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Making Sense out of Flow Cytometry Data Overload: A Crash Course in R/Bioconductor and Flow Cytometry Fingerprinting

Learn how to analyze and interpret flow cytometry data using R and Bioconductor. Explore topics such as gating, compensation, transformation, and visualization.

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Making Sense out of Flow Cytometry Data Overload: A Crash Course in R/Bioconductor and Flow Cytometry Fingerprinting

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  1. Making Sense out of Flow Cytometry Data Overload A crash course in R/Bioconductor and flow cytometry fingerprinting

  2. Outline • Background • R • Bioconductor • Motivating examples • Starting R, entering commands • How to get help • R fundamentals • Sequences and Repeats • Characters and Numbers • Vectors and Matrices • Data Frames and Lists • Importing data from spreadsheets • flowCore • Loading flow cytometry (FCS) data • gating • compensation • transformation • visualization • flowFP • Binning • Fingerprinting • Comparing multivariate distributions • Writing your own functions • Installing and running R on your computer • Suggestions for further reading and reference

  3. Background • R • Is an integrated suite of software facilities for data manipulation, simulation, calculation and graphical display. • It handles and analyzes data very effectively and it contains a suite of operators for calculations on arrays and matrices. • In addition, it has the graphical capabilities for very sophisticated graphs and data displays. • It is an elegant, object-oriented programming language. • Started by Robert Gentleman and Ross Ihaka (hence “R”) in 1995 • as a free, independent, open-source implementation of the S programming language (now part of Spotfire) • Currently, maintained by the R Core development team – an international group of hard-working volunteer developers http://www.r-project.org http://cran.r-project.org/doc/contrib/Owen-TheRGuide.pdf

  4. Background • Bioconductor • “Is an open source and open development software project to provide tools for the analysis and comprehension of genomic data.” • Goals • To provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of genomic data. • To provide a common software platform that enables the rapid development and deployment of extensible, scalable, and interoperable software. • To further scientific understanding by producing high-quality documentation and reproducible research. • To train researchers on computational and statistical methods for the analysis of genomic data. http://bioconductor.org/overview

  5. A motivating example I’ve just collected data from a T cell stimulation experiment in a 96-well plate format. I need to gate the data on CD3/CD4. How consistent are the distributions, so that I can establish one set of gates for the whole plate?

  6. A motivating example

  7. Another motivating example I’m concerned that drawing gates to analyze my data introduces unintended bias. Additionally, since I have multiple data files, drawing multiple gates is time consuming. Can I use R to compute gates and then apply these same objective gating criteria to multiple data files?

  8. Another motivating example Autogate lymphocytes and monocytes Automatically analyze FMO tubes

  9. Back to the basics • R is a command-line driven program • the prompt is: > • you type a command (shown in blue), and R executes the command and gives the answer (shown in black)

  10. Simple example: enter a set of measurements • use the function c()to combine terms together • Create a variable named mfi • Put the result of c()into mfi using the assignment operator <- (you can also use =) • The [1] indicates that the result is a vector

  11. Help, functions, polymorphism • help (log) • ?log • apropos(“log”)

  12. Vignettes – really good help!

  13. Sequences and Repeats

  14. Characters and Numbers • Characters and character strings are enclosed in “” or ‘’ • Special numbers • NA – “Not Available” • Inf – “Infinity” • NaN – “Not aNumber”

  15. Vectors and Matrices

  16. Vectors and Matrices • The subset operator for vectors and matrices is [ ]

  17. Vectors and Matrices • You can extend the length of a vector via subsetting … but not a matrix

  18. Vectors and Matrices • However, all’s not lost if you want to extend either the columns … … or rows

  19. Data Frames • A Data Frame is like a matrix, except that the data type in each column need not be the same • Often, a Data Frame is created from an Excel spreadsheet using the function read.table() Save As… a tab-delimited text file.

  20. Data Frames from spreadsheets

  21. Data Frames from spreadsheets

  22. Data Frames from spreadsheets

  23. Lists

  24. Handling Flow Cytometry Data: flowCore • flowCore is a base package that supports reading and manipulation of FCS data files • The fundamental object that encapsulates the data in an FCS file is a flowFrame • A container object that holds a collection of flowFrames is called a flowSet • In the next slides we will go over • reading an FCS file • gating • compensation • transformation • visualization

  25. Check out the example data

  26. Read an FCS file, summarize the flowFrame

  27. Apply the lymphocyte gate with Subset

  28. needs to be transformed because it is rendering the linear data in the FCS file

  29. hasn’t been compensated!

  30. Lines require library(fields) • Percentages are in summary(fres)$p[1:4] • Percentages are drawn in the graph with text()

  31. Roederer M, Moore W, Treister A, Hardy RR & Herzenberg LA. Probability binning comparison: a metric for quantitating multivariate distribution differences. Cytometry 45:47-55, 2001. and Rogers WT, Moser AR, Holyst HA, Bantly A, Mohler ER III, Scangas G, and Moore JS, Cytometric Fingerprinting: Quantitative Characterization of Multivariate Distributions, Cytometry 73A: 430-441, 2008. Fingerprinting Flow Cytometry Data: flowFP • flowFP • aims to transform flow cytometric data into a form amenable to algorithmic analysis tools • Acts as in intermediate step between acquisition of high-throughput FCM data and empirical modeling, machine learning and knowledge discovery • Implements ideas from

  32. The basic idea • Subdivide multivariate space into bins • Call this a “model” of the space • For each flowFrame in a flowSet, count the number of events in each bin in the model • Flatten the collection of counts for a flowFrame into a 1D feature vector • Combine all of the feature vectors together into a n x m matrix • n = number of flowFrames (instances) • m = number of bins in the model (features) • Also, tag each event with its bin membership • facilitates visualization, interpretation • can be used for gating

  33. Probability Binning

  34. Probability Binning

  35. Probability Binning

  36. Bin Number Probability Binning > plot (mod, fs)

  37. Class Constructors • flowFPModel (base class) • Consumes a flowFrame or flowSet • Produces a model, which is a recipe for subdividing multivariate space • flowFP • Consumes a flowFrame or flowSet, and a flowFPModel • Produces a flowFP, which represents the multivariate probability density function as a fingerprint • Also tags each event with its bin membership • flowFPPlex • Consumes a collection of flowFPs • The flowFPPlex is a container object to facilitate handling large and complex collections of flowFPs

  38. Writing Your Own Functions # # It’s a good idea to comment your code # myfunc <- function (arg1=10, arg2, ...) { # your code goes here answer <- log (arg1, base=arg2) return (answer) } comments declaration assignment code block return

  39. Writing Your Own Functions

  40. Obtaining R and Bioconductor • R • http://cran.r-project.org/ • Bioconductor • http://bioconductor.org/GettingStarted

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