Hi all,
I have a set of 15 Affymetrix chips:
4 treatments, 2 technical replicates of 2 biological replicates for
each
treatment (one chips has been excluded, all the others are really good
quality).
After running rma(), when i try to filter the ExpressionSet
>eset <- rma(mydata)
>eset.f <- nsFilter(eset)$eset
it removes 13047 features for low variance, leaving 171 features in my
dataset.
$numDupsRemoved
[1] 3
$numLowVar
[1] 13047
$feature.exclude
[1] 3
$numRemoved.ENTREZID
[1] 786
It is quite strange, because another analysis (few years ago) on the
same dataset revealed more than 1000 DE genes.
Now, I can just set a less stringent cutoff, but is it reasonable to
go
on with the analysis with 171 features? Is it realistic to get these
results with the default parameters of nsFilter? Obviously, it depends
by what I am expecting and by the experimental design... well, i was
expecting some more dramatic changes in expression. At the end of the
analysis, I end up with ~60 differentially expressed probesets (lfc=1,
p.value=0.05, adjustment method=BH)
Second question: Is it informative to test for gene sets (GSEA) on 171
genes, or would be better not to filter the expressionset?
Thanks,
Paolo
> sessionInfo()
R version 2.6.1 (2007-11-26)
i486-pc-linux-gnu
locale:
LC_CTYPE=en_US.UTF-8;LC_NUMERIC=C;LC_TIME=en_US.UTF-8;LC_COLLATE=en_US
.UTF-8;LC_MONETARY=en_US.UTF-8;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_US.
UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_US.UTF-8
;LC_IDENTIFICATION=C
attached base packages:
[1] splines tools stats graphics grDevices utils
datasets
[8] methods base
other attached packages:
[1] annotate_1.16.1 xtable_1.5-2 AnnotationDbi_1.0.6
[4] RSQLite_0.6-4 DBI_0.2-4 statmod_1.3.1
[7] limma_2.12.0 drosgenome1_2.0.1 genefilter_1.16.0
[10] survival_2.34 Biobase_1.16.1
loaded via a namespace (and not attached):
[1] rcompgen_0.1-17
Hi,
thanks for the prompt reply. I am a bit confused now.
Sean Davis wrote:
> Sounds like you probably need to take a closer look at the data. I
have
> not used the nsFilter function, but it looks like the default
variance
> function is IQR (interquartile range) and the default cutoff is 0.5
for
> that function value is 0.5. If nearly 99% of your probes have an
> IQR<0.5, I would look at the data quality closely to see if there
are
> data quality issues or preprocessing steps that do not make sense
(can't
> tell what was done before RMA).
Boxplot, hist, RLE, NUSE, MAplot, RNAdeg: they all look fine (except
for
one chip that is *a bit* strange, but that should just increase the
variance (?) . Can you suggest me other tests (and how to interpret
them)?
And there is no preprocessing except for RMA (maybe is this the wrong
step?):
miame <- read.MIAME("miame")
phenodata<- read.AnnotatedDataFrame("phenodata",sep=" ")
mydata <- ReadAffy(sampleNames=sampleNames(phenodata),
phenoData=phenodata,
description=miame)
eset <- rma(mydata)
eset.f <- nsFilter(eset)$eset
What if the problem is that the data are TOO good? Makes sense to
guess
that, if data mirror exactly the biology of the sample, I am expecting
heaps of genes with the exactly the same expression level, and "a few"
genes with differential expression? (the experimental design was
virginVSmated female flies: mating is expected to promote some change
in
female physiology, probably affecting more that 60 genes, though).
Cheers,
Paolo
Hi Paolo,
Have you try plotting the IQR or the variance of the signal intensity
for all probesets to see what the distribution looks like? Normally
you
should see two clear groups - one peak with small IQR value over a
narrow range, and the other with peak at greater IQR and more spread
out. Do you really have most of your probesets not varying much across
samples?
It doesn't seem right that so many genes are removed. But I haven't
used
the function nsFilter before.
Alex
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Roslin Institute (Edinburgh)
Midlothian
EH25 9PS
United Kingdom
Tel: +44 131 5274471
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-----Original Message-----
From: bioconductor-bounces@stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Paolo
Innocenti
Sent: 11 January 2008 15:41
To: ML Bioconductor
Subject: Re: [BioC] nsFilter and GSEA
Hi,
thanks for the prompt reply. I am a bit confused now.
Sean Davis wrote:
> Sounds like you probably need to take a closer look at the data. I
> have not used the nsFilter function, but it looks like the default
> variance function is IQR (interquartile range) and the default
cutoff
> is 0.5 for that function value is 0.5. If nearly 99% of your probes
> have an IQR<0.5, I would look at the data quality closely to see if
> there are data quality issues or preprocessing steps that do not
make
> sense (can't tell what was done before RMA).
Boxplot, hist, RLE, NUSE, MAplot, RNAdeg: they all look fine (except
for
one chip that is *a bit* strange, but that should just increase the
variance (?) . Can you suggest me other tests (and how to interpret
them)?
And there is no preprocessing except for RMA (maybe is this the wrong
step?):
miame <- read.MIAME("miame")
phenodata<- read.AnnotatedDataFrame("phenodata",sep=" ") mydata <-
ReadAffy(sampleNames=sampleNames(phenodata),
phenoData=phenodata,
description=miame)
eset <- rma(mydata)
eset.f <- nsFilter(eset)$eset
What if the problem is that the data are TOO good? Makes sense to
guess
that, if data mirror exactly the biology of the sample, I am expecting
heaps of genes with the exactly the same expression level, and "a few"
genes with differential expression? (the experimental design was
virginVSmated female flies: mating is expected to promote some change
in
female physiology, probably affecting more that 60 genes, though).
Cheers,
Paolo
_______________________________________________
Bioconductor mailing list
Bioconductor at stat.math.ethz.ch
https://stat.ethz.ch/mailman/listinfo/bioconductor
Search the archives:
http://news.gmane.org/gmane.science.biology.informatics.conductor
Hi again,
I tried with a different normalisation method, and I was pretty
surprised by the results:
> eset.mas <- mas5(mydata)
background correction: mas
PM/MM correction : mas
expression values: mas
background correcting...done.
14010 ids to be processed
| |
|####################|
> eset.mas.f <- nsFilter(eset.mas)
> eset.mas.f$filter.log
$numDupsRemoved
[1] 1098
$numLowVar
[1] 1
$feature.exclude
[1] 3
$numRemoved.ENTREZID
[1] 786
> eset.rma <- rma(mydata)
Background correcting
Normalizing
Calculating Expression
> eset.rma.f <- nsFilter(eset.rma)
> eset.rma.f$filter.log
$numDupsRemoved
[1] 3
$numLowVar
[1] 13047
$feature.exclude
[1] 3
$numRemoved.ENTREZID
[1] 786
> dim(eset.rma.f$eset)
Features Samples
171 15
> dim(eset.mas.f$eset)
Features Samples
12122 15
I don't understand how is it possible. Any suggestion about what to
do?
Should I lower the cutoff for the rma, or that processing method
doesn't
work for my dataset?
Paolo
PS: I tried also a really low cutoff, but the situation doesn't
change,
unless I choose a cutoff=0.1:
> eset.filter <- nsFilter(eset,var.cutoff=0.2)
> eset.filter$filter.log
$numDupsRemoved
[1] 69
$numLowVar
[1] 10560
$feature.exclude
[1] 3
$numRemoved.ENTREZID
[1] 786
Hi,
It looks like something fairly odd is going on, and that we are not
seeing all of the code that is being run.
What chip are you using? What is very odd is that in your first
example 1098 "duplicate" probes are found, but in the second run only
3.
Basically this cannot happen (since the probes are the same) and
suggests that some piece of code has manipulated the names, and at
that
point I think fairly bad things are going to happen. So this would be
one place to try and fix things.
Second, nsFilter filters by default at the median, so you should
retain about 0.5 of your probe sets. But since you loose so many (you
didn't tell us the chip so I can't be sure) but it looks like all of
the
values are corrupt for that example as well.
So, I think that you are looking in the wrong place. Your problem is
probably earlier on.
best wishes
Robert
Paolo Innocenti wrote:
> Hi again,
>
> I tried with a different normalisation method, and I was pretty
> surprised by the results:
>
> > eset.mas <- mas5(mydata)
> background correction: mas
> PM/MM correction : mas
> expression values: mas
> background correcting...done.
