[BioC] liimma and Across Array Normalisation

Wed Feb 12 00:49:48 CET 2014

I wanted it to visualise it in 3x2 style.

On 11 February 2014 23:20, Gordon K Smyth <smyth at wehi.edu.au> wrote:
> Although not documented, you can actually shorten the read command further
> to:
>
>   x <- read.maimages(targets,source="genepix",green.only=TRUE)
>
> The function will automatically look for a column called "FileName" in
> targets.
>
> Gordon
>
>
> On Wed, 12 Feb 2014, Gordon K Smyth wrote:
>
>> On Tue, 11 Feb 2014, Saket Choudhary wrote:
>>
>>> On 11-Feb-2014, at 10:52 PM, Gordon K Smyth <smyth at wehi.edu.au> wrote:
>>>
>>>> On Tue, 11 Feb 2014, Saket Choudhary wrote:
>>>>
>>>>> On 11-Feb-2014, at 10:31 PM, Gordon K Smyth <smyth at wehi.edu.au> wrote:
>>>>>
>>>>>> Yes, obviously there'll be a baseline shift when you subtract
>>>>>> background, then add an offset and log transform.
>>>>>>
>>>>>> You plots do not appear to be a valid MA plots.
>>>>>
>>>>>
>>>>> Could you please point out the error?
>>>>> I understand a base line shoft is expected, but I cant figure out what
>>>>> is going wrong otherwise.
>>>>
>>>>
>>>> Well, you manually create an MAList object from your single channel
>>>> data, even though an MAList is strictly for two colour data.
>>>>
>>>> If you deceive limma as to the true nature of your data, it's not
>>>> surprising that the resulting plot might not be correct.
>>>>
>>>> I am not clear why you need to make so many variations on the standard
>>>> limma single channel analysis pipeline.
>>>>
>>>
>>> Is there any other way to visualise MA plots for single channel data?
>>
>>
>> plotMA() already works directly on any data object:
>>
>>  x <- read.maimages(targets$FileName,source="genepix",green.only=TRUE)
>>  plotMA(x)
>>
>> What could be easier than that?
>>
>> Gordon
>>
>>>
>>>> Gordon
>>>>
>>>>
>>>>>
>>>>> Thanks,
>>>>> Saket
>>>>>
>>>>>
>>>>>> Gordon
>>>>>>
>>>>>> On Tue, 11 Feb 2014, Saket Choudhary wrote:
>>>>>>
>>>>>>> Hello Gordon,
>>>>>>>
>>>>>>> Is there a reason to believe the MA plots should inherently be
>>>>>>> baseline shifted after normalisation?
>>>>>>>
>>>>>>> Raw MA: https://db.tt/kDBod1EJ
>>>>>>> background correction with 'nec': https://db.tt/0vVWeD21
>>>>>>> background correction with nec followed by normalisation:
>>>>>>> https://db.tt/f0M0rWeg
>>>>>>> background correction with 'normexp: https://db.tt/OJO0zea5
>>>>>>> background correction with normexp followed by normalisation:
>>>>>>> https://db.tt/rbLJmFBE
>>>>>>>
>>>>>>>
>>>>>>> The files are a bit heavy so might take some time to load into any
>>>>>>> pdf reader.
>>>>>>>
>>>>>>> Code: https://gist.github.com/saketkc/8931951
>>>>>>>
>>>>>>> Saket
>>>>>>>
>>>>>>> On 9 February 2014 20:45, Saket Choudhary <saketkc at gmail.com> wrote:
>>>>>>>>
>>>>>>>> Related question: Similar to your case, my final topTable()'s output
>>>>>>>> indicates  some genes having a negative logFC, though literature
>>>>>>>> expects them to have a positive logFC.
>>>>>>>>
>>>>>>>> I looked up the calculations and the transition from positive to
>>>>>>>> negative logFC for these genes seems to happen after the
>>>>>>>> normalizeBetweenArrays step (irrespective of the kind of
>>>>>>>> normalisation
>>>>>>>> I choose).
>>>>>>>>
>>>>>>>> This is a naive question again, but I am trying to understand what
>>>>>>>> should be
>>>>>>>> a good metric to decide which method tends to give the least false
>>>>>>>> positives like this, given tham I have limited knowledge of which
>>>>>>>> genes should be up or down regulated(unlike in your case, where you
>>>>>>>> knew the  kind  of regulation[up/down] expected).
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Saket
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> On 9 February 2014 04:00, Gordon K Smyth <smyth at wehi.edu.au> wrote:
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Sat, 8 Feb 2014, Saket Choudhary wrote:
>>>>>>>>>
>>>>>>>>>> Hello Gordon,
>>>>>>>>>>
>>>>>>>>>> I had a chance to go through the paper. I have a set of negative
>>>>>>>>>> and
>>>>>>>>>> positive controls, arising out of single channel Genepix platform.
>>>>>>>>>> From what I could gather, 'nec' method in limma performs
>>>>>>>>>> backgroundcorrection using these negative control spots.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Yes, but the negative controls are assumed to behave exactly like
>>>>>>>>> probes for
>>>>>>>>> unexpressed genes.  This is true for Illumina Beadchips, but is
>>>>>>>>> often not
>>>>>>>>> the case for other platforms.  If not, then you would be better to
>>>>>>>>> stick
>>>>>>>>> with normexp as you are already using.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>> However one of the inputs to 'nec' is also "detection.p", which
>>>>>>>>>> the
>>>>>>>>>> .gprs don't have.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> detection.p is not a required argument.  It is used only when
>>>>>>>>> negative
>>>>>>>>> controls are not available.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>> I could simply take a mean of all the negative controls E and Eb,
>>>>>>>>>> and
>>>>>>>>>> subtract it from each probe's E&Eb, doing it for all the arrays.
>>>>>>>>>> Would
>>>>>>>>>> this mimic what I want to acheive with the 'nec' function?
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> No, that naive approach is not equivalent and typically performs
>>>>>>>>> poorly.
>>>>>>>>>
>>>>>>>>> Gordon
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>> Saket
>>>>>>>>>>
>>>>>>>>>> On 6 February 2014 13:04, Saket Choudhary <saketkc at gmail.com>
>>>>>>>>>> wrote:
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> Hello Gordon,
>>>>>>>>>>>
>>>>>>>>>>> Unfortunately I do not have access to this as of now. I will
>>>>>>>>>>> however
>>>>>>>>>>> get hold of it soon.
>>>>>>>>>>>
>>>>>>>>>>> After implementing this, I would expect the 'CONTROL' to have
>>>>>>>>>>> similar,
>>>>>>>>>>> if not same values, right?
>>>>>>>>>>>
>>>>>>>>>>> However some of the values for these Control genes after the
>>>>>>>>>>> normalisebetweenarray step have high variance. Is this behaviour
>>>>>>>>>>> normal or am I missing something?
>>>>>>>>>>>
>>>>>>>>>>> Saket
>>>>>>>>>>>
>>>>>>>>>>> On 6 February 2014 06:32, Gordon K Smyth <smyth at wehi.edu.au>
>>>>>>>>>>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> If 'x' is your background-corrected EList, then
>>>>>>>>>>>>
>>>>>>>>>>>> w <- rep(1,nrow(x))
>>>>>>>>>>>> w[controls] <- 100
>>>>>>>>>>>> y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w)
>>>>>>>>>>>>
>>>>>>>>>>>> does what you want.
>>>>>>>>>>>>
>>>>>>>>>>>> For an example of this approach:
>>>>>>>>>>>>
>>>>>>>>>>>> http://rnajournal.cshlp.org/content/19/7/876
>>>>>>>>>>>>
>>>>>>>>>>>> Best wishes
>>>>>>>>>>>> Gordon
>>>>>>>>>>>>
>>>>>>>>>>>> --------- original message ----------
>>>>>>>>>>>> Saket Choudhary saketkc at gmail.com
>>>>>>>>>>>> Thu Feb 6 06:59:42 CET 2014
>>>>>>>>>>>>
>>>>>>>>>>>> I am analysing a proteomics microarray data set for a two group
>>>>>>>>>>>> sample(Normal and Disease) using single color channel. The
>>>>>>>>>>>> arrays have a
>>>>>>>>>>>> set
>>>>>>>>>>>> of pre-defined CONTROL points whose expression levels are
>>>>>>>>>>>> supposed to be
>>>>>>>>>>>> similar/same across all the arrays.
>>>>>>>>>>>>
>>>>>>>>>>>> I would like to 'normalise' the levels of all probes such that
>>>>>>>>>>>> normalisation
>>>>>>>>>>>> ends up with all CONTROL points having similar expression
>>>>>>>>>>>> levels. If I
>>>>>>>>>>>> understand it right, normalizebetweenarray does not allow this
>>>>>>>>>>>> kind of
>>>>>>>>>>>> normalisation.
>>>>>>>>>>>>
>>>>>>>>>>>> Is there a pre-implemented function to do this? If not, what
>>>>>>>>>>>> would be a
>>>>>>>>>>>> way
>>>>>>>>>>>> to acheive this kind of normalisation?
>>>>>>>>>>>>
>>>>>>>>>>>> Code: https://gist.github.com/saketkc/8669586
>
>
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