mramon_AERA2010

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mramon_AERA2010

  1. 1. The use of R statistical software to analyze flow cytometry data M. Ramón 1 , A. Maroto-Morales 1 , O. García-Álvarez 2 , P. Jimenez-Rabadán 2 , MD. Pérez-Guzmán 2 , F. Martínez-Pastor 3 , AJ. Soler 1 , JJ. Garde 1 1 IREC, (CSIC-UCLM-JCCM), Albacete; 2 CERSYRA, Valdepeñas; 3 ITRA-ULE-INDEGSAL, León 10º Congreso de la Asociación Española de Reproducción Animal Cáceres, 2-5 Junio 2010
  2. 2. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 R Statistical Software Integrated suite of software facilities for data manipulation, calculation and graphical display. WinMDI Version 2.9 Windows Multiple Document Interface for Flow Cytometry. The Scripps Research Institute (TSRI) WEASEL Version 2.7.4 Walter & Eliza Analysis Software: Eclectic & Lucid. The Walter and Eliza Hall Institute (WEHI)
  3. 3. The use of R statistical software to analyze flow cytometry data 10º 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
  4. 4. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 <ul><li>A PRACTICAL EXAMPLE </li></ul><ul><li>Semen sample evaluated at 2 different times (0 h and 3 h) </li></ul><ul><li>Each semen sample was stained for: </li></ul><ul><ul><li>YO-PRO®-1. Apoptosis-like changes </li></ul></ul><ul><ul><li>Propidium Iodide (PI). Membrane integrity </li></ul></ul><ul><ul><li>MitoTracker® Deep Red FM (MT). Mitochondrial status </li></ul></ul>
  5. 5. The use of R statistical software to analyze flow cytometry data 10º 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&quot;) > wf <- workFlow(flowData, name=&quot;Workflow #1&quot;)
  6. 6. The use of R statistical software to analyze flow cytometry data 10º 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&quot;) > wf <- workFlow(flowData, name=&quot;Workflow #1&quot;) Organize standard flow cytometry data analysis in a workflow <ul><li>read.FCS() </li></ul><ul><li>read.flowSet() </li></ul><ul><li>list of flowSets </li></ul>> 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 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 …
  7. 7. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 <ul><li>2. STANDARD FLOW OPERATIONS </li></ul><ul><li>Set of basic operations common in Flow Cytometry Analysis </li></ul><ul><li>Visualize Data </li></ul><ul><li>Data Compensation </li></ul><ul><li>Data Transformation </li></ul><ul><li>Data Normalization </li></ul><ul><li>Define Target Population (gating) </li></ul>
  8. 8. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 2. STANDARD FLOW OPERATIONS Scatter plot matrix
  9. 9. The use of R statistical software to analyze flow cytometry data 10º 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
  10. 10. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 2a. DATA TRANSFORMATION > tf <- transformList(colnames(flowData), asinh, transformationId=&quot;asinh&quot;) > add(wf,tf) > flowDataT <- transform(flowData, tf)
  11. 11. The use of R statistical software to analyze flow cytometry data 10º 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=&quot;asinh&quot;) > add(wf,tf) > flowDataT <- transform(flowData, tf) Assign new elements to an existing workflow <ul><li>linear transformation </li></ul><ul><li>quadratic transformation </li></ul><ul><li>log transformation </li></ul><ul><li>asinh transformation </li></ul><ul><li>… </li></ul>
  12. 12. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 2a. DATA TRANSFORMATION Scatter plot matrix
  13. 13. The use of R statistical software to analyze flow cytometry data 10º 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 [[&quot;asinh&quot;]])
  14. 14. The use of R statistical software to analyze flow cytometry data 10º 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 [[&quot;asinh&quot;]]) Debris
  15. 15. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 2b. IDENTIFICATION OF SPERM POPULATION FSC vs. SSC scatter plot > sp.gate <- polygonGate(.gate=r01) > add(wf, sp.gate, parent='asinh')
  16. 16. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 2b. IDENTIFICATION OF SPERM POPULATION FSC vs. SSC scatter plot WinMDI WEASEL
  17. 17. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 3. MEMBRANE INTEGRITY STUDY YO-PRO®-1 (FL1.H) vs. PI (FL3.H) scatter plot > xyplot(`FL3.H` ~ `FL1.H` | sampleId, data=wf[[&quot;asinh&quot;]]) RAW DATA
  18. 18. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 3. MEMBRANE INTEGRITY STUDY YO-PRO®-1 (FL1.H) vs. PI (FL3.H) scatter plot > xyplot(`FL3.H` ~ `FL1.H` | sampleId, data=wf[[&quot;asinh&quot;]]) Debris must be removed !!! RAW DATA
  19. 19. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 3. MEMBRANE INTEGRITY STUDY YO-PRO®-1 (FL1.H) vs. PI (FL3.H) scatter plot > xyplot(`FL3.H` ~ `FL1.H` | sampleId, data=wf[[&quot;asinh&quot;]]) RAW DATA SUBSET SPERM POPULATION
