Wolfgang Huber
Group Leader, EBI

Sir,

I am thankful for your timely and Quick reply to my posting.

Yes, after a close examination, I realized that
normalize.AffyBatch.invariantset() methods requires an AffyBatch object as
its input. Yes, I  have already used normalize.invariantset() method for
normalization.

>
The 2 Inputs for the function normalize.invariantset() are:  A vector of
intensities on the chip and A vector of reference intensities.
The Output of the function normalize.invariantset() is a list which has two
components. The first component is again a list and the second component is
a logical vector. And this normalize.invariantset() method does not
normalize the Input vector that is provided. This method
normalize.invariantset() just uses the smooth.spline() function.

I have created a sample vector called s1 and a reference vector called ref
and used them as the inputs for the method normalize.invariantset() as
follows:

> ref<-c(-5.6125,10.21,1385.4,262.95,5363.857,22.1435)
> ref
[1]   -5.6125   10.2100 1385.4000  262.9500 5363.8570   22.1435
> s1<-c(-6.1,10.725,1392.1,264.925,5315.675,21.75)
> s1
[1]   -6.100   10.725 1392.100  264.925 5315.675   21.750
> out<-normalize.invariantset(s1,ref)
> class(out)
[1] "list"
> out[[1]]
Call:
smooth.spline(x = ref[i.set], y = data[i.set])

Smoothing Parameter  spar= 0.8875491  lambda= 0.0001401015 (17 iterations)
Equivalent Degrees of Freedom (Df): 3.386453
Penalized Criterion: 0.6746511
GCV: 0.5926113
> out[[2]]
[1] TRUE TRUE TRUE TRUE TRUE TRUE

> s2<-c(-6.1,10.725,1392.1,264.925,5315.675,271.75)
> out2<-normalize.invariantset(s2,ref)



>From the above test, I realized that normalize.invariantset() method doesnot
perform any normalization to the data(s1) that I have provided.
My task is to normalize a matrix of (RNA expression) intensity values and
not just a vector which represents a single array. And the method
normalize.invariantset() does not even normalize the data values. It just
gives a logical vector like this :

> out[[2]]
[1] TRUE TRUE TRUE TRUE TRUE TRUE

> out2[[2]]
[1]  TRUE  TRUE  TRUE FALSE  TRUE FALSE

I believe that this vector out[[2]] shows that all genes can be considered
as rank invariant genes(i.e. House keeping genes) and in the second vector
out2[[2]], it shows that the only 1st, 2nd , 3rd and 5th genes can be
considered as rank invariant set.

If my protocol is correct, then I need to calculate a *Scaling factor* based
on only the intensity values of the *rank invariant genes set* and then
normalize the intensity vectors.

I am really not sure what method that I need to use to complete my task. I
am summarizing my task and the need to use Rank Invariant Normalization as
follows:

I am in a situation where I need to compare/correlate the expression values
from two experiments which are totally different. In the first experiment,
the RNA expression values are collected by using Affymetrix chips and in the
second experiment the RNA expression values are collected by using Illumina
chips.

The raw data is not provided by the first experiement authors in the GEO
site. The first experiment authors have just provided normalized data(Rank
Invariant Normalization from BeadStudio Software). And the second experiment
authors have provided both raw data and normalized data(Quantile
Normalization followed by Median Normalization).

The interesting thing is that both the authors have collected the expression
values for almost same set of genes , but in a different tissue (cortex and
Lymphoblastoid cell lines)in their experiments.

So, I am trying to normalize the raw data provided by second experiment
authors by using the rank.invariantset()  method  in R. I believe that if I
can normalize(Rank invariant) the raw data(from second experiment) , then I
can compare the expression values because both the datasets have been
normalized using the same metric.

