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.

Here are the results for GC-RMA for the same datasets as below : # Dataset 1 > print( 100*v ) [1] NA 3.54888484 5.62491233 0.00000000 0.07013606 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 # Dataset 2 > print( 100*v ) [1] NA 0.000000 3.366337 0.000000 0.000000 0.000000 0.000000 0.000000 [9] 0.000000 0.000000 # Dataset 3 > print( 100*v ) [1] NA 0.1077054 9.0562312 0.0000000 0.1750213 0.0000000 0.0000000 [8] 0.0000000 0.0000000 0.0000000 On Mon, 2004-09-13 at 18:12, Adaikalavan Ramasamy wrote: > 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. > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor >