Data Analysis % Total Mean Geometric mean CV % of Parent Mode Median FSC SSC FITC PE CV % of Parent Mode This lecture is both a overview of key components of data analysis Median % of Grandparent

Data Analysis Experimental Design Instrumentation Analysis Presentation Sample Procurement Sample preparation Fix/Perm Which Fluorophore Controls Isotype? Single color FMO Appropriate Lasers Appropriate Filters Instrument Settings Lin vs Log Time A, W, H Interpretation Mean, Median % + CV SD Signal/Noise Gating Histogram Dot Plot Density Plot Overlay Bar Graph

This will involve both the “what is” data analysis and the “how to”. Objective This will involve both the “what is” data analysis and the “how to”. Instrumentation (Mostly covered in “Flow Basics”) Underlying Principles/Concepts Proper Technique

First, lets address the problems Data analysis incorporates many disciplines including instrumentation, statistics, biology, and photonics. Often times knowledge in one of these is limited Many different instruments and software packages are available. Historical precedent- Unfortunately there is a large body of work published with poor data and no clear guidelines

Flow Basics A quick look back to Flow Basics to see what we are analyzing Photons ElectronsVoltage pulseDigital #

Measurements of the Pulse Pulse Width Pulse Height Pulse Area Voltage Intensity Time

10 256 10 10,000 1 196 1000 .1 128 (Volts) (Volts) Relative Brightness Channel Number 3.54 volts .01 64 10 1.23 volts .001 1 (1mV) Photons=voltage=relative brightness

FCS File or List Mode File Data FSC SSC FITC PE APC APC-Cy7 FCS File or List Mode File FCS File- The flow cytometry data file standard provides the information needed to completely describe flow cytometry data sets within the confines of the file containing the experimental data. It is made up of the Header, Text and Data. Current version is FSC 3.0

FCS file Text Keywords- value pairs that describe the experiment, the instrument, the specimen, the data and any other information which the file creator wishes to include. Some values are user defined, others are automatically entered

Histograms N=552 N=7696 Frequency Histogram- is a summary graph showing a count of the data points falling in various ranges. The effect is a rough approximation of the frequency distribution of the data. # of Apples picked per tree N=7696 In a more general mathematical sense, a histogram is a function mi that counts the number of observations that fall into each of the disjoint categories (known as bins), whereas the graph of a histogram is merely one way to represent a histogram. Thus, if we let n be the total number of observations and k be the total number of bins, the histogram mi meets the following conditions: Frequency 0 101 102 103 104 Arbitrary Fluorescent units

Creation of a Histogram Listmode File 0 101 102 103 104 3 6 9 12 PE

Dot Plots Dot plot- Frequency distributions of two simultaneously displayed parameters

Plots Contour Pseudo-color Histogram Greyscale Density Dot plot

Dot plots vs density or Pseudocolor 6,000,000 Events 6,000,000 4,500,000 3,000,000 1,500,000 600,000 300,000 150,000

Gating Selecting a subset of cells based on a specific fluorescent or scatter pattern. -Either graph can be OK -However “gated” may be more appropriate due to exclusion of artifact and non T cells No Gate CD3+ Gate 11.3% 59.8% 5.8% 30% Interpretation 11.3% of the WBC’s are positive for CD4 and 5.8% are positive for CD8 Interpretation 59.8% of the CD3+ cells are positive for CD4 and 30% are positive for CD8

Hierarchical Gating

Compensation Fluorescent Compensation- Due to broad emission profiles of fluorescent dyes, a single detector may process light from multiple dyes. The process of mathematically correcting for this spectral overlap is called compensation

Compensation In essence, back- ground light and electronic noise make a small but important contribution to the overall sig- nals the FACS instrument obtains for each cell. The FACS apparatus measures this background when no cells are present and subtracts it from the signals the detectors record for each cell. This instrument background correction intro- duces statistical variation, which may cause the measurements that the detectors record for cells that have no cell-associated fluores- cence to be a little above or a little below the measurement set as the instrument zero. As a result, the corrected measurements distribute symmetrically around zero.

