Transcript Amgen Branded Presentation (White) 2006

Flow Data Analysis
Challenges Deck from Amgen
Attendees
Wednesday, September 20, 2006
Bioinformatics/
Computational Biology
Biostatistics
John Gosink
Cheng Su
Molecular
Sciences
Katie Newhall
Hugh Rand
Bill Rees
Mark Dalphin
Gary Means
Sample and meta-data tracking can be
complicated
Blood
samples
Multiple cell
types
Stimulation/inhibition
combinations
FCS
files
Cell
events
FSC-H
44
196
211
72
87
173
Misc
drugs
SSC-H
25
143
129
74
22
144
FL1-H
65
90
98
109
153
94
FL2-H
63
110
97
25
72
139
FL3-H
0
0
3
0
0
0
FL1-A
0
0
0
2
13
0
FL4-H
53
74
48
20
58
72
Time
0
1
1
2
2
2
Misc
cytokines
1,000 – 10,000
10 – 100
5 – 10
10,000 – 100,000
5 – 20
blood samples
stimulations / sample
cell types/mix
cell events/ cell type
channels/cell
Approx. the size of an Affymetrix
Microarray .CEL file
An FCS file
5,000 samples x 50 stims/sample x 7 cell-types/cocktail x 5 Mbytes/FCS file
@ 10
terabytes
Need a relational database and associated code infrastructure
John Gosink, Bioinformatics/Computational Biology, Amgen
For Internal Use Only. Amgen Confidential.
2
Some meta-data that we need to capture,
store, and index
(let alone the actual FCS files/data)
• Sample meta-data
• Sample ID
• Sample to well mapping
• Stimulation conditions
• Dilutions
• Reagent meta-data
• Reagent batches
• Labeling scheme
• Machine meta-data (FCS format currently captures most)
• measurement windows
• PMT settings
• compensation (and matrix)
• transformation
• Gating parameters
• coordinates
• thresholds
• gate hierarchy
John Gosink, Bioinformatics/Computational Biology, Amgen
For Internal Use Only. Amgen Confidential.
3
More interesting questions involve natural cell
populations and their variation
• Catalog of all cell types
- What are their distributions in all of flow parameter space
- How to standardize between samples and runs
• What are fruitful approaches to characterizing these distributions
-Baseline catagorization
- Number of “typical” cell volumes (archetypes)
- Location of archetypes
- Shapes of archetypes
- Relationships of cell counts in the archetypes
- Characterization of the “void”
- How empty is the void
- How smooth is the void
• Detection of novel (sub) populations and unforseen changes
John Gosink, Bioinformatics/Computational Biology, Amgen
For Internal Use Only. Amgen Confidential.
4
Separation of Overlapping Peaks
 Question: How do we best quantitate multiple
overlapping peaks
 One Approach: Fit peaks as a sum of small
numbers of basis set functions.
 Issues: Basis set choice, sensitivity, accuracy,
…
Hugh Rand, Bioinformatics/Computational Biology, Amgen
For Internal Use Only. Amgen Confidential.
5
Example histograms
Overlap
Noisy
Small Peaks
More Overlap
Shape
Hugh Rand, Bioinformatics/Computational Biology, Amgen
For Internal Use Only. Amgen Confidential.
6
Receptor Occupancy Assay and Analysis
Flow
Cytometer
Unlabeled Ab @
0 – sat’d dose
No Drug in Animal
Labeled
Ab
Labeled
anti-recpt Ab
FracBound  1
3
3
Some Drug in Animal
FracBound  1
Cell with specific
and non-specific receptors.
Ab induces more recpt.
Cell with specific
and non-specific receptors
Unlabeled Drug Ab
Labeled Drug Ab
Labeled Recpt Ab
Labeled Isotype Ctrl Ab
Mark Dalphin, Bioinformatics/Computational Biology, Amgen
For Internal Use Only. Amgen Confidential.
