisition
• operations which are required to make measurements
of a specified physical characteristic(s) of cells in sample
• Each measurement from each detector is referred to as a
variable or “parameter”
• Data are acquired as a “list” of the values for each variable
(“parameter” ) for each event (“cell”)
• Purpose is to store data
• And to convert data to numerical form
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
System management
Operational Steps
1. Sample Preparation
2. Data Acquisition
3. Data analysis
4. Data Reporting
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
}
We will only deal with
these in this lecture
Data Analysis
Issues to define
•Data acquisition vs. data analysis
•Data analysis software
•Data display
•Establishing Regions of Interest (ROI) and gating
•Analysis methods that can change results
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Data Analysis
Main tasks
•
•
•
•
•
•
•
Cell counting
Population discrimination
A-D conversion of data
Dynamic range must be appropriate
DSP for pulses if appropriate
Data rates and data acquisition
Preprocessing for data acquisition
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Data Analysis
Output goals
•
•
•
•
•
•
Frequency Distributions
Distributions (Gaussian/normal)
Statistical components
Skewness and Kurtosis
Compensation/crosstalk
Reporting
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Data Analysis
• Histograms
– Comparing histograms
• K-S
• Cumulative (Overton) subtraction
• constant CV analysis
• Bivariate displays
– dot plots
– linear regression/Least-squares fits
– Isometric (2 parameter histogram)
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Flow Cytometry Computer Files
•Listmode files
-correlated data file where each event is listed sequentially,
parameter by parameter
-large file size
•Histogram files
-uncorrelated data used for display only
•Flow cytometry standard (FCS 2.0, FCS 3.0)
-format used to save data
-use other software programs to analyze data
Note: No cytometry manufacturer abides strictly by the FCS standard
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Data Analysis Software
Instrument Software
Elite 4.0
Bryte HS 2.0
Lysis II
Commercial Sources
WinList & Modfit LT
ListView & Multicycle
FloJo
FCS Express
Flow Explorer
Free Flow Software
WinMDI
MFI
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Coulter
Bio-Rad
Becton Dickinson
Verity Software
Phoenix Software
Treestar Software
Ray Hicks
Ron Hoebe
Joe Trotter
Eric Martz
WinMDI
WinMDI or Windows Multiple Document Interface
-requires Windows 3.1, Windows 95,
Windows NT or OS/2
Developed by Joe Trotter at the Scripps Institute
Available FREE from Internet:
http://facs.scripps.edu/software.html
Excellent Tutorial developed by Dr. Gerald Gregori
http://www.cyto.purdue.edu/flowcyt/labinfo/labinfo.htm
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Precision - C.V.
•
•
•
•
•
•
•
Precision: CV
Sensitivity
MESF Units
Accuracy and Linearity
Noise
Background
Laser noise
Shapiro’s 7th Law of Flow Cytometry:
No Data Analysis Technique Can Make
Good Data Out of Bad Data!!!
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Data Acquisition - Listmode
Event Param1 Param2 Param3 Param4
FS
SS
FITC
PE
1
2
3
4
5
6
59
58
54
66
112
115
100
110
60
60
60
60
n
etc
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
80
150
80
80
80
80
90
95
30
30
30
30
Statistical Calculations
Number of events – we always collect this
Mean:
• is a measure of central tendency
Standard Deviation:
• is a measure of variability
Coefficient of Variation
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
One parameter (frequency) histogram
# of events for
particular
parameter
establish regions and calculate coefficient of variation (cv)
cv = st.dev/mean of half peak
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Coefficient of Variation
%CV Definition =
St.Dev x 100
MEAN
CV=3.0
CV=3.0
MEAN
Crucial in establishing:
• alignment
• Fluidic stability
• Staining of cells
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Coefficient of Variation
Calculation
Statistical
(Subjective)
•
•
•••••••••
• •••
••••
•••
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Formula
(not boundary
dependent
Objective)
Least-Squares
(Accurate, nonsubjective)
Histogram Comparisons
The question here might be:
Is there a difference between
these two data sets?
We compare histograms to determine if there is a difference between them. If
there is, we can make a statement of difference based on statistics. Since we
are usually measuring biological phenomena, our conclusion will be related to
the biological difference perhaps.