Mean/Median/GeoMean Mean Geometric Mean Median Sum of the “n” individual values of a group divided by n NOT GOOD FOR Log DATA More easily skewed by outliers Geometric Mean Multiply the ‘N” values of a cluster together and get the nth root of this product Better for log data Median Divides your data in half Probably the safest for Log data http://flowjo.typepad.com/the_daily_dongle/2007/10/mean-median-mod.html

Location of mean, median and mode with different distributions. Normal Left skew Location of mean, median and mode with different distributions. (A) Normal distribution. (B) Right (positive) skewed distribution. (C) Left (negative) skewed distribution. Right skew Driscoll P et al. J Accid Emerg Med 2000;17:274-281

This will involve both the “what is” data analysis and the “how to”. Objective This will involve both the “what is” data analysis and the “how to”. Instrumentation (Mostly covered in “Flow Basics”) Underlying Principles/Concepts Proper Technique

There is no wrong way to analyze your data Meaning- Investigators are free to choose: Which plot types for display Placement of gates for analysis Which statistics # events to display or collect Which software package to use How many times you reanalyze

There is definitely a wrong way to analyze your data Meaning- Investigators decisions can lead to incorrect data generation or interpretation: Inappropriate gates for analysis (lymphocyte gate for CD15 staining, or inconsistent gates) Misleading or inconsistent plots for display Inappropriate controls (e.g. using isotype for gating) Inappropriate number of events collected (too few events for meaningful and accurate statistical comparison)

Dot plot Vs Histogram

Doublet discrimination Cell cycle Rare events Dim cell lines Fiona Craig Flow Cytometric Immunophenotyping of Cerebrospinal Fluid SpecimensAJCP 2011 135:22-34 Width Height Cell aggregates were identified in 29 (16.4%) of 177 CSF specimens and represented 0 to 1,503 events and 0% to 80% of total acquired events Red=Singlets Blue=Doublets

On axis data

Bi-exponential Scaling Approximates log scaling at the upper end Approximates linear scaling at the lower end Can display events at or below zero Improves visual separation of population at the low end of the scale In essence, back- ground light and electronic noise make a small but important contribution to the overall sig- nals the FACS instrument obtains for each cell. The FACS apparatus measures this background when no cells are present and subtracts it from the signals the detectors record for each cell. This instrument background correction intro- duces statistical variation, which may cause the measurements that the detectors record for cells that have no cell-associated fluores- cence to be a little above or a little below the measurement set as the instrument zero. As a result, the corrected measurements distribute symmetrically around zero. http://ucflow.blogspot.com/2011/04/display-transformation-and-flowjo.html

Log scale Bi-exponential This occurs because logarithmic scales, by their nature, spread data at the lowest on-scale values and cannot accommodate values at or below zero Cells with minimal fluorescence can be visualized with 'logicle' displays (right) but are 'piled up' on the axis with logarithmic displays (left). The true center of each gated population (median fluorescence value in each dimension; dark red crosses) matches the visual peak for that population in 'logicle' displays (right, top and middle) but does not match the visual peak in the logarithmic displays (left, top and middle). Because logarithmic scales cannot display cells with zero or negative values, these cells are 'piled up' on the axis in the logarithmic displays. However, they are properly visualized in the 'logicle' display (bottom right, red shaded region). Data provided by E. Ghosn (Stanford University, Stanford, California). Herzenberg et al

Isotypes Must be the exact same isotype Conjugated to exactly the same degree Fluorophore/Protein ratio Has the same background binding characteristics as your antibody Different vendors use different conjugation protocols which may cause different characteristics Used at the same concentration Not used for gating purposes!!! Rarely is more than the first criterion met. For example, when you conjugate antibodies to Texas Red, you can get hugely different "background" binding characteristics depending on the reactive form of TR that you use--even after getting exactly the same F/P ratios. Clearly, the sites on an antibody that are conjugated are different by these different reactive forms of TR, and those differences translate into different "background" binding characteristics.

