mramon_aera2010
TRANSCRIPT
The use of R statistical software to analyze flow cytometry data
M. Ramón1, A. Maroto-Morales1, O. García-Álvarez2, P. Jimenez-Rabadán2, MD. Pérez-Guzmán2, F. Martínez-Pastor3, AJ. Soler1,
JJ. Garde1
1IREC, (CSIC-UCLM-JCCM), Albacete; 2CERSYRA, Valdepeñas; 3ITRA-ULE-INDEGSAL, León
10º Congreso de la Asociación Española de Reproducción AnimalCáceres, 2-5 Junio 2010
R Statistical SoftwareIntegrated suite of software facilities for data manipulation, calculation and graphical display.
WinMDI Version 2.9Windows Multiple Document Interface for Flow Cytometry.The Scripps Research Institute (TSRI)
WEASEL Version 2.7.4Walter & Eliza Analysis Software: Eclectic & Lucid. The Walter and Eliza Hall Institute (WEHI)
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
Main Goal: To provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of genomic data.
Bioconductor provides a unified framework to develop methods to analyze and interpret Flow Cytometry data.
Most common Packages
α FlowCore α FlowUtils
α FlowViz α FlowQ
α FlowStats α FlowClust
Bioconductor Open Source Software for Bioinformatics
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
A PRACTICAL EXAMPLE
• Semen sample evaluated at 2 different times (0 h and 3 h)
• Each semen sample was stained for:
• YO-PRO®-1. Apoptosis-like changes
• Propidium Iodide (PI). Membrane integrity
• MitoTracker® Deep Red FM (MT). Mitochondrial status
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
1. READ DATA FILES
> flowData <- read.flowSet( path =“.”, alter.names=TRUE, phenoData= “annotation.txt")
> wf <- workFlow(flowData, name="Workflow #1")
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
1. READ DATA FILES
> flowData <- read.flowSet( path =“.”, alter.names=TRUE, phenoData= “annotation.txt")
> wf <- workFlow(flowData, name="Workflow #1")
Organize standard flow cytometry data analysis in a workflow
• read.FCS()
• read.flowSet()
• list of flowSets
sampleId time stain
file.001 Y.PI.MT.0 0H FL1.H/FL3.H/SSC.W
file.002 Y.PI.MT.3 3H FL1.H/FL3.H/SSC.W
…
> wf
A flow cytometry workflow called 'Workflow #1'
The following data views are provided:
Basic view 'base view'
on a flowSet
not associated to a particular action item
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
2. STANDARD FLOW OPERATIONS
Set of basic operations common in Flow Cytometry Analysis
• Visualize Data
• Data Compensation
• Data Transformation
• Data Normalization
• Define Target Population (gating)
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
2. STANDARD FLOW OPERATIONS
Scatter plot matrix
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
2. STANDARD FLOW OPERATIONS
Scatter plot matrix
Some transformation is needed for better visualization of data
2a. DATA TRANSFORMATION
> tf <- transformList(colnames(flowData), asinh, transformationId="asinh")
> add(wf,tf)
> flowDataT <- transform(flowData, tf)
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
A new transformated flow data set
2a. DATA TRANSFORMATION
> tf <- transformList(colnames(flowData), asinh, transformationId="asinh")
> add(wf,tf)
> flowDataT <- transform(flowData, tf)
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
Assign new elements to an existing workflow
• linear transformation
• quadratic transformation
• log transformation
• asinh transformation
• …
2a. DATA TRANSFORMATION
Scatter plot matrix
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
2b. IDENTIFICATION OF SPERM POPULATION
FSC vs. SSC scatter plot
> xyplot(`SSC.H` ~ `FSC.H` | sampleId, data=wf [["asinh"]])
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
2b. IDENTIFICATION OF SPERM POPULATION
FSC vs. SSC scatter plot
> xyplot(`SSC.H` ~ `FSC.H` | sampleId, data=wf [["asinh"]])
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
Debris
2b. IDENTIFICATION OF SPERM POPULATION
FSC vs. SSC scatter plot
> sp.gate <- polygonGate(.gate=r01)
> add(wf, sp.gate, parent='asinh')
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
2b. IDENTIFICATION OF SPERM POPULATION
FSC vs. SSC scatter plot
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
WinMDI WEASEL
3. MEMBRANE INTEGRITY STUDY
YO-PRO®-1 (FL1.H) vs. PI (FL3.H) scatter plot
> xyplot(`FL3.H` ~ `FL1.H` | sampleId, data=wf[["asinh"]])
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
RAW DATA
3. MEMBRANE INTEGRITY STUDY
YO-PRO®-1 (FL1.H) vs. PI (FL3.H) scatter plot
> xyplot(`FL3.H` ~ `FL1.H` | sampleId, data=wf[["asinh"]])
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
Debris must be removed !!!
