High Throughput Data Analysis (Life Science) Week One, Distance

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library(devtools)
install_github("genomicsclass/tissuesGeneExpression")

Skipping install of 'tissuesGeneExpression' from a github remote, the SHA1 (a43cf4b5) has not changed since last install.
  Use `force = TRUE` to force installation

library(tissuesGeneExpression)
data(tissuesGeneExpression)

(still introduction)

#head(e) #too long dawg
head(tissue)

[1] "kidney" "kidney" "kidney" "kidney" "kidney" "kidney"

The data represents RNA expression levels for seven tissues, each with several biological replicates. We call samples that we consider to be from the same population, such as liver tissue from different individuals, biological replicates:

Question 1: How many biological replicates are there for hippocampus?

table(tissue)

tissue
 cerebellum       colon endometrium hippocampus      kidney       liver 
         38          34          15          31          39          26 
   placenta 
          6 

The answer is 31.

Question 2: What is the distance between samples 3 and 45?

x <- e[,3]
y <- e[,45]
sqrt(sum((x-y)^2))

[1] 152.5662

sqrt(crossprod(x-y))

         [,1]
[1,] 152.5662

tissue[c(3,45)]

[1] "kidney"      "hippocampus"

#d #too long, again, try if u want. but this is one of the methods to know the distances.
d <-dist(t(e))
as.matrix(d)[3,45]

[1] 152.5662

Answer: 152.5662

Question 3: what is the distance between gene 210486_at and 200805_at?

j <- e['210486_at',]
k <- e['200805_at',]

sqrt(crossprod(j-k))

         [,1]
[1,] 41.01153

Answer: 41.01153

Question 4: If I run the command (don’t run it!):

#d = as.matrix(dist(e))

How many cells (number of rows times number of columns) would this matrix have?

22215*22215

[1] 493506225

Answer: 493506225

Question 5: Compute the distance between all pairs of samples:

d = dist(t(e)) Read the help file for dist().

How many distances are stored in d? (Hint: What is the length of d)?

length(d)

[1] 17766

Answer: 17766

Question 6: Why is the answer above not ncol(e)^2?

Note that the distance between samples i and j is the same as distance between samples j and i. Also the distance between a sample and itself is 0. The object returned by dist() avoids storing all those values.

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