Help with the CNVkit analysis
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4.6 years ago

Hi All,

I am confused with the results of CNVkit tool. I am using a flat_reference.cnn against my test sample that has ploidy 4. I am expecting cn = 4 for most of the segments in the .cns file. However, I am getting cn = 2 for most of the segments. Please if someone can explain, why this is the case. Or do I need to change any parameter if the ploidy of the test sample is different from 2? Your quick reply will be much appreciated.

Many thanks, Rav

CNVkit • 2.3k views
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Ploidy of 2x is assumed by default, so yes, Ploidy of 4 has to be specified or you can divide your final results by corresponding factor.

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Dear German,

Many thanks for your reply. Do you mean, I should divide the cn values by 4?

Kind regards,

Rav

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Hi,

I think you need to find difference from 4 and then divide by 2 and then plus 2 =)

E.g. CN=6 with ploidy 4

(6 - 4) / 2 + 2 = 3 with ploidy 2. That's what is expected.

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Hi German,

Many thanks for your answer.

So my understanding is as below :

if cn=6 while using a flat_reference.cnn and a test sample with ploidy = 4, then

6 - 4 = 2 , this would give you the gene gain or loss for a particular segment of a tetraploid sample.

(6-4)/2, this would give gene gain or loss per diploid genome

then why are we adding 2 in the end?

And is my understanding correct for all the steps?

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Sure, the understanding is correct! In the beginning we substracted a baseline - 4 copies. So in the end we have to add baseline - 2, as we assume that the organism is diploid. You may skip adding 2 step - but then you'll have a relatvie change and e.g. for X/Y chromosomes it may be crucial.

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I understand now. Many thanks for all the clarification.

As you mentioned earlier that ploidy of 4 can be specified in the pipeline also. Where can I specify the ploidy in the pipeline so that I do not need to do the calculations in the end?

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Sadly the option I was thinking about is for different reasons. You need to perform calculations yourself, ie.

For homogeneous samples of known ploidy, you can calculate cutoffs from scatch by log-transforming the integer copy number values of interest, plus .5 (for rounding), divided by the ploidy.

https://cnvkit.readthedocs.io/en/stable/pipeline.html

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Thank you German for all the above clarifications. Do you think it is appropriate to calculate CNV using a tumour cell line with ploidy=4 against a normal cell line with ploidy=2? If yes, then how can we interpret the copy number from the log2 value?

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Basically you have no other choice - you don't have a normal with ploidy of 4 =) copy-numbers have to be corrected for ploidy with the above mentioned procedure - change the baseline from 2 to 4, then calculate differences

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Changing the baseline fro 2 to 4 has worked. Thank you so much for all your time and help.

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Hi German,

While using a tumour cell line with ploidy=4 against a normal cell line with ploidy=2, if the log2 value of a segment is -0.37681, should I interpret it's copy number as 0.77 or 0.38?

Because log2 = -0.37681 and its antilog would be 0.77.

How should I interpret it's copy number? Sorry, I am bit confused.

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hi, log2(CN_tumor / CN_normal) = -0.37681. By default CN_normal = 2. CN_tumor then is equal to 1.549604. And then you need to apply that trick as was discussed before - 4 + (1.55 - 2) * 2 = 3.1. Of course I do not know how CNV-Kit does ploidy correction and these calculations are correct only if CN_tumor is assumed to be 2 by default.

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Many thanks for your reply.

I was think the following:

if log2(CN_tumor / CN_normal) = -0.37681

and CN_normal = 2,

then, CN_tumor= (2^-0.37681)*2 = 0.770138593 *2 = 1.540277186

Therefore, CN/diploid = 1.540277186/2 = 0.770138593

Now, if CN_normal = 2, then the CN_tumor/diploid = 0.77

Therefore, if CN_normal = 4, then CN_tumor/diploid should be = 0.77/2 = 0.38

I am more inclined towards CN_tumor/diploid = 0.38 because, if I run the analysis of the same tumor sample with ploidy = 4 against a flat reference, then I get the following result for the same segment:

log2 = -0.387476
Antilog = 0.7645

therefore, CN_tumor/diploid = 0.38225

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but CN_normal is not 4. it is 2. your normal sample is diploid. however, I do not deeply understand the design and assumptions of calling, so it is up to you to decide! =)

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