RAW DATA
3. MEMBRANE INTEGRITY STUDY
YO-PRO®-1 (FL1.H) vs. PI (FL3.H) scatter plot
> xyplot(`FL3.H` ~ `FL1.H` | sampleId, data=wf[["asinh"]])
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
RAW DATA SUBSET SPERM POPULATION
3. MEMBRANE INTEGRITY STUDY
YO-PRO®-1 (FL1.H) vs. PI (FL3.H) scatter plot
> xyplot(`FL3.H` ~ `FL1.H` | sampleId, data=wf[["asinh"]])
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
Is a compensation of data needed?
Must be applied before data transformation !!
SUBSET SPERM POPULATION
2c. DATA COMPENSATION
COMPENSATION MATRIX
• Define our own compensation matrix.
• Provide on FSC files by the cytometer. Spillover() R Function
> cmat <- compensation(comp, parameters=colnames(Data(wf[["base view"]]))[3:6],
+ compensationId="comp")
> add(wf, cmat)
> flowDataC <- compensate(flowData, comp)
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
FL1.H FL2.H FL3.H FL4.H FL5.H
[1,] 1 0.240 0.032 0.001 0
[2,] 0.008 1 0.140 0.003 0
[3,] 0.170 0.170 1 0.210 0
[4,] 0.001 0.001 0.003 1 0
[5,] 0 0 0 0 1
2c. DATA COMPENSATION
COMPENSATION MATRIX
• Define our own compensation matrix.
• Provide on FSC files by the cytometer. Spillover() R Function
> cmat <- compensation(comp, parameters=colnames(Data(wf[["base view"]]))[3:7],
+ compensationId="comp")
> add(wf, cmat)
> flowDataC <- compensate(flowData, comp)
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
2c. DATA COMPENSATION
COMPENSATION MATRIX
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
2c. DATA COMPENSATION
COMPENSATION MATRIX
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
WEASEL
3. MEMBRANE INTEGRITY STUDY
IDENTIFICATION OF DIFFERENT SUBPOPULATIONS
Definition of Regions/Quadrants
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
Simple geometric Filters• Rectangle Gate
• Polygon gate
• Quadrant Gate
Data-Driven Filters• KmeansGate
• Norm2Gate
• Curv1Gate and Curv2Gate
• …
3. MEMBRANE INTEGRITY STUDY
IDENTIFICATION OF DIFFERENT SUBPOPULATIONS
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
RECTANGLE REGION POLYGON REGIONS QUADRANT REGIONS
3. MEMBRANE INTEGRITY STUDY
IDENTIFICATION OF DIFFERENT SUBPOPULATIONS
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
QUADRANT REGIONS
-2 0 2 4 6 8 10
0.0
00
.10
0.2
00
.30
breakpoint for parameter FL1.H
N = 12668 Bandwidth = 0.329
De
nsi
ty
breakpointdens region
2 4 6 8
0.0
0.1
0.2
0.3
0.4
breakpoint for parameter FL3.H
N = 12668 Bandwidth = 0.1223
De
nsi
tybreakpointdens region
3. MEMBRANE INTEGRITY STUDY
IDENTIFICATION OF DIFFERENT SUBPOPULATIONS
Definition of Regions/Quadrants
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
Regions are different for each semen sample
Data normalization will allow the use of a single region or quadrant set
2d. DATA NORMALIZATION
> norm <- normalization(parameters=param, normalizationId="norm",
+ normFunction=function(x, parameters, ...) warpSet(x,parameters))
> add(wf, norm, parent="asinh")
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
Y.PI.MT.0
Y.PI.MT.3
0 2 4 6 8 10
FL1.H
0 2 4 6 8 10
FL3.H
BEFORE NORMALIZATION
2d. DATA NORMALIZATION
> norm <- normalization(parameters=param, normalizationId="norm",
+ normFunction=function(x, parameters, ...) warpSet(x,parameters))
> add(wf, norm, parent="asinh")
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
Y.PI.MT.0
Y.PI.MT.3
-5 0 5 10
FL1.H
0 2 4 6 8 10
FL3.H
AFTER NORMALIZATION
3. MEMBRANE INTEGRITY STUDY
IDENTIFICATION OF DIFFERENT SUBPOPULATIONS
Summarize statistics
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
FL1.H-FL3.H- FL1.H+FL3.H-
FL1.H+FL3.H+ Y.PI.MT.0 47.77% 33.68% 18.37% Y.PI.MT.3 53.65% 30.28% 15.15%
6. SUMMARY
• Read data
• Visualize data
• Compensation data
• Transform data
• Define Sperm population
• Normalize data
• Identify Subpopulations
• Get statistics
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
WHY TO USE R?
• Allows the analysis of several raw FCS files at once
• Use criteria base on data density to identify sperm subpopulations
• Definition of a unique set of filters/gates
• Reduction in time consumption
• Powerful statistical environmental
• Important development community
• Reproducibility research
7. iFLOW
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
The use of R statistical software to analyze flow cytometry data10º Congreso de la Asociación Española de Reproducción Animal (AERA)Cáceres, 2-5 Junio 2010
THANK YOU FOR YOUR ATTENTION