
Allen Integrated Cell is a powerful tool for visualizing biology in 3D - breck
https://techcrunch.com/2018/05/09/allen-integrated-cell-is-a-powerful-tool-for-visualizing-biology-in-3d/
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donovanr
just saw this; I'm part of the computational modeling team that worked on this
-- can try to field any questions or find more qualified people to do so.

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m1el
This is probably going to be a lot, but...

1\. Would you advertise this tool as a visualization to help with future
research and understanding cells OR a possible diagnostic aid?

2\. Is there any project that aims to apply these tools to find changes in
cells of an aging organism? Do you think that would be useful?

3\. Is it possible to figure out for any given class of cell how much of its
volume is understood? e.g. "there's this little part and we have no idea
what's going on there" or "this protein is everywhere and we can't figure out
what it does".

4\. How can you evaluate the correctness of your probabilistic model? Neural
nets and auto-encoders are known to produce bad results. as an exaggeration,
you wouldn't want to have this as your model of human face:
[https://zo7.github.io/img/2016-09-25-generating-
faces/random...](https://zo7.github.io/img/2016-09-25-generating-
faces/random-1.jpg)

And thanks for publishing the source code for training!

~~~
donovanr
1\. All of the above. The label free tool in particular gives you such a big
free lunch at the microscope that the combination of it and good visualization
has the potential to massively impact research workflows.

2\. We are very interested in how cells change as they divide, differentiate,
age, are perturbed by their environment, etc. We study cells in culture right
now -- getting good images of in vitro cells from multicellular organisms is
way harder. So yes it would absolutely be useful. I don't know if we're going
to tackle it ourselves, but one of our core missions is to lay the groundwork
for the community to take our tools and run with them -- it's a big win for us
if we can bring previously unfeasible research within the realm of the
possible.

3\. I am a Bayesian at heart, so modeling uncertainty is something that I'm
always thinking about. It's high on my list of priorities to do something
along these lines.

4\. Image similarity is a hard problem. At the end of the day, metrics only
get you so far and the proof is in the pudding. Unfortunately there is no
ground-truth data to test against -- the probabilistic model was constructed
exactly because we can't measure where everything is all at the same time.
Some things we do to convince ourselves that we're on track is to see that the
variation in the imputed predictions and the actual data are statistically
similar, and to see if experts are confounded in differentiating the outputs
of our models from actual data. You can read more here
[https://www.biorxiv.org/content/early/2017/12/21/238378](https://www.biorxiv.org/content/early/2017/12/21/238378)

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bawana
this model says mitochondria are clustered around the nucleus which is not
what i see in the microscope

~~~
donovanr
Characterizing cellular variation is exactly what we're interested in, e.g
when and why are the mitts clustered around the nuclear vs not. Lots of images
in our data have them packed around the nucleus -- you can look at the
localizations from our microscopes here (select the Tom20 tag):
[http://www.allencell.org/3d-cell-
viewer.html](http://www.allencell.org/3d-cell-viewer.html) here. You can also
grab the raw data (including bright field images e.g. what you would "see" in
the microscope) here [http://www.allencell.org/data-
downloading.html#DownloadImage...](http://www.allencell.org/data-
downloading.html#DownloadImageData)

