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  • what is the name of this kind of graph and how to plot?

    Do anyone know about the graph attached and how to plot it ?
    any packages?

    thanks
    Attached Files

  • #2
    It is a scatterplot, you can make one as plotting normalized values from 2 samples by log10 scale. But most probably you will have difficulties making coloring by condition (your log2ratio for deregulated genes ) in simple software like Excel.
    Last edited by vebaev; 10-02-2011, 08:33 AM.
    ------------
    SMART - bioinfo.uni-plovdiv.bg

    Comment


    • #3
      This plot is basically for visualising DGE results and it is called upon by using plotSmear function in edgeR.

      I have copied the text from edgeR for better understanding.

      "The function plotSmear can be used to generate a plot of the log-fold change against the log-
      concentration for each tag (analogous to an MA-plot in the microarray context). We can easily
      identify the top DE tags and highlight them on the plot. The code for producing the default
      fold-change plot is shown below, and the result of this code is shown in Figure 2.
      > detags264 <- rownames(topTags(de.com, n = 264)$table)
      > plotSmear(d, de.tags = detags264, main = "FC plot using common dispersion")
      > abline(h = c(-2, 2), col = "dodgerblue")"

      I kind of like this plot but i still don't know how to use different colors for different things.

      I hope this info helps

      Comment


      • #4
        Originally posted by upendra_35 View Post
        This plot is basically for visualising DGE results and it is called upon by using plotSmear function in edgeR.

        I have copied the text from edgeR for better understanding.

        "The function plotSmear can be used to generate a plot of the log-fold change against the log-
        concentration for each tag (analogous to an MA-plot in the microarray context). We can easily
        identify the top DE tags and highlight them on the plot. The code for producing the default
        fold-change plot is shown below, and the result of this code is shown in Figure 2.
        > detags264 <- rownames(topTags(de.com, n = 264)$table)
        > plotSmear(d, de.tags = detags264, main = "FC plot using common dispersion")
        > abline(h = c(-2, 2), col = "dodgerblue")"

        I kind of like this plot but i still don't know how to use different colors for different things.

        I hope this info helps
        thanks, upendra_35
        but i thought the function plotSmear in edgeR is not capable of plotting the graph i have attached but the graph i attached this time.

        correct me if i am wrong
        Attached Files

        Comment


        • #5
          As far as i know plotSmear can only plot the first plot you have attached. I don't know much about the second plot, but i guess it is other way of representing DGE (red indicates Non-DE genes, black indicates under expressed and pink indicates over expressed genes). Where did you get this plot anyway?

          Comment


          • #6
            Originally posted by upendra_35 View Post
            As far as i know plotSmear can only plot the first plot you have attached. I don't know much about the second plot, but i guess it is other way of representing DGE (red indicates Non-DE genes, black indicates under expressed and pink indicates over expressed genes). Where did you get this plot anyway?
            well, upendra_35, thanks,.

            the first graph i attached is from a paper has been published and the second one was generated from R package DEGseq.

            i have read the edgeR manual and the examples and found the plotSmear function in the manual and graph in the example are more like the second graph i have attached.

            by the way, red ones in the second one mean DEGs and pink ones mean expressed genes that do not meet the threshold of p value and changefold i have set and the black ones are non-DEGs.

            Comment


            • #7
              Thanks tianyub836 for the info. Much appreciated........

              Comment

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