Labeled
isotype ctrl
7
2
6
Some math…
Simple form, without non-specific binding
Occupancy 1 
N Dd
N Rd
FDd
F
 1  D0
FRd
FR 0
Add non-specific binding and things are not so tidy
FDd
FRd
FId
PDd 
, PRd 
, PId 
FD 0
FR 0
FI 0
NI 0
f 
N R0
Occpancy 1 
PDd  f ( PDd  PId )
PRd  f ( PRd  PId )
Mark Dalphin, Bioinformatics/Computational Biology, Amgen
For Internal Use Only. Amgen Confidential.
8
Problems with receptor occupancy assays
 Even with 1:1 conjugates, MFI varies significantly from
Ab to Ab against the same receptor
 “Can’t see less than 1,000 receptors per cell”
 Large variability from instrument to instrument and run to
run
 Why doesn’t this behave like a well-controlled physical
experiment; why is it “semi-quantitative”?
 I’d like to see:
– Easy loading of data-sets and meta-data
– Module to compute occupancy
– Some way to look at associated binding curves
Mark Dalphin, Bioinformatics/Computational Biology, Amgen
For Internal Use Only. Amgen Confidential.
9
Gating Sensitivity
 If gates change slightly, will results change?
 Reasons for considering gating sensitivity:
–
–
–
–
–
Quantitative analysis of the responses
Gating is done per individual samples
Gating is somewhat subjective, even auto-gating
Multiple gates used
Subgroups of small size
Cheng Su, Biostatistics, Amgen
For Internal Use Only. Amgen Confidential.
10
Gating Sensitivity Analysis
 Sensitivity Analysis
– Get new gates by moving the boundary of gates
– Conduct analysis
– Compare the results
 Challenges
– software/system: to import the gate boundary
– methodology: methods to automate gate movement
and compare results
Cheng Su, Biostatistics, Amgen
For Internal Use Only. Amgen Confidential.
11
System Outline
Samples
LSRII
B Cells
XML
FCS
Gating
T Cells
NK Cells
Analysis
(R,SAS,Java,…)
Result
Cheng Su, Biostatistics, Amgen
For Internal Use Only. Amgen Confidential.
12
Check
against
How to move what we do in proprietary graphical tools
into a more high-throughput environments?
 Question: Are there applications available that can
accommodate the size of FCS files that I generate, allow me to
compare data across a plate, and provide data output in an
acceptable format?
 Problem: Currently using a 9-color, 12-parameter antibody
panel in whole blood (and it’s only getting bigger!)
–
–
–
–
FCS file size = 10,000 to 30,000 KB
Analysis time = 8 hours for 32 samples/wells
Export time = 20-30 minutes for 32 FCS files
Output = at least 7 gated files for each FCS file
Katie Newhall, Molecular Sciences, Amgen
For Internal Use Only. Amgen Confidential.
13
How to move what we do in proprietary graphical tools
into a more high-throughput environments?
 Potential solutions
– Analysis
• Automated gating
• Sample flagging
• Comparison of samples across a plate
• Output of histogram statistics in an excel format
– Export time
• Gating information and experimental metadata
exported with FCS/TXT files
Katie Newhall, Molecular Sciences, Amgen
For Internal Use Only. Amgen Confidential.
14
immunophenotyping
 experiment:
–
–
–
–
–
80 clinical whole blood samples
no ex vivo manipulation
4 dose cohorts
38 3-color, RBClyse/no-wash stains
3280 6-parameter FCS files
 What populations of events change in some way as
a function of drug dose or disease state or changes
in other populations?
Bill Rees, Molecular Sciences, Amgen
For Internal Use Only. Amgen Confidential.
