Thank you, that is pretty much as I expected :-D
My only point being that the very nature of an marrayRaw object allows
each individual array to have different weights to every other array,
so it is a little confusing that the maNormMain function applies only
one set of weights to multiple arrays.
Anyway, I have taken enough help from this list, so here is some
contributed code that *should* do what I want:
# raw data should be in an marrayRaw object called "data"
# layout object should be in an marrayLayout object called "layout"
# the marrayRaw object must have a maW slot
# number of arrays
x = 3
#create layout here if none exists
#layout = read.marrayLayout(ngr = 12, ngc = 4, nsr = 14, nsc = 15)
# type of normalisation - 'maPrintTip' will give print-tip Loess, NULL
will
# give Loess
z = "maPrintTip"
# define matrices to hold the weighted data
# 100080 is the number of spots on my array
mam = matrix(nrow = 10080, ncol = x)
maa = matrix(nrow = 10080, ncol = x)
maw = matrix(nrow = 10080, ncol = x)
# create individual weighted
for (i in seq(1,x)) {
temp = maNormMain(data[,i], f.loc = list(maNormLoess(x="maA",
y="maM",
z=z, w=data[,i]@maW)))
mam[,i] = maM(temp)
maa[,i] = maA(temp)
maw[,i] = data[,i]@maW
}
# create a marray Norm object
my.weight.norm = new('marrayNorm',
maA = maa,
maM = mam,
maW = maw,
maLayout = layout,
maNormCall = maNormCall(maNormMain(data, f.loc =
list(maNormLoess(x="maA", y="maM", z="maPrintTip", w=data@maW)))))
-----Original Message-----
From: Marcus Davy [mailto:MDavy@hortresearch.co.nz]
Sent: 19 August 2003 04:16
To: michael.watson@bbsrc.ac.uk; jean@biostat.ucsf.edu;
bioconductor@stat.math.ethz.ch
Subject: RE: [BioC] Defining Weights in marrayNorm.
Hi,
For multiple arrays as far as I can see there is currently no way that
you can make a call to maNormMain that will allow maLoess to utilise
weights from EACH column of your maW matrix.
maNormMain uses a controlling function maNormLoess specified as a LIST
of
calls in the arguement:
f.loc=list(maNormLoess(x="maA",y="maM", z="maPrintTip",
w=Flagweights,
...)
maNormLoess calls maLoess. Inside the code for maLoess the actual
loess
fit
has an arguement weights=args$w, where args$w will be the vector of
Flagweights.
fit <- loess(y ~ x, weights = args$w, subset = args$subset,
span = args$span, na.action = args$na.action, degree =
args$degree,
family = args$family, control = args$control)
What was probably happening in your call
data.weight.norm = maNormMain(data, f.loc = list(maNormLoess(x="maA",
y="maM", z="maPrintTip", w=data@maW)))
was that only the first column of data@maW was being used as weights
for the
the normalization vectors x="maA data for each array",
and y="maM data for each array" in the loess smoother.
e.g.
data(cars)
cars.lo <- loess(dist ~ speed, cars)
plot(cars.lo$x, cars.lo$y)
lines(cars.lo$x,cars.lo$fit)
set.seed(1)
weights <- matrix(rbinom(100,1,0.4),nc=2)
cars.lo <- loess(dist ~ speed, cars, weights=weights)
lines(cars.lo$x,cars.lo$fit, col="red")
cars.lo <- loess(dist ~ speed, cars, weights=weights[1:50,1])
lines(cars.lo$x,cars.lo$fit, lty=8, col="green")
marcus
>>> "michael watson (IAH-C)" <michael.watson@bbsrc.ac.uk> 14/08/2003
2:01:44 AM >>>
Hi
In my continuing search to get this working, I have made progress :-D
But I think I found a bug/feature...
OK here is what I did.
I took a GenePix file. I made a copy of it. I added column
"SpotWeight" to both. In one of the files I set the weights all to 1.
In the other, I set all of the weights to be between 0 and 0.5 (random
numbers). I just wanted to see if I could get it working.
So:
> data = read.GenePix(fnames = files, name.Gf = "F532 Median", name.Gb
= "B532 Median", name.Rf = "F635 Median", name.Rb = "B635 Median",
name.W = "SpotWeight", layout=layout)
> maW(data)
produces a nice lovely vector of my weights, so far so good. By
chance, the first column was the one with all 1's - I think this is
significant.
> data.norm = maNorm(data, norm = "printTipLoess")
This works great and just produces normalised data as if maW didn't
exist - we expect this from the code, maNorm() function does not use
weights.
Now:
> data.weight.norm = maNormMain(data, f.loc =
list(maNormLoess(x="maA",
y="maM", z="maPrintTip", w=data@maW)))
is my big hope. And it doesn't throw any errors :-D. However, it
does
just produce M values as if maW doesn't exist. I am about to throw in
the towel when I think I should try something. So I try:
> data.weight.norm = maNormMain(data[,1], f.loc =
list(maNormLoess(x="maA", y="maM", z="maPrintTip", w=data[,1]@maW)))
This again turns up the now familiar M values, unaffected by maW. But
of course, in my first data set maW is all set to 1, so of course
thats
what it SHOULD produce. So i try:
> data.weight.norm = maNormMain(data[,2], f.loc =
list(maNormLoess(x="maA", y="maM", z="maPrintTip", w=data[,2]@maW)))
and guess what? It works! Hurrah! My M values have been affected by
maW, they are different to normal and I can only assume maNormMain is
calculating weighted normalised M values according to maW.
But wait - isn't this a little incorrect? The marrayRaw class allows
me to have different weights for different spots for all of my arrays.
So why when I normalise using maNormMain() do I have to do it on an
array-by-array basis? Surely:
> data.weight.norm = maNormMain(data, f.loc =
list(maNormLoess(x="maA",
y="maM", z="maPrintTip", w=data@maW)))
should work in that when it is normalising the nth marrayRaw data set
in "data", it should use the nth set of weights in data@maW...?
Instead
what it appears to have done is take the first column of data@maW, and
by chance in my case that was all 1's so I noticed. If those hadn't
been 1's but had been legitimate weights for the first array, I don't
think I would have noticed.... and had all of my arrays normalised
according to weights for the first array.... :-(
Anyway, I believe I have cracked it now in that I can weight normalise
all ninety of my arrays. The fact that i have to make 90 calls to
maNormMain and prodcue 90 normalised data sets is a nuisance rather
than
anything else, though I do believe what i have said above makes sense,
I
hope someone agrees :-) In most other respects the marray* classes,
and
bioconductor in general, are fantastic, so I hope I don't appear
unappreciative ;-)
Thanks
Mick
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