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Add few more parts to correlation lesson #3

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Add few more parts to correlation lesson #3

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59 changes: 57 additions & 2 deletions lab01_correlation/lesson.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@
Hint: Try copying the read.csv code from the text above.

- Class: cmd_question
Output: "Next, we shall load the gene expression dataset. rnaseq <- read_csv("data/pannets_expr_rnaseq.csv.gz")'"
Output: "Next, we shall load the RNAseq gene dataset. rnaseq <- read_csv("data/pannets_expr_rnaseq.csv.gz")'"
CorrectAnswer: rnaseq <- read_csv("data/pannets_expr_rnaseq.csv.gz")
AnswerTests: omnitest(correctExpr = 'rnaseq <- read_csv("data/pannets_expr_rnaseq.csv.gz")')
Hint: Try copying the read.csv code from the text above.
Expand All @@ -36,7 +36,62 @@
CorrectAnswer: head(rnaseq)
AnswerTests: omnitest(correctVal = 'head(rnaseq)')
Hint: Check the output of 'head(rnaseq)'

- Class: cmd_question
Output: "Now we need to create pivot table in long form for expression data. columns should be -Gene, names_to should be Tumor and values_to should be Expr. Store it into rnaseq_long."
CorrectAnswer: rnaseq_long <- pivot_longer(rnaseq, cols = -Gene, names_to = "Tumour", values_to = "Expr")
AnswerTests: omnitest(correctExpr = 'rnaseq_long <- pivot_longer(rnaseq, cols = -Gene, names_to = "Tumour", values_to = "Expr")')

- Class: cmd_question
Output: "Use the Pivot table created before to create another pivot table in wide form. Identifier columns should be Tumour, names parameter should be Gene and values should be obtained from Expr."
CorrectAnswer: rnaseq_wide <- pivot_wider(rnaseq_long, id_cols = Tumour, names_from = Gene, values_from = Expr)
AnswerTests: omnitest(correctExpr = 'rnaseq_wide <- pivot_wider(rnaseq_long, id_cols = Tumour, names_from = Gene, values_from = Expr)')


- Class: cmd_question
Output: "Next, we shall load the microarray gene expression dataset. array <- read_csv("data/pannets_expr_array.csv.gz")'"
CorrectAnswer: array <- read_csv("data/pannets_expr_array.csv.gz")
AnswerTests: omnitest(correctExpr = 'array <- read_csv("data/pannets_expr_array.csv.gz")')
Hint: Try copying the read.csv code from the previous commands.


- Class: cmd_question
Output: "Now we need to create pivot table in long form for gene expression data. The pivot table should be stored in array_long."
CorrectAnswer: array_long <- pivot_longer(array, cols = -Gene, names_to = "Tumour", values_to = "Expr")
AnswerTests: omnitest(correctExpr = 'array_long <- pivot_longer(array, cols = -Gene, names_to = "Tumour", values_to = "Expr")')
Hint: Look at the command we executed previously for the idea.

- Class: cmd_question
Output: "Now we create a Pivot table in Long form using the array_long variable. The data should be stored in array_wide."
CorrectAnswer: array_wide <- pivot_wider(array_long, id_cols = Tumour, names_from = Gene, values_from = Expr)
AnswerTests: omnitest(correctExpr = 'array_wide <- pivot_wider(array_long, id_cols = Tumour, names_from = Gene, values_from = Expr)')
Hint: Look at the command we have written to create a wide pivot table for RNASeq data.

- Class: text
Output: Now we compare ACTB gene expression between RNA-seq and microarray data. For this we first need to create a dataframe, create a scatterplot between RNASeq Gene Expression vs microarray gene expression.

- Class: cmd_question
Output: "Lets start by creating a data frame for Tumor vs ACTB gene expression. For this we need to create three columns- tumour, rnaseq and array and obtain data for these columns from the rnaseq_wide and array_wide data for ACTB"
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Put an overview of our datasets here and review what we've done to prepare each of them, and how the mapping of values is going to occur for when we plot it.

CorrectAnswer: actb <- data.frame(tumour = rnaseq_wide$Tumour, rnaseq = rnaseq_wide$ACTB, array = array_wide$ACTB)
AnswerTests: omnitest(correctExpr = 'actb <- data.frame(tumour = rnaseq_wide$Tumour, rnaseq = rnaseq_wide$ACTB, array = array_wide$ACTB)')



- Class: cmd_question
Output: "Now lets create a scatter plot to find the relation between RNASeq Gene Expression vs Microarray Gene Expression"
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expand on the purpose of doing this and what the student should look for in the ggplot, what kind of correlations will they see? maybe explain basics around scatterplots and what they are best used for.

CorrectAnswer: ggplot(actb, aes(x = rnaseq, y = array)) + geom_point() + labs(title = "ACTB expression")
AnswerTests: omnitest(correctExpr = 'ggplot(actb, aes(x = rnaseq, y = array)) + geom_point() + labs(title = "ACTB expression")')

- Class: text
Output: For calculating correlations between two variables, we have the cor() function which takes as parameters the variables between which the correlations need to be calculated and the method of calculation for correlation.

- Class: cmd_question
Output: "Let us first calculate the correlation between RNAseq and microarray using pearson method."
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Explain Pearson vs Spearman correlation in more detail, what are advantages to each method? Have the student compare the outputs to both and how it may affect their interpretation of the correlation.

CorrectAnswer: cor(actb$rnaseq, actb$array, method = "pearson")
AnswerTests: omnitest(correctExpr = 'cor(actb$rnaseq, actb$array, method = "pearson")')

- Class: cmd_question
Output: "Let us first calculate the correlation between RNAseq and microarray using spearman method."
CorrectAnswer: cor(actb$rnaseq, actb$array, method = "pearson")
AnswerTests: omnitest(correctExpr = 'cor(actb$rnaseq, actb$array, method = "spearman")')