[d17]

As I see it from some previous posts, the choice for distribution of Q won’t affect the distribution of K (the # of mapped reads) to be modeled as negative binomial:

"However, simulation shows that misspecification of the distribution for Q does not cause the results too change much, i.e., it does not really matter whether we use log-normal or gamma or something else of roughly this shape. (Sorry, can't find the references for this now; ask again if you need them.)"

I appreciate if you can let me know about any references that you were referring to in that message.

Thanks for pointing that out to me, tldgID!
I would still be curious, though, to find out whether there is any particular reason everyone has chosen negative binomial, or whether it is a historical accident of sorts. If it truly doesn't really matter whether you use log-normal or gamma for the distribution of Q, then I would expect that at least someone, somewhere would have published a Poisson log-normal model.

[Simon Anders]

I appreciate if you can let me know about any references that you were referring to in that message.

I think I had this paper here in mind:

Firth D:
Multiplicative Errors: Log-normal or Gamma?
J. R. Statist. Soc. B. 1988;50:266-268.

(But I don't have access to it at the moment, so I can't double-check whether it really supports my claim. I simply hope so. ;-) )


This one here might be relevant, too:

Jun Lu, John K Tomfohr and Thomas B Kepler:
BMC Bioinformatics 2005, 6:165
doi:10.1186/1471-2105-6-165


Simon

[tldgID]

Thanks Simon! I'll check them out!

[anle]

Hi Simon I have some things confused in mmy mind and I would need some help
You say:
"Let Q be a random variable with gamma distribution with expectation q0 and variance a q0²"

If I understand well q is the shape and O the scale parameter of the Gamma. How did u end up to the conclusion that the distribution of Q is coming with these parameters? Also if I understand well then the P(K|q) follows poison with mean,var=q. How can one get again to this result??
If my question is to naive to explain maybe u can suggest some reading (bayesian statistics for dummies or sthng )

[Simon Anders]

There is no letter 'O'. This is a zero. I wanted to write 'q0' with zero as subscript but did not manage. (Don't know if the forum software allows subscripts.) So, 'q0' is one value (and distinct from q, which I use further down for the realization of the random variable Q) and 'a' is the other one.

I specified the first two moments. Solving the expressions for them for the shape and scale parameter, you get for the scale a q0 and for the shape 1/a.

By the way, this is all frequentist statistics. There is nothing Bayesian in here.

[anle]

There is no letter 'O'. This is a zero. I wanted to write 'q0' with zero as subscript but did not manage. (Don't know if the forum software allows subscripts.) So, 'q0' is one value (and distinct from q, which I use further down for the realization of the random variable Q) and 'a' is the other one.

I specified the first two moments. Solving the expressions for them for the shape and scale parameter, you get for the scale a q0 and for the shape 1/a.

By the way, this is all frequentist statistics. There is nothing Bayesian in here.

Thnx!! Sorry I was misconfused but marginalization and conditional probabilities make me think of Bayes

[chaoxing]

Some questions about DESeq and DESeq2

Hi Simon,

I have three questions about DESeq here(referring to "Differential expression analysis for sequence count data"):

1. You set up three assumptions, what is the basis of equation(3) and eqation(4)? How to explain them? If explain details with intuition and mathematics, that will be best. Thank you very much.

2. Another basic question: why you set p=u/(sigma^2) and r=(u^2)/(sigma^2-u) ? How to explain it with intuition and mathematics as well? Thank you very much.

3. I also ran DESeq2 for my expression data, but I compare DESeq result with DESeq2 one, the number of differential expression miRNA is way more far. DESeq screen about 40 out of 1040 miRNAs, while DESeq2 screen about 400more out of 1040 miRNAs. Furthermore, the rank of these screened miRNAs is also inconsistent. So could you give some rough ideas to explain the difference between the two tools? Thank you.

[Simon Anders]

Please don't highjack an old thread to post unrelated questions. This makes it impossible for other readers to find pertinent information.

Also, please don't put several unrealted questions into the same thread.

Finally, I have sent you the link to this thread when you asked the same question by e-mail, because I feel that it answers your first question. Have you read the thread? Did it answer your question? If not, what is unclear about the equations?

[Simon Anders]

Finally, I have no clue what you mean with p and r. I don't recall using these letters in our paper.

[chaoxing]

Pr(K=k)=(k+r-1,r-1)p^r(1-p)^k,
you set p=u/sigma^2, r=u^2/(sigma^2-u),

p and r are derived from here.

Thank you.