> 14010 ids to be processed
> | |
> |####################|
> > eset.mas.f <- nsFilter(eset.mas)
> > eset.mas.f$filter.log
> $numDupsRemoved
> [1] 1098
>
> $numLowVar
> [1] 1
>
> $feature.exclude
> [1] 3
>
> $numRemoved.ENTREZID
> [1] 786
>
> > eset.rma <- rma(mydata)
> Background correcting
> Normalizing
> Calculating Expression
> > eset.rma.f <- nsFilter(eset.rma)
> > eset.rma.f$filter.log
> $numDupsRemoved
> [1] 3
>
> $numLowVar
> [1] 13047
>
> $feature.exclude
> [1] 3
>
> $numRemoved.ENTREZID
> [1] 786
>
> > dim(eset.rma.f$eset)
> Features Samples
> 171 15
> > dim(eset.mas.f$eset)
> Features Samples
> 12122 15
>
> I don't understand how is it possible. Any suggestion about what to
do?
> Should I lower the cutoff for the rma, or that processing method
doesn't
> work for my dataset?
>
> Paolo
> PS: I tried also a really low cutoff, but the situation doesn't
change,
> unless I choose a cutoff=0.1:
>
> > eset.filter <- nsFilter(eset,var.cutoff=0.2)
> > eset.filter$filter.log
> $numDupsRemoved
> [1] 69
>
> $numLowVar
> [1] 10560
>
> $feature.exclude
> [1] 3
>
> $numRemoved.ENTREZID
> [1] 786
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives:
http://news.gmane.org/gmane.science.biology.informatics.conductor
>
--
Robert Gentleman, PhD
Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N, M2-B876
PO Box 19024
Seattle, Washington 98109-1024
206-667-7700
rgentlem at fhcrc.org
Dear Robert and BioC Mailing list,
The chips are Affymetrix Drosophila genome 1.0 (annotation
drosgenome1).
I am even more confused: to make sure that was not my fault, I copied
the .CEL files in a new directory, started a fresh R session from
there
and run *just* the following code. Same results:
> library(affy)
Loading required package: Biobase
Loading required package: tools
Welcome to Bioconductor
Vignettes contain introductory material. To view, type
'openVignette()'. To cite Bioconductor, see
'citation("Biobase")' and for packages 'citation(pkgname)'.
Loading required package: affyio
Loading required package: preprocessCore
> mydata <- ReadAffy()
> eset.rma <- rma(mydata)
Background correcting
Normalizing
Calculating Expression
> eset.mas <- mas5(mydata)
background correction: mas
PM/MM correction : mas
expression values: mas
background correcting...done.
14010 ids to be processed
| |
|####################|
> library(genefilter)
Loading required package: survival
Loading required package: splines
> eset.rma.f <- nsFilter(eset.rma)
> eset.mas.f <- nsFilter(eset.mas)
> eset.rma.f
$eset
ExpressionSet (storageMode: lockedEnvironment)
assayData: 171 features, 15 samples
element names: exprs
phenoData
sampleNames: dta_2a.CEL, dta_2b.CEL, ..., virgin_4b.CEL (15 total)
varLabels and varMetadata description:
sample: arbitrary numbering
featureData
featureNames: 147260_at, 142359_at, ..., 145988_at (171 total)
fvarLabels and fvarMetadata description: none
experimentData: use 'experimentData(object)'
Annotation: drosgenome1
$filter.log
$filter.log$numDupsRemoved
[1] 3
$filter.log$numLowVar
[1] 13047
$filter.log$feature.exclude
[1] 3
$filter.log$numRemoved.ENTREZID
[1] 786
> eset.mas.f
$eset
ExpressionSet (storageMode: lockedEnvironment)
assayData: 12122 features, 15 samples
element names: exprs, se.exprs
phenoData
sampleNames: dta_2a.CEL, dta_2b.CEL, ..., virgin_4b.CEL (15 total)
varLabels and varMetadata description:
sample: arbitrary numbering
featureData
featureNames: 153135_at, 154994_at, ..., 152360_at (12122 total)
fvarLabels and fvarMetadata description: none
experimentData: use 'experimentData(object)'
Annotation: drosgenome1
$filter.log
$filter.log$numDupsRemoved
[1] 1098
$filter.log$numLowVar
[1] 1
$filter.log$feature.exclude
[1] 3
$filter.log$numRemoved.ENTREZID
[1] 786
> sessionInfo()
R version 2.6.1 (2007-11-26)
i486-pc-linux-gnu
locale:
LC_CTYPE=en_US.UTF-8;LC_NUMERIC=C;LC_TIME=en_US.UTF-8;LC_COLLATE=en_US
.UTF-8;LC_MONETARY=en_US.UTF-8;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_US.
UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_US.UTF-8
;LC_IDENTIFICATION=C
attached base packages:
[1] splines tools stats graphics grDevices utils
datasets
[8] methods base
other attached packages:
[1] drosgenome1_2.0.1 genefilter_1.16.0 survival_2.34
[4] drosgenome1cdf_2.0.0 affy_1.16.0 preprocessCore_1.0.0
[7] affyio_1.6.1 Biobase_1.16.1
loaded via a namespace (and not attached):
[1] annotate_1.16.1 AnnotationDbi_1.0.6 DBI_0.2-4
[4] rcompgen_0.1-17 RSQLite_0.6-4
>
Could be the CEL files that are damaged?
Thanks,
best wishes,
Paolo
Robert Gentleman wrote:
> Hi,
> It looks like something fairly odd is going on, and that we are not
> seeing all of the code that is being run.
>
> What chip are you using? What is very odd is that in your first
> example 1098 "duplicate" probes are found, but in the second run
only 3.
> Basically this cannot happen (since the probes are the same) and
> suggests that some piece of code has manipulated the names, and at
that
> point I think fairly bad things are going to happen. So this would
be
> one place to try and fix things.
>
> Second, nsFilter filters by default at the median, so you should
retain
> about 0.5 of your probe sets. But since you loose so many (you
didn't
> tell us the chip so I can't be sure) but it looks like all of the
values
> are corrupt for that example as well.
>
> So, I think that you are looking in the wrong place. Your problem
is
> probably earlier on.
>
> best wishes
> Robert
>
>
> Paolo Innocenti wrote:
>> Hi again,
>>
>> I tried with a different normalisation method, and I was pretty
>> surprised by the results:
>>
>> > eset.mas <- mas5(mydata)
>> background correction: mas
>> PM/MM correction : mas
>> expression values: mas
>> background correcting...done.
>> 14010 ids to be processed
>> | |
>> |####################|
>> > eset.mas.f <- nsFilter(eset.mas)
>> > eset.mas.f$filter.log
>> $numDupsRemoved
>> [1] 1098
>>
>> $numLowVar
>> [1] 1
>>
>> $feature.exclude
>> [1] 3
>>
>> $numRemoved.ENTREZID
>> [1] 786
>>
>> > eset.rma <- rma(mydata)
>> Background correcting
>> Normalizing
>> Calculating Expression
>> > eset.rma.f <- nsFilter(eset.rma)
>> > eset.rma.f$filter.log
>> $numDupsRemoved
>> [1] 3
>>
>> $numLowVar
>> [1] 13047
>>
>> $feature.exclude
>> [1] 3
>>
>> $numRemoved.ENTREZID
>> [1] 786
>>
>> > dim(eset.rma.f$eset)
>> Features Samples
>> 171 15
>> > dim(eset.mas.f$eset)
>> Features Samples
>> 12122 15
>>
>> I don't understand how is it possible. Any suggestion about what to
>> do? Should I lower the cutoff for the rma, or that processing
method
>> doesn't work for my dataset?
>>
>> Paolo
>> PS: I tried also a really low cutoff, but the situation doesn't
>> change, unless I choose a cutoff=0.1:
>>
>> > eset.filter <- nsFilter(eset,var.cutoff=0.2)
>> > eset.filter$filter.log
>> $numDupsRemoved
>> [1] 69
>>
>> $numLowVar
>> [1] 10560
>>
>> $feature.exclude
>> [1] 3
>>
>> $numRemoved.ENTREZID
>> [1] 786
>>
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor at stat.math.ethz.ch
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>> Search the archives:
>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>>
>
Hi Paolo,
Thanks for doing that, and an apology as I misremembered having
ported a bug fix back to the release branch. This is a bug(let) that
is
fixed in the devel version, and that I will shortly port back to
release.
The problem is that the 0.5 default in the call to nsFilter was
meant
to be interpreted as a quantile, not as a value, but the in the
implementation it was implemented as a value.
So, my recommendation is to do something like
rmaIQRs = apply(exprs(eset.mas), 1, IQR)
med1 = median(rmaIQRs)
nsFilter(eset.mas, var.cutoff = med1)
and then something similar for the rma version, and then things
should
line up somewhat better.
let us know if that is not clear, or if anything else comes up
Robert
Paolo Innocenti wrote:
> Dear Robert and BioC Mailing list,
>
> The chips are Affymetrix Drosophila genome 1.0 (annotation
drosgenome1).
> I am even more confused: to make sure that was not my fault, I
copied
> the .CEL files in a new directory, started a fresh R session from
there
> and run *just* the following code. Same results:
>
> > library(affy)
> Loading required package: Biobase
> Loading required package: tools
>
> Welcome to Bioconductor
>
> Vignettes contain introductory material. To view, type
> 'openVignette()'. To cite Bioconductor, see
> 'citation("Biobase")' and for packages 'citation(pkgname)'.
>
> Loading required package: affyio
> Loading required package: preprocessCore
> > mydata <- ReadAffy()
> > eset.rma <- rma(mydata)
> Background correcting
> Normalizing
> Calculating Expression
> > eset.mas <- mas5(mydata)
> background correction: mas
> PM/MM correction : mas
> expression values: mas
> background correcting...done.
> 14010 ids to be processed
> | |
> |####################|
> > library(genefilter)
> Loading required package: survival
> Loading required package: splines
> > eset.rma.f <- nsFilter(eset.rma)
> > eset.mas.f <- nsFilter(eset.mas)
> > eset.rma.f
> $eset
> ExpressionSet (storageMode: lockedEnvironment)
> assayData: 171 features, 15 samples
> element names: exprs
> phenoData
> sampleNames: dta_2a.CEL, dta_2b.CEL, ..., virgin_4b.CEL (15
total)
> varLabels and varMetadata description:
> sample: arbitrary numbering
> featureData
> featureNames: 147260_at, 142359_at, ..., 145988_at (171 total)
> fvarLabels and fvarMetadata description: none
> experimentData: use 'experimentData(object)'
> Annotation: drosgenome1
>
> $filter.log
> $filter.log$numDupsRemoved
> [1] 3
>
> $filter.log$numLowVar
> [1] 13047
>
> $filter.log$feature.exclude
> [1] 3
>
> $filter.log$numRemoved.ENTREZID
> [1] 786
>
>
> > eset.mas.f
> $eset
> ExpressionSet (storageMode: lockedEnvironment)
> assayData: 12122 features, 15 samples
> element names: exprs, se.exprs
> phenoData
> sampleNames: dta_2a.CEL, dta_2b.CEL, ..., virgin_4b.CEL (15
total)
> varLabels and varMetadata description:
> sample: arbitrary numbering
> featureData
> featureNames: 153135_at, 154994_at, ..., 152360_at (12122 total)
> fvarLabels and fvarMetadata description: none
> experimentData: use 'experimentData(object)'
> Annotation: drosgenome1
>
> $filter.log
> $filter.log$numDupsRemoved
> [1] 1098
>
> $filter.log$numLowVar
> [1] 1
>
> $filter.log$feature.exclude
> [1] 3
>
> $filter.log$numRemoved.ENTREZID
> [1] 786
>
>
> > sessionInfo()
> R version 2.6.1 (2007-11-26)
> i486-pc-linux-gnu
>
> locale:
> LC_CTYPE=en_US.UTF-8;LC_NUMERIC=C;LC_TIME=en_US.UTF-8;LC_COLLATE=en_
US.UTF-8;LC_MONETARY=en_US.UTF-8;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_U
S.UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_US.UTF
-8;LC_IDENTIFICATION=C
>
> attached base packages:
> [1] splines tools stats graphics grDevices utils
datasets
> [8] methods base
>
> other attached packages:
> [1] drosgenome1_2.0.1 genefilter_1.16.0 survival_2.34
> [4] drosgenome1cdf_2.0.0 affy_1.16.0 preprocessCore_1.0.0
> [7] affyio_1.6.1 Biobase_1.16.1
>
> loaded via a namespace (and not attached):
> [1] annotate_1.16.1 AnnotationDbi_1.0.6 DBI_0.2-4
> [4] rcompgen_0.1-17 RSQLite_0.6-4
> >
>
>
> Could be the CEL files that are damaged?
