general analysis ideas

[stripathy]

Adam Calhoun posted some simple analyses of the Cosyne Abstracts across time. Maybe they're inspiring for the kinds of things we'd want to do?
https://neuroecology.wordpress.com/2016/02/23/cosyne2016-by-the-numbers/

[rgerkin]

Cool. I just asked him on twitter if he's got the code to share, since those results look right enough to me that I'd trust applying it here. Then we can cite his blog post!

[stripathy]

Yeah! @svdavid if you'll be at cosyne, you should try talking with Adam Calhoun about what we're doing. Adam's a really cool and nice guy. We had a 3 hour dinner at cosyne a couple years back talking about the neuroscience of why cats are so popular on the internets.

[svdavid]

That's pretty cool. Yeah I will be at Cosyne and will look him up.

On Tue, Feb 23, 2016 at 1:22 PM, Shreejoy Tripathy <[email protected]

wrote:

Yeah! @svdavid https://github.com/svdavid if you'll be at cosyne, you
should try talking with Adam Calhoun about what we're doing. Adam's a
really cool and nice guy. We had a 3 hour dinner at cosyne a couple years
back talking about the neuroscience of why cats are so popular on the
internets.

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Reply to this email directly or view it on GitHub
#10 (comment)
.

[rgerkin]

@stripathy @svdavid I didn't hear from him but I implemented something similar in 2cc98bc, which created this. The results are reasonably intuitive.

I also tried non-negative matrix factorization and sparse PCA, but I got less intuitive results, and I think part of the reason is that because there aren't really obvious clusters in the network, those algorithms don't really get you anything.

One limitation is that many of the ancestors aren't actually in the adjacency matrix (because they aren't in the distance matrix). Only 58 of the 437 ancestors (as marked 'p0' in the distance file @svdavid provided) are also listed as nodes ('p1' or 'p2') in that file. So I'm not sure what the criteria were for inclusion/exclusion. Since neurotree is more of a tree than a bush, and there are more entries with each generation, the most connected people (i.e. having the most edges between themselves and other nodes) are likely to be the people approximately one generation in the past. I don't know if this is a bias we should be trying to correct, but I guess it depends what the point of all of this is.

[svdavid]

Is the fact that p0 is not in the NE author list a problem? p0 is the id
of the person in the tree that joins p1 and p2 (ie, the closest common
ancestor). The value of p0 in my mind was simply a reference for
clustering. Is there some other info you'd like about those nodes to make
your analysis work?

stephen

On Thu, Feb 25, 2016 at 1:16 PM, Richard C Gerkin [email protected]
wrote:

@stripathy https://github.com/stripathy @svdavid
https://github.com/svdavid I didn't hear from him but I implemented
something similar in 2cc98bc
2cc98bc,
which created this
https://github.com/neuroelectro/neuroelectro_neurotree/blob/master/eigenvectors.ipynb.
The results are reasonably intuitive.

I also tried non-negative matrix factorization and sparse PCA, but I got
less intuitive results, and I think part of the reason is that because
there aren't really obvious clusters in the network, those algorithms don't
really get you anything.

One limitation is that many of the ancestors aren't actually in the
adjacency matrix (because they aren't in the distance matrix). Only 58 of
the 437 ancestors (as marked 'p0' in the distance file @svdavid
https://github.com/svdavid provided) are also listed as nodes ('p1' or
'p2') in that file. So I'm not sure what the criteria were for
inclusion/exclusion. Since neurotree is more of a tree than a bush, and
there are more entries with each generation, the most connected people
(i.e. having the most edges between themselves and other nodes) are likely
to be the people approximately one generation in the past. I don't know if
this is a bias we should be trying to correct, but I guess it depends what
the point of all of this is.

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#10 (comment)
.

[rgerkin]

@svdavid david Right, I was thinking that the p0-type people would become the eigenvectors, if they were included, so maybe they should be? What was the cutoff, anyway? Being in neuroelectro? Being in pubmed?

[stripathy]

@rgerkin I think the cutoff was "being a last author in neuroelectro", but we discussed whether it made more sense for this matrix to also include the p0 people as well (I think it should).

[svdavid]

@rgerkin @stripathy See comment about fingerprint_mtx in #2. I think this may resolve the issue?