Level 2 Cache
To understand the level 2 cache, you must understand the structure of the chunkedgraph so see ChunkedGraph.
Nodes on the second level or layer of the graph, corresponds to all the supervoxels that are locally connected to one another within a single level 2 spatial “chunk” of the data. The Level 2 Cache, is a service whose job it is to track and update relevant statistics about every level 2 node within the a chunkedgraph. The source code of this service can be found here.
Finding Level 2 Nodes
The chunkedgraph can be used to find the level2 nodes of a rootID using a stop_layer=2 keyword argument on the get_leaves(). Conversely the level 2 node of a supervoxel can be found using the same keyword argument of get_roots(). Note if you don’t specify a timestamp it will give you the level2 node that is presently associated with the object.
Statistics
The statistics that are available are
Statistic |
Description |
|---|---|
area_nm2 |
The surface area of the object in square nanometers. Does not include border touching voxels |
size_nm3 |
The volume of the object in cubic nanometers, based on counting voxels in the object. |
max_dt_nm |
The maximum edge distance transform of that object in nanometers. Meant to capture the maximum ‘thickness’ of the voxels in the node. |
mean_dt_nm |
The average edge distance transform of that object in nanometers. Meant to capture the average ‘thickness’ of voxels in that node. |
rep_coord_nm |
A list of x,y,z coordinates in nanometers that represent a point within the object that is designed to be close to the ‘center’ of the object. This is the location of the max_dt_nm value. |
chunk_intersect_count |
A 2 x 3 matrix representing the 6 sides of the chunk, and whose values represent how many voxels border that side of the chunk. Meant to help understand significant the borders with other chunks are. Ordering is the [[x_bottom, y_bottom, z_bottom],[x_top, y_top, z_top]] where {xyz}_bottom refers to the face which has the smallest values for that dimension, and {xyz}_top refers to the face which has the largest. |
pca |
A 3x3 matrix representing the principal components of the xyz point cloud of voxels for this object. Ordering is NxD where N is the components and D are the xyz dimensions. Meant to help desribe the orientation of the level 2 chunk. Note that this is not calculated for very small objects and so might not be present for all level 2 nodes. You will see that its availability correlates strongly with size_nm3. |
pca_val |
The 3 principal component values for the PCA components. |
Retrieving Level 2 Statistics
Level 2 stats about nodes can be retreived using the get_l2data() method. It simply takes a list of level 2 nodes you want to retrieve. Optionally you can specify only the attributes that you are interested in retrieving which will speed up the request.
Missing Data
The service is constantly watching for changes made to objects and recalculating stats on new level2 nodes that are created, in order to keep its database of statistics current. This however takes some time, and is subject to sporadic rare failures. If you request stats on a level 2 node which are not in the database, you will receive an empty dictionary for that node. This will immediately trigger the system to recalculate the statistics of that missing data, and so it should be available shortly (on the order of seconds) if systems are operational. Please note that PCA is not calculated for very small objects because it is not meaningful. So if you are interested in differentiating whether PCA is not available because it hasn’t been calculated, vs when its not available because it is not possible to calculate, you should ask for at least one other non PCA statistic as well. You will see that its availability correlates strongly with size_nm3.
Use Cases
Calculate Total Area and Volume of Cells
Say you want to calculate the total surface area and volume of a object in the dataset. The areas and volume of each component can simply be added together to do this.
import pandas as pd
root_id = 648518346349541252
lvl2nodes = client.chunkedgraph.get_leaves(root_id,stop_layer=2)
l2stats=client.l2cache.get_l2data(lvl2nodes, attributes=['size_nm3','area_nm2'])
l2df = pd.DataFrame(l2stats).T
total_area_um2=l2df.area_nm2.sum()/(1000*1000)
total_volume_um3 = l2df.size_nm3.sum()/(1000*1000*1000)
By utilizing the bounds argument of get_leaves, you can also do simple spatially restricted analysis of objects. In fact, because you have data on each level2 node individually, you can segregate the neuron using any labelling of its topology.
Skeletonization
Level 2 nodes have ‘cross chunk’ edges within the chunkedgraph which represent what level 2 nodes that object is locally connected to. This forms a graph between the level 2 nodes of the object that can be retreived using the chunkedgraph function level2_chunk_graph(). This graph represents a topological representation of the neuron at the resolution of individual chunks, and is gaurunteed to be fully connected, unlike a voxel or mesh representation of the neuron which can have gaps where there are defects in the segmentation volume or incorrectly inferred edges at self contact locations.
The level 2 graph can be turned into a skeleton representation of a neuron using a graph based TEASAR like algorithm as described for skeletonizing meshes in this MeshParty Documentation. There is an implementation of this approach that utilizes the chunkedgraph and the L2cache if available here and on pypi as pcg-skel. In this implementation the l2cache is used to more accurately place the level 2 nodes in space using the rep_coord_nm value.
Trajectory Distributions
If one is interested in the bulk direction of processes in a region of the brain, one can start with supervoxels in a region, find level 2 nodes that correspond to them, filter out components based on size, (or other criteria such as whether they are part of objects that have components in some other brain area) and look at the distribution of PCA components to understand the directions that those processes are moving within that region of space.