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Kolmogorov-Smirnov
K-S Test
Fluorescnece Intensity
100
50
0
50
100
Channel Number
50
100
A good technique for estimating the differences between histograms
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Histogram Analysis
Normalized Subtraction
Match region
False Negatives
• Very accurate
• Assumption that control & test histogram are same shape
• Match region finds best amplitude of control to match test
histogram
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Histogram Analysis
Integration
Frequency
“Positive” histogram
False Negatives
False Positives
• Very subjective analysis
• Not easily automated
• Not good for weakly fluorescent signals
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Histogram Analysis
Accumulative Subtraction
Negative Control
Cumulative Events
Number of Events
Actual
Negatives
Test
Actual
Positives
• Very accurate
• Assumption that control & test histogram are same shape
• Match region finds best amplitude of control to match test histogram
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Basic Histogram Operations
Gating or Region of Interest (ROI) selection
• 1. A gate is a region of interest
• Gates can be applied to any histogram
• Gates or ROI can also be applied to multparameter plots
• Gates are applied to select out cells with a desired
characteristic.
• Gates can be additive – this means the results are
compounded in the data analysis
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Gating Example
Total cells -5000
We have here a histogram
By definition it is single
parameter
Gate M1 determines a
region from point A
to point B on the X
axis (log FITC)
A
B
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Within the boundaries of AB, the gate M1 gives is the
total number of cells within
the range A-B – the number
of cells is 4900
Gating Example
Total cells -5000
We have here a histogram
By definition it is single
parameter
Gate M2 determines a
region from point A1
to point B1 on the X
axis (log FITC)
M2
A1
B1
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Within the boundaries of
A1-B1, the gate M2 gives is
the total number of cells
within the range A1-B1
which is 4,700
Multiple Gates
Total cells -5000
M1
M3
M4
Any number of gates can be
applied to a histogram. Gates
can be inclusive, exclusive or
“either or”.
M5
M6
M2
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
For example, you could select
all cells that satisfy gate M6,
excluding gate M3 – (M6-M3)
would give you the same result
as adding gates M1 and M4
(M1+M4).
Multiple parameter displays
Following display are important in flow
•Dot plot
• Density dot plot
• Contour plot
• Isometric plot
•3D projection
•Complex displays – TIP and TIG displays
Note: TIP – Tube identifier Parameter – allows the display of data points for multiple samples
TIG: Time Interval Gating – allow the display of multiple samples over time.
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Isometric Plot - 3 Parameter view
- simulated surface is created
- # of particles used as 3rd parameter
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
- 2 parameter data plus cell number
- 3-D space
Density Dot Plot
A
A: Color of dots gives an indication of the identify of
subpopulations. e.g. in the above plot the green dots are
high density and the mauve are low density areas (FS is
Forward Scatter and 90ls is Ninety Degree light scatter or
orthogonal light scatter.)
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Contour Plot
B
B: The color of lines in each contour provides an indication
of the number of events in that level of the plot. e.g. in the
above plot the green are high density and the mauve are low
density with proper contour lines. The data sets of A and B
are identical.
More displays
Color coded dot plots
In this display, each population has
been identified by a different color
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Here, the multiple colors are in the
lymphocyte gate. All of the se cells are
identified on the left plot. When applied
to the scatter plot, there is a region with
multiple colors.
Kinetic Analysis
2 D plots
50 ng PMA
Stimulated
0 ng PMA
Unstimulated
0
450
900
1350
1800
TIME (seconds)
0
450
900
1350
1800
TIME (seconds)
Figure 9.3.4 This figure shows an example of stimulation of
neutrophils by PMA (50 nm/ml). On the left the unstimulated
cells show no increase in DCF fluorescence . On the right, activated
cells increase the green DCF fluorescence at least 10 times the
initial fluorescence.
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Some Multi-data display formats
--- --+ -++ -+- +-- +-+ ++- +++
CD45
CD4 CD8
CD8
leu11a
Mo1
CD20
FITC Fluorescence
Multiple histograms displayed in
a combination format
J. Paul Robinson, K.Ragheb, G. Lawler,S.Kelley, & G. Durack: Rapid
Multivariate Analysis and Display of cross-reacting antibodies on Human
Leukocytes. Cytometry 13:75-82,1992
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
This is the “Phenogram” format which displays
all of the possible binary combinations of a set
of fluorochromes – in this case there are 3 colors
(n) so there are 2n =8 combinations.