Thank you ,
Suprabhath Reddy

On Sun, Mar 23, 2008 at 3:25 PM, Wolfgang Huber <huber@ebi.ac.uk> wrote:

> Hi Suprabhath
>
> a look at the man page of "normalize.AffyBatch.invariantset" shows that
> this function expects an "AffyBatch" object, while you are giving it an
> exprSet.
>
> Why can't you use the function "normalize.invariantset"?
>
>
> You could inspect the code of the function
> "normalize.AffyBatch.invariantset" (by simply typing its name into the
> command line) to see what it does and how you would interface
> to normalize.invariantset.
>
>  HTH
>  Wolfgang
>
>
> Suprabhath Reddy Gajjala a écrit 23/03/2008 18:58:
> > Hello Everyone,
> >
> > I am writing programs in R from 7 months and I am able to solve most of
> > the errors/issues except for this current post.
> >
> > My Task is to read a Microsoft Excel file(textE_to_affy.csv) which
> > contains the Microarray Expression Values collected from the Illumina
> > Microarray experiment. These collected intensity values need to be
> > normalized(Rank Invariant Normalization) by using the R function
> > "normalize.AffyBatch.invariantset()".
> >
> > Since the normalize.AffyBatch.invariantset() method requires the input
> > argument to be an AffyBatch Object, I used the read.exprSet() method to
> > convert the intensity values present in the (textE_to_affy.csv) file
> > into an AffyBatch Object as follows:
> >
> >> testFile <- tempfile()
> >> textAffy<-read.table("textE_to_affy.csv",header=TRUE,sep=",",
> row.names=1)
> >> textAffy
> >                   Sample1       Sample2       Sample3
> > GI_10047089-S   -6.100   -5.12500   -5.61250
> > GI_10047091-S   10.725    9.70625   10.21562
> > GI_10047093-S 1392.100 1378.70000 1385.40000
> > GI_10047099-S  264.925  260.98125  262.95312
> > GI_10047103-S 5315.675 5412.01875 5363.84688
> > GI_10047105-S   21.750   22.53750   22.14375
> >> write.table(TA,testFile,quote = FALSE, sep = "\t", row.names = TRUE,
> > col.names = TRUE)
> >> testFile
> > [1] "C:\\DOCUME~1\\Jaswanth\\LOCALS~1\\Temp\\RtmpXrUE8T\\file2ea6bb3"
> >> eSet <- read.exprSet(testFile)
> > Warning messages:
> > 1: read.exprSet is deprecated, use readExpresionSet instead
> > 2: read.phenoData is deprecated, use read.AnnotatedDataFrame instead
> > 3: The phenoData class is deprecated, use AnnotatedDataFrame (with
> > ExpressionSet) instead
> > 4: The exprSet class is deprecated, use ExpressionSet instead
> > 5: The exprSet class is deprecated, use ExpressionSet instead
> > 6: The exprSet class is deprecated, use ExpressionSet instead
> > 7: The exprSet class is deprecated, use ExpressionSet instead
> > 8: The exprSet class is deprecated, use ExpressionSet instead
> > 9: The exprSet class is deprecated, use ExpressionSet instead
> >> eSet
> > Expression Set (exprSet) with
> >         6 genes
> >         3 samples
> >         phenoData object with 1 variables and 3 cases
> >         varLabels
> >                 sample: arbitrary numbering
> > Warning messages:
> > 1: The exprSet class is deprecated, use ExpressionSet instead
> > 2: The exprSet class is deprecated, use ExpressionSet instead
> > 3: The phenoData class is deprecated, use AnnotatedDataFrame (with
> > ExpressionSet) instead
> >
> > RInormalizedData<-normalize.AffyBatch.invariantset(eSet,prd.td=c(0.003,
> > 0.007),baseline.type="mean",type="together")
> >
> > Error in function (classes, fdef, mtable)  :
> >   unable to find an inherited method for function "indexProbes", for
> > signature "exprSet", "character"
> >
> > I have observed that this same error message has been posted and
> > answered in a different context in this forum. Even though I am not
> > using the S4 class and method concepts here, I am getting this error.
> >
> > I have also uploaded the input file on which the normalization needed to
> > be performed.
> >
> > Hence, I would be grateful for you if you can give some direction/advice
> > for me to overcome this error.
> >
> > Thanks a lot,
> > Suprabhath Reddy
> >
> >
> > ------------------------------------------------------------------------
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor@stat.math.ethz.ch
> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> > Search the archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor
>
> --
> Best wishes
>  Wolfgang
>
> ------------------------------------------------------------------
> Wolfgang Huber  EBI/EMBL  Cambridge UK  http://www.ebi.ac.uk/huber
>

	[[alternative HTML version deleted]]

