Welcome to dynamicAtlas’s documentation!#
DynamicAtlas is a MATLAB toolkit for interfacing with a library of fixed and live datasets of a morphological process, here focusing on the stage of development called ‘germ band extension’ in the fruit fly.
Suppose we want to create an lookup table of ALL data in the library. This is simple. First where is the data? Call that path atlasPath
atlasPath = '/path/to/the/data/dynamicAtlas/'
da = dynamicAtlas.dynamicAtlas(atlasPath, {'WT'}) ;``
Now da is an instance of the dynamicAtlas class with access to all the data through its methods.
What if we just want a subset of the WT library? We can look for just certain geonotypes (parent directories) and/or for certain labels (subdirectories in the library). Let’s grab all the data stained for the transcription factor Runt in embryos that have a wild-type genotype. We can create an atlas of this subset of the data like:
options = struct() ;
options.labels = { 'Runt'} ;
da = dynamicAtlas.dynamicAtlas(atlasPath, {'WT'}, options) ;``
Then we could slice all of this data with a given timestamp range (timestamp-delta, timestamp+delta) via
Then we have a queriedSample object called qs. It has some interesting methods like getData() and getPIV() which load the pullback images of the tissue or the velocities for us to analyze it. To execute these methods, simply run
qs.getData() or qs.getPIV().
We can also ask for things like the mean velocity across all the data in the queried sample using qs.getMeanPIV().
Alternatively, we could slice the atlas data for only frames of live datasets of a given genotype and a given label with a given timestamp range:
Similar methods to check out are da.findEmbryo('202006261115'), da.findStaticGenotypeLabel() and similar.
Examples of using dynamicAtlas#
Classes and Folders#
Classes in +dynamicAtlas:
Folders of functions:
Some details on how dynamicAtlas works#
We saw how to make a dynamicAtlas class instance and a queriedSample class instance.
There is one more class in this toolkit that helps it all run smoothly called a lookupMap. You don’t have to use the lookupMap if you want to keep things simple, but in case you want to look under the hood, let’s take a look.
You can instantiate the map using the lookup method of dynamicAtlas:
mapWT = da.lookup('WT') ;
Now mapWT has methods:
methods(mapWT)
To find embryos with a Runt stain, you can say:
runts = findGenotypeLabel('WT', 'Runt')
Alternatively, we can take a peek within the lookup map of da to get all Runt labels, regardless of genotype:
runts = mapWT.findLabel('Runt')
or even index the map stored in da’s lookup map:
runts = mapWT.map('Runt')
To find all embryos with a t=10 +/- 2 min
snaps = mapWT.findTime(10, 2)
To find Runt-labeled embryos with a t=10 +/- 2 min
runtsnaps = mapWT.findLabelTime('Runt', 10, 2)
Master timeline generation#
Fixed samples are timestamped according to their morphology against live datasets. If we want to build such a master timeline from scratch, we can do so as follows.
Use dynamic datasets within the lookupMap to build master timeline. To control how this is performed, toggle da.timeLineMethod:
da.makeMasterTimeline('WT', 'Runt')
Timestamp other data against the master timeline. To control how this is performed, toggle da.timeStampMethod.