15
An immunophenotyping panel
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FITC
PE
PerCP
APC
CD4
CD4
CD4
CD4
CD4
CD4
CD4
CD4
CD4
CD4
CD4
CD4
CD4
CD4
CD45RA
CD244
CD26
CLA
CD94
CD8
CD8
CD8
CD8
CD8
CD20
CD20
CD4
CD45RA
CD8
IgD
IgM
CD27
CLA
CD16
CD14
CD14
CD14
CD16
CCR4
CCR7
CCR8
CXCR3
CD25
CD27
CD28
CD38
CD54
KIR
CD161
CD212
HLA-DR
CCR6
CD4
CD4
CD8
CD8
CD8
CD161
NKG2D
4-1BB-L
CD30
CD70
CD54
CD69
CD152
CD152
CD152
CD152
CD152
CD152
CD152
CD56
CD80
CD86
HLA-DR
CD11b
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CD45
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CCR5
CD56
CD3
CD19
CD19
CD19
CD19
CD19
CCR5
CCR5
CCR5
CCR5
Bill Rees, Molecular Sciences, Amgen
For Internal Use Only. Amgen Confidential.
16
gate
L
L
L
L
+G
+G
+G
+G
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
M
M
M
G
T cells
B cells
NK cells
monocytes
Immunophenotyping
 I will not deal with this 2-dimensions at a time
– time
– too many populations in each stain, only some do I know to look for
– don’t know what I’m looking for with minimal biological insight
 Issues:
–
–
–
–
–
definitions of terms
Metrics, e.g. MFI and %CD45+ events, % responders
Linking raw data to other study data/protocols and to analysis product
Autogating with visual QC
Can the identification of the major cell types (operationally defined by
robust stains, e.g. CD3+ CD8+ CD56-) be automated to incrementally
reduce the analysis time?
Bill Rees, Molecular Sciences, Amgen
For Internal Use Only. Amgen Confidential.
17
Whole blood stimulation assays where leukocytes are
evaluated for phosphoprotein pathway activation inhibition
Specimen_001_C1_C01.fcs
262144
grans
CD33+
131072
Specimen_001_C1_C01.fcs
4
10
0
2
10
3
4
10
10
CD8/CD33
5
10
CD56
65536
3.45%
CD56+
Specimen_001_C1_C01.fcs
0.21%
5
66.04%
4
CD4+
10
CD3+/CD56+
3
CD4
196608
10
10
3
10
2
10
CD3+
70.52%
Bcells
25.82%
2
10
3
10
CD3
4
4
5
10
CD8/CD33
10
Specimen_001_C1_C01.fcs
10
3
10
5
10
CD4+ mem
4
CD4
CD8+
28.54%
5.12%
10
29.34%
70.66%
10
3
CD8/CD33
5
10
DN
2
10
Specimen_001_C1_C01.fcs
0.31%
2
13.87%
86.13%
CD8+ mem
4
10
3
10
2
10
10
0.00%
3
10
Molecular
Sciences,
Amgen
Note:Gary
This isMeans,
the region
where notes
could be placed
For Internal Use Only. Amgen Confidential.
0.00%
4
10
CD45RO
5
0.00%
3
10
10
18
0.00%
4
10
CD45RO
5
10
Whole
blood
Stimulate
Process
Cells
Labeling
Flow
Sample
Data
File
Software
metadata
Problem?
Each set of gated data must
be independently exported
and kept linked to the
experimental process
Use bioinformatics
tools to evaluate
coordinate regulation
of at multiple different
intracellular targets
Lymphocyte
NK
B cell
CD4+/CD8+
Granulocyte
T cell
CD4+
CD8+
CD4+
memory
CD8+
memory
11 gates x 4 targets x 96 wells
Gary Means, Molecular Sciences, Amgen
For Internal Use Only. Amgen Confidential.
19
Monocyte
DN
Solutions?
 Automatically export events with additional columns
which contain all of the gating information associated
with each event.
 Metadata must be inextricably associated with the
experimental results.
Gary Means, Molecular Sciences, Amgen
For Internal Use Only. Amgen Confidential.
20