FMO controls FMO = Fluorescence minus one The gating control contains all the fluorophores except the one of interest PE FITC Perfetto et al

Rare events CV = 100 X SD/ N or 100 / sqrt N. The essential feature of Poisson distributions is that if N events are observed the standard deviation (SD) associated with that count is square root of N . The coefficient of variation (CV) is then given by CV = 100 X SD/ N or 100 / sqrt N. How many events to detect is more of a statistics questions than a flow cytometry question

Statistical Significance of Results Determining the Number of Events to Collect

Rare Events Case Study #1 Number of Events vs Measurement Precision .1%-.18% .07%-.1%

Rare Events Case Study #2 Normal Kappa/Lambda ratio on B cells is .9-3.5 (polytypic) When values fall outside this range it’s “clonal” Often times in patients undergoing therapy there is a very low frequency of B cells to enumerate. Gated on CD19+ Bcells 20 Kappa/Lambda=58/20=2.9(Polytypic) But, √58=8 and √20=5 So actual values are 58±8/20±5 Which leads to ratio’s between 4.4 and 2. Is this clonal??????? 58

4 decade or 5 decade display

FACSDiVa The Diva system first converts the analog signal to digital using a 14-bit ADC (16, 384 bins), then when the data is log transformed, it is multiplied by 16 (or 24) effectively adding 4 bits to the data making it 18-bit (262,144 bins). It displays this data on a 5-decade scale, but then does not display the first decade. What you’re left with is a 4-decade log scale (actually the scale goes above the 4th decade a little bit) with bins from 262,144 down to 262. Decade Analog PMT Signal 14-bit Bins Upscaled to 18-bit Decade Range 5 10V 16384 262144 10001-100000+ 4 1V 1638 26214 1001-10000 3 .1V 164 2621 101-1000 2 .01V 16 262 11-100 1 .001V 26 1-10

Dead Cell Exclusion Immunophenotyping Rare event analysis Despite light scatter gate Rare event analysis Prefetto et al JIM 2006

Dump gate With dump gate Without dump gate

Light Scatter Laser Beam FSC Detector Collection Lens SSC Detector Original from Purdue University Cytometry Laboratories

Why Look at FSC v. SSC Since FSC ~ size and SSC ~ internal structure, a correlated measurement between them can allow for differentiation of cell types in a heterogenous cell population FSC SSC Lymphocytes Granulocytes Dead LIVE Monocytes RBCs, Debris, Dead Cells

Kinetic and Ratio parameters

Automated clustering algorithms Aghaeepour N, Nikolic R, Hoos HH, Brinkman RR. Rapid cell population identification in flow cytometry. Cytometry A. 79(1):6-13, 2011. Zare H, Shooshtari P, Gupta A, Brinkman RR. Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinformatics. 11(1): 403, 2010.

Available Spectra Viewers http://www.bdbiosciences.com/research/multicolor/spectrum_viewer/index.jsp http://www.invitrogen.com/site/us/en/home/support/Research-Tools/Fluorescence-SpectraViewer.html http://www.biolegend.com/spectraanalyzer

Data Analysis Feedback http://www.surveymonkey.com/s/K7WYTH7

References -Perfetto S, 2006 JIM 313 pg199-208 (live vs dead) -Keeney et al 1998 Cytometry -Cytometry 30(5), 1997 http://ucflow.blogspot.com/2011/04/display-transformation-and-flowjo.html (bi-exponential display) -Cytometry A 783A:384-385 -Seventeen-colour flow cytometry: unravelling the immune system -Stephen P. Perfetto, Pratip K. Chattopadhyay & Mario Roederer Nature Reviews Immunology 4, 648-655 (August 2004) -Interpreting flow cytometry data: a guide for the perplexed Herzenberg L, 2006 Nature Immunology Vol 7 Num 7 A practical approach to multicolor flow cyometry for immunophenotyping Baumgarth N, 2000 J. Imm. Methods 243 77-97 -Isotype controls in the Analysis of Lymphocyte and CD34+ stem and progenitor cells by Flow Cytometry-Time to Let Go! Keeney, M 1998 Cytomery (Communication in Clincal Cytometry 34: 280-283

# of Parameters 1 2 3 4 5 6 7 8 9 10 # of possible plots 1 3 6 10 15 21 28 36 45