Controls and duplicate correlation
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@lepalmernotesccsunysbedu-1254
Last seen 6.8 years ago
I was searching the archives for the warning message I got with duplicateCorrelation. I got that resolved but found this comment from Gordon from last November. "Secondly, assigning zero weight is not the way to remove missing or control spots. You should leave the weights as they are are and instead subject the data object. For example, isBlank <- (MA$genes$Status %in% c("blank","miss")) corfit <- duplicateCorrelation(MA[!isBlank,], design, ndups=1, block=pair) See the Weaver example in the Limma User's Guide for an example of the treatment of control spots. " I looked at the Weaver example, and wasnt sure what this last line referred to. I tried doing this to my data: isBlank <- (MA$genes$Status %in% c("Blank","Reserved")) corfit <- duplicateCorrelation(MA[!isBlank,], design, ndups=2,spacing=240) but got this error Error in unwrapdups(M, ndups = ndups, spacing = spacing) : dim<- : dims [product 57600] do not match the length of object [57948] Summary of isBlank Mode FALSE TRUE logical 9658 1862 The data consists of 3 sets of dye swaps, with 5760 spots printed twice on chip (11520 spots total). There are 'Blanks' (nothing printed) and 'Reserved' which is DNA from another species which should be expressed. These blanks and reserved are flagged in .gpr file and should have a weight of 0. My questions are: What am I doing wrong? Is it still necessary to do above to remove blanks and reserved? Is this removal of control spots required at any other time? I like having the reserved spots being analyzed, as they should not show up in statistically significant genes. If they do, then I know that I did not do the calculations correctly. Is it wrong to leave these in with the analysis (they are still weighted at 0) Thanks, Lance Palmer [[alternative HTML version deleted]]
limma weaver limma weaver • 652 views

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