Question: high poportion of identical expression measure with RMA and 3 arrays
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15.4 years ago by
I was pondering about the issue of minimum number of required arrays for RMA and GCRMA when I came across a thread by James MacDonald http://files.protsuggest.org/biocond/html/3347.html. He mentioned that median polish has a peculiar feature that returned identical values for odd number of arrays, especially 3 arrays. I am not sure if this issue has been addressed since but here is some empirical evidence to support this in case anyone is interested. ### Codes ### library(affy) v <- rep(NA, 10) for(i in 2:10){ a <- exprs( justRMA( filenames=list.celfiles()[1:i] )) v[i] <- mean( apply(a, 1, var) == 0 ) # proportion identical gc(); print(i) } 100*v ### Results ### # Dataset 1 : hu6800 (west) > 100 * v [1] NA 0.0000000 13.1434984 0.0000000 0.1262449 0.0000000 [7] 0.0000000 0.0000000 0.0000000 0.0000000 # Dataset 2 : hgu-95av2 (febbo) > 100 * v [1] NA 0.007920792 7.912871287 0.000000000 0.095049505 0.000000000 [7] 0.000000000 0.000000000 0.000000000 0.000000000 # Dataset 3 : HGU-133A (pga) > 100 * v [1] NA 1.5348023 16.0750348 0.0000000 0.3590181 0.0000000 [7] 0.0000000 0.0000000 0.0000000 0.0000000 When there are 3 arrays, there is a high proportion of probesets with identical values (i.e. zero variance). I am trying to see if the same effect can be seen with justGCRMA. misc : affy 1.4.32, gcrma 1.1.0, R-1.9.0 (12/04/2004) and FC 2.
hu6800 affy gcrma • 458 views
modified 15.4 years ago • written 15.4 years ago by Adaikalavan Ramasamy1.8k
Answer: high poportion of identical expression measure with RMA and 3 arrays
0
15.4 years ago by