问题
I am working to have a phylogenetic tree based on pairwise-data of genes.Below is my subset of the data(test.txt).The tree does not has to be constructed on the basis of any DNA sequences,but just treating it as words.
ID gene1 gene2
1 ADRA1D ADK
2 ADRA1B ADK
3 ADRA1A ADK
4 ADRB1 ASIC1
5 ADRB1 ADK
6 ADRB2 ASIC1
7 ADRB2 ADK
8 AGTR1 ACHE
9 AGTR1 ADK
10 ALOX5 ADRB1
11 ALOX5 ADRB2
12 ALPPL2 ADRB1
13 ALPPL2 ADRB2
14 AMY2A AGTR1
15 AR ADORA1
16 AR ADRA1D
17 AR ADRA1B
18 AR ADRA1A
19 AR ADRA2A
20 AR ADRA2B
Below is my code in R
library(ape)
tab=read.csv("test.txt",sep="\t",header=TRUE)
d=dist(tab,method="euclidean")
fit <- hclust(d, method="ward")
plot(as.phylo(fit))
My figure is attached here
I have a question on how they are clustered.Since the pairs
17 AR ADRA1B
18 AR ADRA1A
and
2 ADRA1B ADK
3 ADRA1A ADK
should be clustered closely because they have one common gene.so 17 and 2 should be together,and 18 and 3.
Should I use any other method,if I am wrong in using this method(Euclidean distance)?
Should I convert my data to a matrix of rows and columns ,where gene1 is x-axis ,and gene2 is y-axis,each cell being filled by 1 or 0?(Basically if they are paired would mean 1, and if not then 0)
Updated Code :
table=table(tab$gene1, tab$gene2)
d <- dist(table,method="euclidean")
fit <- hclust(d, method="ward")
plot(as.phylo(fit))
However, in this I get only the genes from gene1 and not gene2 column.The below figure is exactly what I want but should have genes from gene2 column as well
回答1:
There is some room for interpretation in the example of the question. My answer is only valid if there are really only two genes present in each individual and each row describes an individual. If, however, each row means that gene1 occurs with gene2 with certainty no useful clustering can be performed, in my opinion. In that case I would expect an additional column stating the probability for their common occurrence and something like an principal component analysis (PCA) may be preferred, but I am far away from being an expert on (hierarchial) clustering.
Before you can use the dist function, you have to bring your data into an appropriate format:
# convert test data into suitable format
gene.names <- sort(unique(c(tab[,"gene1"],tab[,"gene2"])))
gene.matrix <- cbind(tab[,"ID"],matrix(0L,nrow=nrow(tab),ncol=length(gene.names)))
colnames(gene.matrix) <- c("ID",gene.names)
lapply(seq_len(nrow(tab)),function(x) gene.matrix[x,match(tab[x,c("gene1","gene2")],colnames(gene.matrix))]<<-1)
The obtained gene.matrix has the shape:
ID ACHE ADK ADORA1 ADRA1A ADRA1B ADRA1D ADRA2A ...
[1,] 1 0 1 0 0 0 1 0
[2,] 2 0 1 0 0 1 0 0
[3,] 3 0 1 0 1 0 0 0
[4,] 4 0 0 0 0 0 0 0
...
So each row represents an observation (=individual) where the first column identifies the individual and each of the subsequent columns contains 1 if the gene is present and 0 if it is missing. On this matrix the dist function can be reasonably applied (ID column removed):
d <- dist(gene.matrix[,-1],method="euclidean")
fit <- hclust(d, method="ward")
plot(as.phylo(fit))
Maybe, it is a good idea to read up the differences between the distance measures euclidean, manhattan etc. For instance, the euclidian distance between the individuals with ID=1 and ID=2 is:
euclidean_dist = sqrt((0-0)^2 + (1-1)^2 + (0-0)^2 + (0-0)^2 + (0-1)^2 + ...)
whereas the manhattan distance is
manhattan_dist = abs(0-0) + abs(1-1) + abs(0-0) + abs(0-0) + abs(0-1) + ...
来源：https://stackoverflow.com/questions/21839012/phylogenetic-tree