Hi guys,

here are some late feedback on this discussion:

* When talking about copy numbers, it is important to always be very
clear and distinguish between whether we talk about normal/germline
CNs or tumor CNs.  The former take integer CN levels (0, 1, 2, 3,
...), whereas for tumors we very rarely observe pure homogeneous tumor
cells, which is why we only measure and observe non-integer CN levels.
Hopefully, we observe at least discrete CN levels in tumors, but one
should never expect integer levels.

* aCGH: a historical term often used as a synonym for total copy
numbers.  For example, some say "aCGH analysis" when they really mean
"total copy-number analysis".  aCGH stands for array-CGH, or in full
'array comparative genomic hybridization'.  This refers to the older
generation two-color/two-channel arrays where a test and a reference
sample where labelled with two different dyes and "competitively"
hybridized to the same array and the same probes.  I recommend to stop
using this term and instead use "total copy number", total CN, or
"TCN" (when it's clear).   By being explicit about "total", you're
also explicitly contrasting it to "parent-specific" CNs (which you can
do if you have SNP data).

* CNA: Copy-Number Aberration.  This term can be applied to both tumor
and germline samples.  In tumors you expect non-integer CN levels.  In
germline/normals you expect integer CN levels (0, 1, 2, 3, ...).

* CNP: Copy-Number Polymorphism.  This term applies to copy-number
differences in relationship to a population.  This also implies we're
talking about germline genomes.  In other words, CNPs are also integer
CN levels (0, 1, 2, 3, ...).  CNPs are used to specify, say, "2% of
the Europeans have a 1 copy deletion of length 1.0-1.5 Mb on Chr 3 at
124.5Mb".  CNPs is for segment deletions and gains what SNPs are for
nucleotide polymorphisms.  The term CNP is rare.  It is much more
common to hear/see "CNV".

* CNV: Copy-Number Variation.  Ideally the word "variation" refers to
"polymorphism" and therefore the term CNV should be used only to refer
to CNPs.  I don't know if there is a formal definitions, but I find it
unfortunate to see CNV being used when CNA should be used.  By my
books, CNV only takes integer CN levels (0, 1, 2, 3, ...).  The term
CNV should never be used to refer to CN levels in tumors.

* Calling total CN levels is very hard in tumors, and as the first
above point alludes to, it may not even be a well defined problem.
For instance, imagine you have a tumor sample with 5% tumor cells and
95% normal cells, and that the those tumors cells all have a deletion
on Chr 2.  Then, at what point to you consider that sample itself to
have a deletion on Chr 2?  Are you after he sample/tissue itself, or
are you after those 5% tumors cells?  What if you have a heterogeneous
mix of tumor cells?  The more precise you can specify your question
the more easy it is for you to decided what approach forward (may)
work and what doesn't work.  Here "work" can also be read as "make
sense".

* The first and most important task for almost all segmentation
methods is to *segment* the genome, that is, identify at what genomic
locations the observed DNA (tumor, normal or a mix) changes in CN
level.  Together, these location, aka "change points", defines how the
genome can be "partitioned" into segments with equal CN levels, such
that when we look at a particular segment, we can assume that all
genomic locations within that segment has the same underlying genomic
composition (e.g. gain, loss, loss in 5% of the cells, etc.).  CBS,
GLAD, and many other methods, segment the genome this way as a first
step.

* A common task after having decided on the segments (partitioning of
the genome), is to decide on what is going on within each segment.
Not all methods does this.  For instance, CBS "only" provides you with
the change points.  GLAD on the other hand does both the segmentation
and then also provides a method for calling.  Theoretically, there is
nothing preventing you from using the GLAD *calling* algorithm using
the segmentation found by CBS.  Unfortunately, I don't think it is
straightforward to do that in practice; at least you have to coerce
one data format into one that GLAD understands.

* GLAD does not scale well with the number of loci, because it's
computational complexity is ~O(n^2), unless things have changed since.
In 2007, I tried to predict GLAD's processing time when we were using
the Affymetrix 500K chips and the GenomeWideSNP_5 and GenomeWideSNP_6
were starting to come out.  A GWS6 chip would basically take days to
segment.  See attached PNG for a table.

