Summary of work done in spreadsheet

Using excel’s conditional formatting, the sequences were color coded. 10 polymorphic (at least dimorphic) loci were then haphazardly selected from the chimp alignment. The number of alleles per locus were tallied and a matrix was created for the pair-wise similarities. Then pairwise similarity was calculated to fill in the similarity matrix. This was then converted so a dissimilarity matrix.

Preliminaries

# install.packages(ape)
# install.packages(phangorn)

library(ape)
library(phangorn)

Example 1: 3 Sequences

Similarity matrix

This matrix is based on the proportion of bases that are identical between sequence. This is often referred to as PID for Proportion Identical or Percentage Identical.

BLAST reports PID in its main output. PID is a very simple metric of similarity; more sophisticated measures are used in pratice.

Make a similarity matrix with the matrix() command. Note that I have to declare the number of rows

# Bad matrix 1
matrix(c(1.0, 0.5, 0.3,
         0.5, 1.0, 0.4,
         0.3, 0.4, 1.0))
##       [,1]
##  [1,]  1.0
##  [2,]  0.5
##  [3,]  0.3
##  [4,]  0.5
##  [5,]  1.0
##  [6,]  0.4
##  [7,]  0.3
##  [8,]  0.4
##  [9,]  1.0
# Good matrix
matrix(c(1.0, 0.5, 0.3,
         0.5, 1.0, 0.4,
         0.3, 0.4, 1.0),
       nrow = 3)
##      [,1] [,2] [,3]
## [1,]  1.0  0.5  0.3
## [2,]  0.5  1.0  0.4
## [3,]  0.3  0.4  1.0

Store the matrix

my_sim_mat <- matrix(c(1.0, 0.5, 0.3,
                       0.5, 1.0, 0.4,
                       0.3, 0.4, 1.0),
                 nrow = 3,
                 byrow = T)

Label the matrix with row.names() and colnames()

row.names(my_sim_mat) <- c("G","T","M") #column namesmy_
colnames(my_sim_mat) <- c("G","T","M")

Disimilarity matrix

Similarity, disimilarity, and distance are all related. Most methods use distance, not similarity.

We can do vectorized math to recalculate the matrix subtracting 1-matrix to give you opposite (so similarity of 30% becomes a dissimilarity (PID) of 70%) dissimilarity=distance

my_dist_mat <- 1-my_sim_mat

Convert to R’s distance format

as.dist() converts to a distance matrix

my_dist_mat2 <- as.dist(my_dist_mat)
my_dist_mat2
##     G   T
## T 0.5    
## M 0.7 0.6
my_dist_mat2
##     G   T
## T 0.5    
## M 0.7 0.6

my_dist_mat G T M G 0.0 0.5 0.7 T 0.5 0.0 0.6 M 0.7 0.6 0.0

my_dist_mat2 #tossed out the duplicate information

G   T

T 0.5
M 0.7 0.6

G-T shortest distance

is(my_dist_mat2)
## [1] "dist"
class(my_dist_mat2)
## [1] "dist"

Build a neighbor-joining (nj) tree

Neighbor Joining is one of the most common ways to build a tree using molecular data that’s been converted to sequences; its one of the options within BLAST.

Build the tree with nj()

my_nj <- ape::nj(my_dist_mat2)

Plot the tree as an “unrooted” tree #turned the matrix into a visual representation of the data

plot(my_nj, "unrooted")

Plot the tree as an “rooted” tree

plot(my_nj)

UPGMA/WPGMA are other algorithms that work with distance matrices. They are not commonly used now but are useful for teaching becaues they can easily be done by hand on small datasets.

my_upgma <- phangorn::upgma(my_dist_mat2)

Plot the UPGMA tree

plot(my_upgma)

Compare the rooted NJ and the UPGMA see them side by side

par(mfrow = c(1,2))
plot(my_nj)
plot(my_upgma)

WPGMA tree

plot(wpgma(my_dist_mat2))

Minimum evolution tree

plot(fastme.ols(my_dist_mat2))

HOMEWORK ## Example 2: 5 Sequences

Build the matrix.

Be sure to add the nrow = … statemetn.

five_sim_mat <- matrix(c(1.0,     0.8,  0.7,  0.3,  0.2,        
                         0.8,   1.0,    0.7,  0.4,  0.3,                
                         0.7,   0.7,    1.0,    0.2,  0.1,      
                         0.3,     0.4,  0.2,    1.0,    0.9,        
                         0.2,   0.3,    0.1,    0.9,    1.0),       
                       nrow = 5, 
                       byrow = T)

Name columns and rows

row.names(five_sim_mat) <- c("M", "B", "G", "T", "L")
colnames(five_sim_mat) <- c("M", "B", "G", "T", "L")

Turn into a distnace matrix. This is 2 steps and requires the as.dist() command my_dist_mat <- 1-my_sim_mat

five_dist_mat <- 1-five_sim_mat
five_dist_mat2 <- as.dist(five_dist_mat)

Neighbor-Joining tree with nj()

five_nj <- nj(five_dist_mat2)

Plot unrooted NJ tree

plot(five_nj, "unrooted")

Plot rooted NJ tree

plot(five_nj)

my_upgma <- phangorn::upgma(my_dist_mat2)

my_upgma <- phangorn::upgma(five_dist_mat2)

Build UPGMA tree

five_upgma <- phangorn::upgma(five_dist_mat2)

Plot UPGMA tree

plot(five_upgma)

Compare rooted NJ and UPGMA plots

par(mfrow = c(1,2))
plot(five_nj)
plot(five_upgma)

Build WPGMA tree

plot(wpgma(five_dist_mat2))

Compare rooted WPGMA and UPGMA plots

par(mfrow = c(1,2))
plot(five_upgma)
plot(wpgma(five_dist_mat2))

Build Minimum evolution tree

plot(fastme.ols(five_dist_mat2))