> Thanks,
> best wishes,
> Paolo
>
>
>
> Robert Gentleman wrote:
>> Hi,
>> It looks like something fairly odd is going on, and that we are
not
>> seeing all of the code that is being run.
>>
>> What chip are you using? What is very odd is that in your first
>> example 1098 "duplicate" probes are found, but in the second run
only 3.
>> Basically this cannot happen (since the probes are the same) and
>> suggests that some piece of code has manipulated the names, and at
that
>> point I think fairly bad things are going to happen. So this would
be
>> one place to try and fix things.
>>
>> Second, nsFilter filters by default at the median, so you should
retain
>> about 0.5 of your probe sets. But since you loose so many (you
didn't
>> tell us the chip so I can't be sure) but it looks like all of the
values
>> are corrupt for that example as well.
>>
>> So, I think that you are looking in the wrong place. Your problem
is
>> probably earlier on.
>>
>> best wishes
>> Robert
>>
>>
>> Paolo Innocenti wrote:
>>> Hi again,
>>>
>>> I tried with a different normalisation method, and I was pretty
>>> surprised by the results:
>>>
>>> > eset.mas <- mas5(mydata)
>>> background correction: mas
>>> PM/MM correction : mas
>>> expression values: mas
>>> background correcting...done.
>>> 14010 ids to be processed
>>> | |
>>> |####################|
>>> > eset.mas.f <- nsFilter(eset.mas)
>>> > eset.mas.f$filter.log
>>> $numDupsRemoved
>>> [1] 1098
>>>
>>> $numLowVar
>>> [1] 1
>>>
>>> $feature.exclude
>>> [1] 3
>>>
>>> $numRemoved.ENTREZID
>>> [1] 786
>>>
>>> > eset.rma <- rma(mydata)
>>> Background correcting
>>> Normalizing
>>> Calculating Expression
>>> > eset.rma.f <- nsFilter(eset.rma)
>>> > eset.rma.f$filter.log
>>> $numDupsRemoved
>>> [1] 3
>>>
>>> $numLowVar
>>> [1] 13047
>>>
>>> $feature.exclude
>>> [1] 3
>>>
>>> $numRemoved.ENTREZID
>>> [1] 786
>>>
>>> > dim(eset.rma.f$eset)
>>> Features Samples
>>> 171 15
>>> > dim(eset.mas.f$eset)
>>> Features Samples
>>> 12122 15
>>>
>>> I don't understand how is it possible. Any suggestion about what
to
>>> do? Should I lower the cutoff for the rma, or that processing
method
>>> doesn't work for my dataset?
>>>
>>> Paolo
>>> PS: I tried also a really low cutoff, but the situation doesn't
>>> change, unless I choose a cutoff=0.1:
>>>
>>> > eset.filter <- nsFilter(eset,var.cutoff=0.2)
>>> > eset.filter$filter.log
>>> $numDupsRemoved
>>> [1] 69
>>>
>>> $numLowVar
>>> [1] 10560
>>>
>>> $feature.exclude
>>> [1] 3
>>>
>>> $numRemoved.ENTREZID
>>> [1] 786
>>>
>>> _______________________________________________
>>> Bioconductor mailing list
>>> Bioconductor at stat.math.ethz.ch
>>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>>> Search the archives:
>>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>>>
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives:
http://news.gmane.org/gmane.science.biology.informatics.conductor
>
--
Robert Gentleman, PhD
Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N, M2-B876
PO Box 19024
Seattle, Washington 98109-1024
206-667-7700
rgentlem at fhcrc.org
