1. © 2015 by Wade Rogers Introduction to R Cytomics Workshop December, 2015

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

3. R 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

5. 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

7. Flow Cytometry in Bioconductor About 40 packages specific to flow cytometry available in Bioconductor What’s so different about flow cytometry anyway?

8. 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 and be confident that the results are valid for all of the wells?

9. A motivating example

10. 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?

11. Another motivating example

12. A third example I often drain my tubes since I’m trying to acquire as many events as I can from a limited sample for a rare event assay. I’m concerned that the disruption of flow near the beginning and end of the acquisition (and sometimes in the middle due to minor clogs) may introduce an “artificial phenotype”. Is there some way to automatically detect and edit out portions of a file that aren’t consistent with the rest?

13. A third example

14. 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)

15. 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

16. Rstudio

17. Console

18. Rstudio Editor

19. Rstudio Env, History

20. Rstudio Your best friend

21. Rstudio lower right pane

25. Rstudio help

29. Package Vignette – really good help!

30. BASIC DATA STRUCTURES

31. Sequences and Repeats

32. Characters and Numbers Characters and character strings are enclosed in “” or ‘’ Special numbers NA – “Not Available” Inf – “Infinity” NaN – “Not a Number”

33. Factors Factors capture categorical data (variables that take on discrete, often descriptive, values) We’ll see more about factors when we talk about data frames …

34. Vectors and Matrices

35. The subset operator for vectors and matrices is [ ]

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

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

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

39. Data Frames from spreadsheets

42. Lists