## User: Fischer-philipp

Reputation:
20
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New User
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Last seen:
1 day, 16 hours ago
Joined:
12 months ago
Email:
F**************@gmx.net

#### Posts by Fischer-philipp

<prev • 16 results • page 1 of 2 • next >
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... Hey Michael, Yes your support is great! I am sorry that I bothered you with my questions. All the best =). ...
written 6 months ago by Fischer-philipp20
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... 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 6 months ago by Fischer-philipp20
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... I understand that - thank your for your help. ...
written 6 months ago by Fischer-philipp20
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... =) 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 6 months ago by Fischer-philipp20
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... 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 6 months ago by Fischer-philipp20
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... 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 7 months ago by Fischer-philipp20
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... 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 7 months ago by Fischer-philipp20
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... 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 7 months ago by Fischer-philipp20 • updated 7 months ago by Michael Love26k
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... 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 months ago by Fischer-philipp20
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... 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 7 months ago by Fischer-philipp20 • updated 7 months ago by Michael Love26k

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