Introduction to R practice

Creating vectors/factors and dataframes

1. We are performing RNA-Seq on cancer samples being treated with three different types of treatment (A, B, and P). You have 12 samples total, with 4 replicates per treatment. 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 paste() function).

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

   	sex	stage	treatment	myc
   sample1	M	I	A	2343
   sample2	F	II	A	457
   sample3	M	II	A	4593
   sample4	F	I	A	9035
   sample5	M	II	B	3450
   sample6	F	II	B	3524
   sample7	M	I	B	958
   sample8	F	II	B	1053
   sample9	M	II	P	8674
   sample10	F	I	P	3424
   sample11	M	II	P	463
   sample12	F	II	P	5105

Subsetting vectors/factors and dataframes

1. Using the meta data frame from question #1, write out the R code you would use to perform the following operations (questions DO NOT build upon each other):

   • return only the treatment and sex columns:
   • return the treatment values for samples 5, 7, 9, and 10:
   • use subset() to return all data for those samples receiving treatment P:
   • use subset() to return only the stage and treatment data for those samples with myc > 5000:
   • remove the treatment column from the dataset:
   • remove samples 7, 8 and 9 from the dataset:
   • keep only samples 1-6:
   • add a column called pre_treatment to the beginning of the dataframe with the values T, F, F, F, T, T, F, T, F, F, T, T (Hint: use cbind()):
   • change the names of the columns to: “A”, “B”, “C”, “D”:

Extracting components from lists

1. Create a new list, list3 with three components, the glengths vector, the dataframe df, and number value. Use this list to answer the questions below . list3 has the following structure (NOTE: the components of this list are not currently named):

      [[1]]
      [1]   4.6  3000.0 50000.0 
   
      [[2]]
             species  glengths 
        1    ecoli    4.6
        2    human    3000.0
        3    corn     50000.0
   
      [[3]]
      [1] 8
   

Write out the R code you would use to perform the following operations (questions DO NOT build upon each other):

• return the second component of the list:
• return 50000.0 from the first component of the list:
• return the value human from the second component:
• give the components of the list the following names: “genome_lengths”, “genomes”, “record”:

Creating figures with ggplot2

1. Create the same plot as above using ggplot2 using the provided metadata and counts datasets. The metadata table describes an experiment that you have setup for RNA-seq analysis, while the associated count matrix gives the normalized counts for each sample for every gene. Download the count matrix and metadata using the links provided.

   Follow the instructions below to build your plot. Write the code you used and provide the final image.

   • Read in the metadata file using: meta <- read.delim("Mov10_full_meta.txt", sep="\t", row.names=1)

   • Read in the count matrix file using: data <- read.delim("normalized_counts.txt", sep="\t", row.names=1)

   • Create a vector called expression that contains the normalized count values from the row in normalized_counts that corresponds to the MOV10 gene.

   • Check the class of this expression vector. You will need to convert this to a numeric vector using as.numeric(expression)

   • Bind that vector to your metadata data frame (meta) and call the new data frame df.

   • Create a ggplot by constructing the plot line by line:

     • Initialize a ggplot with your df as input.

     • Add the geom_jitter() geometric object with the required aesthetics which are x and y.

     • Color the points based on sampletype

     • Add the theme_bw() layer

     • Add the title “Expression of MOV10” to the plot

     • Change the x-axis label to be blank

     • Change the y-axis label to “Normalized counts”

     • Using theme() change the following properties of the plot:

       • Remove the legend (Hint: use ?theme help and scroll down to legend.position)

       • Change the plot title size to 1.5x the default

       • Change the axis title to 1.5x the default size

       • Change the size of the axis text only on the y-axis to 1.25x the default size

Practice with nested functions (optional)

Let’s derive some nested functions similar to those we will use in our RNA-Seq analysis. The following dataframes, value_table and meta, should be used to address the questions below (you do not actually need to create these dataframes):

value_table

meta

1. We would like to count the number of samples which have normal Mov10 expression (MOVexpr) in the meta dataset. Let’s do this in steps:

   • Write the R code you would run to return the row numbers of the samples with MOVexpr equal to “normal”:

   • Write the R code you would run to determine the number of elements in the MOVexpr column:

   • Now, try to combine your first two actions into a single line of code using nested functions to determine the number of elements in the MOVexpr column with expression levels of MOV10 being normal:

2. Now we would like to add the MX1 and MX3 columns to the meta data frame. Let’s do this in steps:

   • Write the R code you would run to extract columns MX1 and MX3 from the value_table and to save it to a variable mx (hint: you will need to use the c() function to specify the columns you want to extract):

   • Using the cbind() function, write the R code you would use to add the columns in your mx variable to the end of your meta dataset :

   • Now, try to combine your first two actions into a single line of code using nested functions (hint: you do not need to generate the mx variable) to add the MX1 and MX3 columns to the meta file:

3. Finally, we would like to extract only those rows from the meta dataset for replicate 2 from all conditions (KD.2, OE.2, IR.2). Let’s do this in steps:

   • Write the function you would use to determine the row names of the meta dataset:

   • Using the which() function, write the R code you would run to determine the location of the row name KD.2 in the meta dataset:

   • Using the which() function, write the R code you would use to determine the location of row names KD.2, OE.2, and IR.2 in the meta dataset (use the OR operator (

     ) to return multiple locations):

   • Now, extract the rows from the meta dataset with row names KD.2, OE.2, and IR.2 using a single line of code using nested functions: