Practical_comprehensive.md

Introduction to R practice

Creating vectors/factors and data frames

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

2. 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 using []:
   • return the treatment values for samples 5, 7, 9, and 10 using []:
   • remove the treatment column from the dataset using []:
   • remove samples 7, 8 and 9 from the dataset using []:
   • keep only samples 1-6 using []:
   • output a data frame with a column called pre_treatment as the first column with the values T, F, F, F, T, T, F, T, F, F, T, T (Hint: use data.frame or cbind()):
   • change the names of the columns to: "A", "B", "C", "D":

Extracting components from lists

3. Create a new list, list_three with three components, the pre_treatment vector, the dataframe meta, and vector stage. Use this list to answer the questions below . list_three has the following structure (NOTE: the components of this list are not currently named):

      [[1]]
       [1]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE
      [12]  TRUE
   
      [[2]]
               A  B C    D
      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
   
      [[3]]
       [1] "I"  "II" "II" "I"  "II" "II" "I"  "II" "II" "I"  "II" "II"
   

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 2343 from the second component of the list:
• return all "I" values from the third component:
• give the components of the list the following names: "pre_treatment", "meta", "stage":

Creating figures with ggplot2

4. 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 ("Mov10_full_meta.txt") to a variable called counts_meta using the read.delim() function. Be sure to specify the row names are in column 1 and the delimiter/column separator is a tab ("/t").

   • Read in the count matrix file ("normalized_counts.txt") to a variable called data using the read.delim() function and specifying there are row names in column 1 and the tab delimiter.

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

   • Check the class of expression. Convert it to a numeric vector using as.numeric(expression)

   • Bind that vector to your metadata data frame (counts_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 and center align

       • 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

       • Rotate the x-axis text to 45 degrees using axis.text.x=element_text(angle=45, hjust=1)

5. Save the plot as a PDF to the figures directory.

Packages and installations

6. Install the tidyverse R package from the CRAN repository and load the library.

7. Install the biomaRt R package from the Bioconductor repository and load the library.

Building on the basics

The Tidyverse suite of integrated packages are R packages designed to work together to make common data science operations more user friendly. The packages have functions for data wrangling, tidying, reading/writing, parsing, and visualizing, among others.

8. Using this tutorial, explore some of the functionality for reading in and wrangling data with the readr and dplyr packages, which were installed when you installed the tidyverse suite in the previous section.

9. Turn the meta data frame from question #1 of the "Creating vectors/factors and data frames" section above into a tibble called meta_tb. (Hint: Be sure to turn the rownames into a column before changing it into a tibble.)

10. Using meta_tb, write out the R code you would use to rename the columns back to sex, stage, treatment, and myc using rename(). Save back (overwrite) to the meta_tb variable.

11. Using meta_tb, write out the R code you would use to perform the following operations (questions DO NOT build upon each other):

    • use filter() to return all data for those samples receiving treatment P.
    • use filter()/select() to return only the stage and treatment columns for those samples with myc > 5000.
    • use arrange() to arrange the rows by myc in descending order.

12. Write meta_tb to a comma separated value file using the write_delim() function.