  20. 20. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 3. MEMBRANE INTEGRITY STUDY YO-PRO®-1 (FL1.H) vs. PI (FL3.H) scatter plot > xyplot(`FL3.H` ~ `FL1.H` | sampleId, data=wf[[&quot;asinh&quot;]]) Is a compensation of data needed? Must be applied before data transformation !! SUBSET SPERM POPULATION
  21. 21. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 <ul><li>2c. DATA COMPENSATION </li></ul><ul><li>COMPENSATION MATRIX </li></ul><ul><li>Define our own compensation matrix. </li></ul><ul><li>Provide on FSC files by the cytometer. Spillover() R Function </li></ul><ul><li>> cmat <- compensation(comp, parameters=colnames(Data(wf[[&quot;base view&quot;]]))[3:6], </li></ul><ul><li>+ compensationId=&quot;comp&quot;) </li></ul><ul><li>> add(wf, cmat) </li></ul><ul><li>> flowDataC <- compensate(flowData, comp) </li></ul>
  22. 22. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 <ul><li>2c. DATA COMPENSATION </li></ul><ul><li>COMPENSATION MATRIX </li></ul><ul><li>Define our own compensation matrix. </li></ul><ul><li>Provide on FSC files by the cytometer. Spillover() R Function </li></ul><ul><li>> cmat <- compensation(comp, parameters=colnames(Data(wf[[&quot;base view&quot;]]))[3:7], </li></ul><ul><li>+ compensationId=&quot;comp&quot;) </li></ul><ul><li>> add(wf, cmat) </li></ul><ul><li>> flowDataC <- compensate(flowData, comp) </li></ul>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
  23. 23. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 2c. DATA COMPENSATION COMPENSATION MATRIX
  24. 24. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 2c. DATA COMPENSATION COMPENSATION MATRIX WEASEL
  25. 25. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 3. MEMBRANE INTEGRITY STUDY IDENTIFICATION OF DIFFERENT SUBPOPULATIONS Definition of Regions/Quadrants <ul><li>Simple geometric Filters </li></ul><ul><li>Rectangle Gate </li></ul><ul><li>Polygon gate </li></ul><ul><li>Quadrant Gate </li></ul><ul><li>Data-Driven Filters </li></ul><ul><li>KmeansGate </li></ul><ul><li>Norm2Gate </li></ul><ul><li>Curv1Gate and Curv2Gate </li></ul><ul><li>… </li></ul>
  26. 26. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 3. MEMBRANE INTEGRITY STUDY IDENTIFICATION OF DIFFERENT SUBPOPULATIONS RECTANGLE REGION POLYGON REGIONS QUADRANT REGIONS
  27. 27. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 3. MEMBRANE INTEGRITY STUDY IDENTIFICATION OF DIFFERENT SUBPOPULATIONS QUADRANT REGIONS
  28. 28. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 3. MEMBRANE INTEGRITY STUDY IDENTIFICATION OF DIFFERENT SUBPOPULATIONS Definition of Regions/Quadrants Regions are different for each semen sample Data normalization will allow the use of a single region or quadrant set
  29. 29. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 2d. DATA NORMALIZATION > norm <- normalization(parameters=param, normalizationId=&quot;norm&quot;, + normFunction=function(x, parameters, ...) warpSet(x,parameters)) > add(wf, norm, parent=&quot;asinh&quot;) BEFORE NORMALIZATION
  30. 30. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 2d. DATA NORMALIZATION > norm <- normalization(parameters=param, normalizationId=&quot;norm&quot;, + normFunction=function(x, parameters, ...) warpSet(x,parameters)) > add(wf, norm, parent=&quot;asinh&quot;) AFTER NORMALIZATION
  31. 31. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 3. MEMBRANE INTEGRITY STUDY IDENTIFICATION OF DIFFERENT SUBPOPULATIONS Summarize statistics 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%
  32. 32. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 <ul><li>6. SUMMARY </li></ul><ul><li>Read data </li></ul><ul><li>Visualize data </li></ul><ul><li>Compensation data </li></ul><ul><li>Transform data </li></ul><ul><li>Define Sperm population </li></ul><ul><li>Normalize data </li></ul><ul><li>Identify Subpopulations </li></ul><ul><li>Get statistics </li></ul>
  33. 33. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 <ul><li>WHY TO USE R? </li></ul><ul><li>Allows the analysis of several raw FCS files at once </li></ul><ul><li>Use criteria base on data density to identify sperm subpopulations </li></ul><ul><li>Definition of a unique set of filters/gates </li></ul><ul><li>Reduction in time consumption </li></ul><ul><li>Powerful statistical environmental </li></ul><ul><li>Important development community </li></ul><ul><li>Reproducibility research </li></ul>
  34. 34. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 7. iFLOW
  35. 35. The use of R statistical software to analyze flow cytometry data 10º Congreso de la Asociación Española de Reproducción Animal (AERA) Cáceres, 2-5 Junio 2010 THANK YOU FOR YOUR ATTENTION

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