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  • edgeR MDS plot: any way to get genes contributing to axes out?

    Hi All,

    I'm working with edgeR and have a MDS plot that is interesting but want to know which genes contribute to the two axes. Any suggestions of how to get this out?

    Cheers!

  • #2
    Hello! I am also interested in determining which genes contribute to the axes in my MDS plot. Did you ever find a way to do this? Thank you!

    Comment


    • #3
      No, I'm afraid I never figured it out. Perhaps this will spur others who might know how to share.

      Comment


      • #4
        Hi- I think you want to look at the rotations (or loadings) from a Principal Components Analysis (MDS is PCA where the data are a distance matrix).

        Let's see this example... You have 10 samples and 23 genes. Gene A and B are anticorrelated while C is highly expressed in samples 4, 5, 6, and 7. The remaining 20 genes do not show any pattern of expression:

        Code:
        n<- 10
        A<- 1:n
        B<- c(1, 1, 1, 10, 10, 10, 10, 2, 2, 2)
        C<- n:1
        k<- matrix(rnorm(n= 20 * n, mean= 5, sd= 0.5), ncol= n)
        dat<- as.matrix(rbind(A, B, C, k))
        rownames(dat)<- LETTERS[1:nrow(dat)]
        
        round(dat)
          [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
        A    1    2    3    4    5    6    7    8    9    10
        B    1    1    1   10   10   10   10    2    2     2
        C   10    9    8    7    6    5    4    3    2     1
        D    5    5    5    5    5    6    4    5    5     5
        E    5    5    5    5    5    6    6    5    4     5
        ...
        If you do a PCA and plot you see that the 10 samples form 3 groups, as expected:

        Code:
        pc<- prcomp(t(dat))
        plot(pc$x)
        
        # Or also:
        biplot(pc)
        For question "Which genes contribute to the separation", let's have a look at the rotations:

        Code:
        round(pc$rotation[1:10,1:2], 3)
             PC1    PC2
        A  0.408 -0.567
        B  0.797  0.581
        C -0.408  0.567
        D  0.068  0.031
        E  0.009 -0.018
        F -0.009 -0.006
        G  0.008 -0.042
        H -0.030 -0.014
        I -0.048  0.014
        J -0.009  0.015
        ...
        We can see that A, B, C contribute the most to the variation.

        (For this questions you might get better answers on the Bioconductor mailing list...)

        Hope this helps (and make sure I'm not missing anything...)

        Comment


        • #5
          Thanks for that. I was also sent this link which explains why they do not provide this information.

          Comment

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