[BioC] how to rank affy probesets by their probe-effect magnitude

[Robert Castelo]

Matt,

does probeVec represents some sort of baseline expression for each 
specific probe?

does it make sense to calculate these vectors for another affy chip with 
20 samples?

thanks!
robert.

On 3/5/12 7:52 PM, Matthew McCall wrote:
> Robert,
>
> I'm not sure exactly what you're after, but you might want to look at
> the hgu133afrmavecs and hgu133plus2frmavecs data packages. The
> probe-effect (probeVec), within-batch residual variance
> (probeVarWithin), the between-batch residual variance
> (probeVarBetween), and the within probeset standard deviation
> (probesetSD) have all been computed using a large biologically diverse
> data set.
>
> Best,
> Matt
>
> On Mon, Mar 5, 2012 at 1:22 PM, Robert Castelo<robert.castelo at upf.edu>  wrote:
>> dear list,
>>
>> i'm searching for a way to rank affy probesets from classical 3' affy
>> arrays by their probe effect magnitude. i mean that i would like to know
>> if a probeset is has a larger probe-specific effect than another one.
>>
>> i guess the solution should be in the affyPLM package since if i do
>>
>> library(affy)
>> library(affyPLM)
>>
>> ab<- ReadAffy()
>> pset<- fitPLM(ab)
>>
>>
>> i obtain an object (pset) of the PLMset class which contains slots
>> 'probe.coefs' and 'se.probe.coefs', where each is a list as many keys as
>> probesets and where each probeset contains information on the probe
>> effect of each probe within the probeset:
>>
>> head(names(pset at probe.coefs))
>> [1] "1000_at"   "1001_at"   "1002_f_at" "1003_s_at" "1004_at"
>> "1005_at"
>> head(names(pset at se.probe.coefs))
>> [1] "1000_at"   "1001_at"   "1002_f_at" "1003_s_at" "1004_at"
>> "1005_at"
>>
>> pset at probe.coefs[[1]]
>>              Overall
>> probe_1   0.97287528
>> probe_2   0.61454806
>> probe_3  -2.81701693
>> probe_4 1.68063395
>> probe_5  -3.31991235
>> probe_6 1.56657388
>> probe_7  -3.30256264
>> probe_8 -1.99431231
>> probe_9 -0.35200585
>> probe_10 -0.49024387
>> probe_11 -1.09087811
>> probe_12 0.22008832
>> probe_13 2.54263342
>> probe_14  3.71106614
>> probe_15  2.12580554
>> probe_16 -0.06729251
>>
>> pset at se.probe.coefs[[1]]
>>             Overall
>> probe_1  0.06124122
>> probe_2  0.06039453
>> probe_3  0.06180433
>> probe_4  0.05948503
>> probe_5 0.06727454
>> probe_6 0.06016827
>> probe_7 0.06233682
>> probe_8 0.06791376
>> probe_9 0.05960599
>> probe_10 0.05963511
>> probe_11 0.05868359
>> probe_12 0.06046023
>> probe_13 0.05885199
>> probe_14 0.05829506
>> probe_15 0.05837877
>> probe_16 0.06340662
>>
>> however, i'm unsure how to proceed from now on to decide whether a
>> particular probeset is more "affected" by probe-specific effects than
>> other probeset. any suggestion would be highly appreciated,
>>
>> thanks,
>> robert.
>>
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