## User: Fischer-philipp

Fischer-philipp •

**20**- Reputation:
**20**- Status:
- New User
- Location:
- Last seen:
- 1 week, 3 days ago
- Joined:
- 6 months ago
- Email:
- F**************@gmx.net

#### Posts by Fischer-philipp

<prev
• 16 results •
page 1 of 2 •
next >

0

votes

1

answer

179

views

1

answers

... Hey Michael,
Yes your support is great!
I am sorry that I bothered you with my questions.
All the best =). ...

written 14 days ago by
Fischer-philipp •

**20**0

votes

1

answer

179

views

1

answers

... Another question came to my mind.
Am I getting the cox-reid adjustment right. 1/2 log det xt w x
One penalized values for alpha which have a lot of information on mu?
If so I do not get why this makes sense.
To emphasize on values of alpha coming more from the poisson distribution?
Thanks agai ...

written 14 days ago by
Fischer-philipp •

**20**0

votes

1

answer

179

views

1

answers

... I understand that - thank your for your help. ...

written 15 days ago by
Fischer-philipp •

**20**0

votes

1

answer

179

views

1

answers

... =) nice thanks.
Further it would be great if you are so kind and comment my suggestion (the link) of how to derive the sigma_^2_d of the normal hierarchical.
![model of hierarchical normal with full bayes][1] https://ibb.co/xHcZ2MR
[1]: https://ibb.co/xHcZ2MR ...

written 15 days ago by
Fischer-philipp •

**20**0

votes

1

answer

179

views

1

answers

... Hey - Thank you again.
So I was trying to draw sthg like this:
![Hierarchical normal ][1] https://pasteboard.co/IdKFNpx.png
Which is from: http://idiom.ucsd.edu/~rlevy/pmsl_textbook/chapters/pmsl_8.pdf
![hierarchical normal model deseq2?][2] https://pasteboard.co/IdKGGQF.jpg
Does it make sense?
...

written 16 days ago by
Fischer-philipp •

**20**0

votes

1

answer

179

views

1

answers

... So I am wonderin what the hierarchical normal model looks like:
- ) log(a_i^gw) - log(a_tr(\bar mu_i) ~ N(a_i_gw, S_lr)
- ) a_i^gw ~ N(a_i; sigma^i_lde)
- ) log(a_i) ~ N(log(a_tr(\bar mu_i); Sigma^2_d) as prior
It is really interesting for me what you did - but I am really stuck.
Thank you ag ...

written 4 weeks ago by
Fischer-philipp •

**20**0

votes

1

answer

179

views

1

answers

... Thank you very much for your very helpfull answers.
Unfortunately I am stuck in the section of **Dispersion Prior:**
Additional file 1: Table S2 compares:
- ψ1((m−p)/2) as an approximation of σ2lde with
- the variance of logarithmic Cox–Reid adjusted dispersion.
And says it is very similar.
A ...

written 5 weeks ago by
Fischer-philipp •

**20**4

votes

1

answer

179

views

1

answer

... Hello Community,
The paper of DESe1 says that the forumula [(6)][1] :
A parametric curve of the form (6) is fit by regressing the gene-wise dispersion estimates α_g^wi onto the means of the normalized counts, μ̄ i via a gamma-family GLM regression.
Wihtin the Source code of DESeq2 I found the f ...

written 5 weeks ago by
Fischer-philipp •

**20**• updated 5 weeks ago by Michael Love ♦**23k**0

votes

1

answer

108

views

1

answers

... Thank you for your quick answers.
so it comes from the relationship sigma^2 = \mu + \alpha \mu^2 ?
It still remains a question to me why it is:
`(bv - xim*bm)/bm^2 }`
and not
`(bv - xim*bm)/(xim*bm)^2 }`
or
`(bv - bm)/bm^2`
...

written 7 weeks ago by
Fischer-philipp •

**20**1

vote

1

answer

108

views

1

answer

... Within the function `momentsDisEstimate()` of `DESeq2` a rough method-of-moments estimate of the mean counts is derived by this equation `(bv - xim*bm)/bm^2 `. (I am aware that this is just an initial set to maximize Cox-Reid adjusted likelihood of the gene-wise dispersion estimat.)
So I was curi ...

written 8 weeks ago by
Fischer-philipp •

**20**• updated 8 weeks ago by Michael Love ♦**23k**#### Latest awards to Fischer-philipp

No awards yet. Soon to come :-)

Use of this site constitutes acceptance of our User
Agreement
and Privacy
Policy.

Powered by Biostar
version 16.09

Traffic: 295 users visited in the last hour