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I have a RMA normalized genes expression datset with 22810 rows and 9 columns( types of promoters) and a subset of the data is as follows:

ID_REF GSM362180    GSM362181  GSM362188    GSM362189  GSM362192
244901 5.094871713 4.626623079 4.554272515 4.748604391 4.759221647
244902 5.194528083 4.985930299 4.817426064 5.151654407 4.838741605
244903 5.412329253 5.352970877 5.06250609  5.305709079 8.365082403
244904 5.529220594 5.28134657  5.467445095 5.62968933  5.458388909
244905 5.024052699 4.714631878 4.792865831 4.843975286 4.657188246
244906 5.786557533 5.242403911 5.060605782 5.458148567 5.890061836

I want to do a clustering of the above and tried the hierarchical clustering:

d <- dist(as.matrix(deg), method = "euclidean")

where deg is the a matrix of the differentially expressed genes ( 4300 in number ).And I get the following warning:

  Warning message:
 In dist(as.matrix(deg), method = "euclidean") : NAs introduced by coercion

Is it allright to proceed with the clustering inspite of the warning ?

hc <- hclust(d)
plot(hc, hang = -0.01, cex = 0.7)

I get a dendrogram which is very dense and the labels are not clear: Also I do not know which of the 9 promoters are classified in the tree for the several genes: How would it be possible to label the tree with the promoters and also how to visualize the genes into a clearer dendrogram? Iam not sure how I have to add the dendrogram here else I would .

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1 回答 1

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Following your comment, I can't reproduce your error. I read in the data:

##Read in the data
deg = read.table(textConnection("ID_REF GSM362180    GSM362181  GSM362188    GSM362189  GSM362192
244901 5.094871713 4.626623079 4.554272515 4.748604391 4.759221647
244902 5.194528083 4.985930299 4.817426064 5.151654407 4.838741605
244903 5.412329253 5.352970877 5.06250609  5.305709079 8.365082403
244904 5.529220594 5.28134657  5.467445095 5.62968933  5.458388909
244905 5.024052699 4.714631878 4.792865831 4.843975286 4.657188246
244906 5.786557533 5.242403911 5.060605782 5.458148567 5.890061836"), header=TRUE)

I can then calculate the distance matrix:

R> dist(as.matrix(deg), method = "euclidean")
      1     2     3     4     5
2 1.173                        
3 4.266 3.701                  
4 3.423 2.288 3.120            
5 4.011 3.038 4.312 1.814      
6 5.282 4.204 3.912 2.109 1.957
于 2012-10-23T13:06:10.233 回答