Robinson, J. Paul, Durack, Gary & Kelley, Stephen: "An innovation in
flow cytometry data collection & analysis producing a correlated multiple
sample analysis in a single file". Cytometry 12:82-90,1991.
The first distribution
demonstrates forward gating.
Cell fluorescence is gated
based on their scatter
characteristics. Below
fluorescence is used to
“backgate” the fluorescence
signal onto the scatter dotplot
Forward gate
log PE
Back gate
1P Fluorescence
2P Fluorescence
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
2P Scatter
Specific Cases - DNA analysis
Doublet Discrimination
8 x 125  m laser beam shape
Peak Fluorescence
Peak Fluorescence
16 x 64  m laser beam shape
Clumps
Integral Fluorescence
Slide 18, 11/11/96 of DNA.ppt
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Integral Fluorescence
From Duque et al, Clin.Immunol.News.
APRE-BV
PRE-BIV
Mu
Negative
Positive
PRE-BIII
PRE-BII
CD20
AUL
PRE-BI
CD10
TdT
AMLL
AML
AML-M3
?
CD19
B,T
CD13,33
T-ALL
CD13,33
T
HLA-DR
Decision Tree in Acute Leukemia
An example of how data analysis can result in a decision process for a
data set
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Multi-color studies
generate a lot of data
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QUADSTATS
Log Fluorescence
QUADSTATS
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Log Fluorescence
QUADSTATS
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Log Fluorescence
QUADSTATS
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10
Log Fluorescence
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QUADSTATS
Log Fluorescence
QUADSTATS
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Log Fluorescence
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QUADSTATS
Log Fluorescence
QUADSTATS
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Log Fluorescence
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QUADSTATS
Log Fluorescence
QUADSTATS
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Log Fluorescence
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QUADSTATS
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QUADSTATS
Log Fluorescence
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QUADSTATS
7
Log Fluorescence
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Log Fluorescence
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-+
5
Log Fluorescence
+-
Log Fluorescence
--
+-
QUADSTATS
-+
++
--
+-
Log Fluorescence
Log Fluorescence
QUADSTATS
QUADSTATS
QUADSTATS
QUADSTATS
QUADSTATS
QUADSTATS
QUADSTATS
QUADSTATS
QUADSTATS
QUADSTATS
-+
++
--
+-
Log Fluorescence
-+
++
--
+-
Log Fluorescence
-+
++
--
+-
Log Fluorescence
-+
++
--
+-
Log Fluorescence
-+
++
--
+-
Log Fluorescence
-+
++
--
+-
Log Fluorescence
-+
++
--
+-
Log Fluorescence
-+
++
--
+-
Log Fluorescence
-+
++
--
+-
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
Log Fluorescence
++
--
QUADSTATS
Log Fluorescence
QUADSTATS
-+
++
4
Log Fluorescence
+-
Log Fluorescence
-+
Log Fluorescence
--
QUADSTATS
Log Fluorescence
++
Log Fluorescence
Log Fluorescence
QUADSTATS
3
Log Fluorescence
2
Log Fluorescence
1
Log Fluorescence
2 color
-+
4
color
3
color
-+
++
--
+-
Log Fluorescence
This example shows how complex the analysis can become for a large set of data with
many variables. Represented are the number of dual plots that would have to be
displayed to represent the possible number of combinations. It should be noted of course
that you cannot display 3 or more dimensions in 2 dimensional space!!
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT
Summary of Material
• There are 2 primary types of detectors used in flow
cytometers
• These have different sensitivities and applications
• We collect data in log space mostly because we need a
large dynamic range (this is difficult to do in linear space
because of limits and costs of hardware)
• Data acquisition and analysis
• Types of data formats and presentation formats
• Data analysis techniques such as gating, forward and back
gating
© 1988-2004 J.Paul Robinson, Purdue University BMS 633A –BME 695Y LECTURE 3.PPT