* CBS is much faster as an algorithm.  Also, the implementation in the
DNAcopy package has been made even faster over time.  There was a
major speedup back in 2009, cf.
http://aroma-project.org/benchmarks/DNAcopy_v1.19.2-speedup/

Over and for now

Henrik

On Thu, Jan 22, 2015 at 12:42 AM, Chengyu Liu <chengyu.liu...@gmail.com> wrote:
> Hi,
>
>>
>> I have tried this and works good but at the end I need the information
>> whether there is a gain or loss at the segment. I will use GLAD model to get
>> gain or loss at a segment. My samples and controls are completely unrelated
>> so I am little bit doubtful whether I am doing right or not. I also found
>> some other algorithms that can work on segments produced by CBS model still
>> looking into them.
>>>
>>>
> I think you can use GLAD to call gain and loss. But CBS does not return gain
> or loss, only segments. If you use CBS you should call gain or loss yourself
> (or use other tools such as GISTIC).
>
>>
>> Then I am also looking for CNA. What other softwares have you tried on
>> data from CytoScan HD array?
>
> Like you I used aroma to preprocess, segmented using CBS and manually call
> gain or loss. The simplest way is using a threshold to define gain or loss.
> If I remember correctly, one of TCGA papers in Nature, there a fixed
> threshold was used to define gain and loss. Maybe you can check that.
>
> Br,
>
>>>
>>>>
>>>>
>>>>
>>>>>
>>>>> Br,
>>>>> C.Y
>>>>>
>>>>>
>>>>>>
>>>>>>
>>>>>> Thanks,
>>>>>>
>>>>>> Best Regards,
>>>>>> Sam.
>>>>>>
>>>>>> On Tuesday, January 20, 2015 at 10:38:27 AM UTC+1, Chengyu Liu wrote:
>>>>>>>
>>>>>>> Hi,
>>>>>>>
>>>>>>> On Monday, January 19, 2015 at 3:42:59 PM UTC+2, Sam Padmanabhuni
>>>>>>> wrote:
>>>>>>>>
>>>>>>>> Dear AromaAffymetrix Team,
>>>>>>>>
>>>>>>>> First of all, thank you very much for such a detailed vignette on
>>>>>>>> how to perform the CNV analysis.
>>>>>>>>
>>>>>>>> I am Sam, a PhD student in genetics, working on CNV analysis on data
>>>>>>>> from CytoScan HD Array. I have read the vignette to do CRMAv2 and 
>>>>>>>> non-paired
>>>>>>>> CBS. I have copied the commands and ran in R.
>>>>>>>>
>>>>>>>> But, I have few questions regarding CbsModel and GladModel in
>>>>>>>> segmentation algorithm:
>>>>>>>>
>>>>>>>> 1. It is mentioned that, copy number states is not calculated in
>>>>>>>> CbsModel segmentation. How do I get information of whether the segment 
>>>>>>>> is a
>>>>>>>> loss or gain from output of CbsModel? I mean can this information be 
>>>>>>>> passed
>>>>>>>> to other algorithms to estimate copy number state.
>>>>>>>
>>>>>>> As far as I know, the out put of CBS is the relative copy number.  It
>>>>>>> does not directly tell you the copy number states.
>>>>>>>>
>>>>>>>>
>>>>>>>> 2. I have looked in to GLAD model and it is mentioned that it is
>>>>>>>> developed for aCGH but my data is not from aCGH. Can it be still used 
>>>>>>>> to
>>>>>>>> calculate copy number states for the data I am working on?
>>>>>>>
>>>>>>> GLAD can calculate copy number states for affy-array, although I have
>>>>>>> not used it before.
>>>>>>>>
>>>>>>>>
>>>>>>>> 3. Also, do you have a vignette on how to run CRMAv2 and CBS on
>>>>>>>> CytoScan HD array? This would be really helpful.
>>>>>>>
>>>>>>> It is the same with other chiptype, prepare input as required (there
>>>>>>> is vignette).
>>>>>>>
>>>>>>>
>>>>>>> BTW, I am also working on CytoScan HD. What kind of analysis are you
>>>>>>> going to do? Do you have paired samples or non-paired? Maybe we have
>>>>>>> something common and we can discuss.
>>>>>>>
>>>>>>> Br,
>>>>>>> C.Y
>>>>>>>
>>>>>>>
>>>>>>>>
>>>>>>>> Thank you,
>>>>>>>>
>>>>>>>> Best,
>>>>>>>> Sam.
>>>>>>>>
>>>>>>>>
>>>>
>>>> Best Regards,
>>>> Sam.
>>
>>
>>
>> Best,
>> Sam.
>
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