RStudio

knitr::opts_chunk$set(
  eval = FALSE,
  comment = "#>",
  fig.align = "center"
)

Question 1

Write a function that plots rate vs expression when provided a data.frame, a systematic_name, and a nutrient. Add the common gene name as a title and the nutrient as a subtitle. Include an argument that lets the user select whether a regression line is shown.

plot_expr <- function(input, sys_name, nutri, add_lm = F) {

  # Finish writing the function

}

plot_expr(
  input = brauer_gene_exp, 
  sys_name = "YDL104C",
  nutri = "Ammonia",
  add_lm = T
)

Question 2

Add an if statement to check whether the systematic_name and nutrient provided by the user are present in the input data.

plot_expr <- function(input, sys_name, nutri, add_lm = F) {

  # Finish writing the function

}

plot_expr(
  input = brauer_gene_exp, 
  sys_name = "YDL104C",
  nutri = "Ammonia",
  add_lm = T
)

Question 3

Add the ellipsis so the user can pass arguments directly to theme().

plot_expr <- function(input, sys_name, nutri, add_lm = F) {

  # Finish writing the function

}

plot_expr(
  input = brauer_gene_exp, 
  sys_name = "YDL104C",
  nutri = "Ammonia",
  add_lm = T,
  axis.title = element_text(size = 20),
  axis.text = element_text(face = "italic")
)

Question 4

Use your function with map() to create a figure containing plots for four genes

sys_names <- c("YNL049C", "YML017W", "YDL104C", "YLR115W")

expr_plots <- map(

  # Add the arguments for map()

)

plot_grid(plotlist = expr_plots)

Question 5

Included in the package is a matrix called tx_rates containing transcription rates for ~7000 genes. Transcription rates were measured every 15 minutes for 3 hours after exposure of dendritic cells to LPS, which activates innate immunity responses. The processed data was obtained from GEO

For this exercise perform the following steps:

1) Exclude genes that have rates of 0 across all samples

2) Log transform the data (add a pseudocount of 1 to avoid infinite values)

3) Normalize the data to the 0 hour time point (i.e. subtract the "rate at 0" from all of the columns )

4) run kmeans clustering on the log transformed and normalized matrix. Use k = between 5 and 10 clusters.

5) Make a heatmap with rows split based on the clusters identified in step 4. This may take a few minutes to plot, depending on your computers resources. Use the provided pseudo-code below to generate a color palette. Discuss any interesting patterns that you find in your clusters in the interpretation section.

Note that plotting the entire ~7K row matrix may take a few minutes. If you are having trouble plotting the entire matrix, then you can plot just a subset using the code below

i.e

# just use first 1000 rows
tx_rates_smaller <- tx_rates[1:1000, ]

# or using the `sample()` function to randomly select 1000 rows
row_index <- sample(1:nrow(tx_rates), 1000)

tx_rates_smaller <- tx_rates[row_index, ]

Strategy

cols_to_use <- circlize::colorRamp2(c(-4, 0, 4), 
                                    c("blue", "white", "red"))

# pseudocode
Heatmap(mat, col = cols_to_use, ...