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R_refresher.md

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R refresher
Meeta Mistry, Radhika Khetani, Mary Piper, Jihe Liu

Approximate time: 45 minutes

Learning Objectives

  • Describe the various data types and data structures (including tibbles) used by R
  • Use functions in R and describe how to get help with arguments
  • Describe how to install and use packages in R
  • Use the pipe (%>%) from the dplyr package
  • Describe the syntax used by ggplot2 for making plots

Setting up

  1. Let’s create a new project directory for this review:

    • Create a new project called R_refresher
    • Create a new R script called reviewing_R.R
    • Create the following folders in the project directory - data, figures
    • Download a counts file to the data folder by right-clicking here

  2. Now that we have our directory structure setup, let's load our libraries and read in our data:

    • Load the tidyverse library
    • Use read.csv() to read in the downloaded file and save it in the object/variable counts
      • What is the syntax for a function?
      • How do we get help for using a function?
    • What is the data structure of counts?
      • What main data structures are available in R?
    • What are the data types of the columns?
      • What data types are available in R?

Creating vectors/factors and dataframes

  1. We are performing RNA-Seq on cancer samples with genotypes of p53 wildtype (WT) and knock-down (KO). You have 8 samples total, with 4 replicates per genotype. Write the R code you would use to construct your metadata table as described below.

    • Create the vectors/factors for each column (Hint: you can type out each vector/factor, or if you want the process go faster try exploring the rep() function).
    • Put them together into a dataframe called meta.
    • Use the rownames() function to assign row names to the dataframe (Hint: you can type out the row names as a vector, or if you want the process go faster try exploring the paste0() function).

    Your finished metadata table should have information for the variables sex, stage, genotype, and myc levels:

    sex stage genotype myc
    KO1 M 1 KO 23
    KO2 F 2 KO 4
    KO3 M 2 KO 45
    KO4 F 1 KO 90
    WT1 M 2 WT 34
    WT2 F 1 WT 35
    WT3 M 1 WT 9
    WT4 F 2 WT 10

Exploring data

Now that we have created our metadata data frame, it's often a good idea to get some descriptive statistics about the data before performing any analyses.

  • Summarize the contents of the meta object, how many data types are represented?
  • Check that the row names in the meta data frame are identical to the column names in counts (content and order).
  • Convert the existing stage column into a factor data type

Extracting data

  1. Using the meta data frame created in the previous question, perform the following exercises (questions DO NOT build upon each other):

    • return only the genotype and sex columns using []:
    • return the genotype values for samples 1, 7, and 8 using []:
    • use filter() to return all data for those samples with genotype WT:
    • use filter()/select()to return only the stage and genotype columns for those samples with myc > 50:
    • add a column called pre_treatment to the beginning of the dataframe with the values T, F, T, F, T, F, T, F
      • why might this design be problematic?
    • Using %>% create a tibble of the meta object and call it meta_tb (make sure you don't lose the rownames!)
    • change the names of the columns to: "A", "B", "C", "D", "E":

Visualizing data

  1. Often it is easier to see the patterns or nature of our data when we explore it visually with a variety of graphics. Let's use ggplot2 to explore differences in the expression of the Myc gene based on genotype.

    • Plot a boxplot of the expression of Myc for the KO and WT samples using theme_minimal() and give the plot new axes names and a centered title.

Preparing for downstream analysis tools

  1. Many different statistical tools or analytical packages expect all data needed as input to be in the structure of a list. Let's create a list of our count and metadata in preparation for a downstream analysis.

    • Create a list called project1 with the meta and counts objects, as well as a new vector with all the sample names extracted from one of the 2 data frames.

Rscript with answers