Approximate time: 45 minutes

Learning Objectives

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.

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