{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Mouse Connectivity\n", "\n", "This notebook demonstrates how to access and manipulate data in the Allen Mouse Brain Connectivity Atlas. The `MouseConnectivityCache` AllenSDK class provides methods for downloading metadata about experiments, including their viral injection site and the mouse's transgenic line. You can request information either as a Pandas DataFrame or a simple list of dictionaries.\n", "\n", "An important feature of the `MouseConnectivityCache` is how it stores and retrieves data for you. By default, it will create (or read) a manifest file that keeps track of where various connectivity atlas data are stored. If you request something that has not already been downloaded, it will download it and store it in a well known location.\n", "\n", "Download this notebook in .ipynb format here." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2995 total experiments\n" ] }, { "data": { "text/plain": [ "gender M\n", "id 122642490\n", "injection_structures [985, 993]\n", "injection_volume 0.151071\n", "injection_x 4300\n", "injection_y 2690\n", "injection_z 7050\n", "primary_injection_structure 985\n", "product_id 5\n", "specimen_name Syt6-Cre-585\n", "strain C57BL/6J\n", "structure_abbrev MOs\n", "structure_id 993\n", "structure_name Secondary motor area\n", "transgenic_line Syt6-Cre_KI148\n", "transgenic_line_id 1.77839e+08\n", "Name: 122642490, dtype: object" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from allensdk.core.mouse_connectivity_cache import MouseConnectivityCache\n", "\n", "# The manifest file is a simple JSON file that keeps track of all of\n", "# the data that has already been downloaded onto the hard drives.\n", "# If you supply a relative path, it is assumed to be relative to your\n", "# current working directory.\n", "mcc = MouseConnectivityCache()\n", "\n", "# open up a list of all of the experiments\n", "all_experiments = mcc.get_experiments(dataframe=True)\n", "print(\"%d total experiments\" % len(all_experiments))\n", "\n", "# take a look at what we know about an experiment with a primary motor injection\n", "all_experiments.loc[122642490]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`MouseConnectivityCache` has a method for retrieving the adult mouse structure tree as an `StructureTree` class instance. This is a wrapper around a list of dictionaries, where each dictionary describes a structure. It is principally useful for looking up structures by their properties." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
acronymgraph_idgraph_orderidnamergb_tripletstructure_id_pathstructure_set_ids
0VISp1185385Primary visual area[8, 133, 140][997, 8, 567, 688, 695, 315, 669, 385][396673091, 112905828, 688152357, 691663206, 6...
1HY17151097Hypothalamus[230, 68, 56][997, 8, 343, 1129, 1097][2, 112905828, 691663206, 12, 184527634, 11290...
\n", "
" ], "text/plain": [ " acronym graph_id graph_order id name rgb_triplet \\\n", "0 VISp 1 185 385 Primary visual area [8, 133, 140] \n", "1 HY 1 715 1097 Hypothalamus [230, 68, 56] \n", "\n", " structure_id_path \\\n", "0 [997, 8, 567, 688, 695, 315, 669, 385] \n", "1 [997, 8, 343, 1129, 1097] \n", "\n", " structure_set_ids \n", "0 [396673091, 112905828, 688152357, 691663206, 6... \n", "1 [2, 112905828, 691663206, 12, 184527634, 11290... " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# pandas for nice tables\n", "import pandas as pd\n", "\n", "# grab the StructureTree instance\n", "structure_tree = mcc.get_structure_tree()\n", "\n", "# get info on some structures\n", "structures = structure_tree.get_structures_by_name(['Primary visual area', 'Hypothalamus'])\n", "pd.DataFrame(structures)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a convenience, structures are grouped in to named collections called \"structure sets\". These sets can be used to quickly gather a useful subset of structures from the tree. The criteria used to define structure sets are eclectic; a structure set might list:\n", "\n", "* structures that were used in a particular project.\n", "* structures that coarsely partition the brain.\n", "* structures that bear functional similarity.\n", "\n", "or something else entirely. To view all of the available structure sets along with their descriptions, follow this [link](http://api.brain-map.org/api/v2/data/StructureSet/query.json). To see only structure sets relevant to the adult mouse brain, use the StructureTree:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
descriptionidname
0List of structures in Isocortex layer 5667481446Isocortex layer 5
1List of structures in Isocortex layer 6b667481450Isocortex layer 6b
2Summary structures of the cerebellum688152368Cerebellum
3List of structures for ABA Differential Search12ABA - Differential Search
4List of valid structures for projection target...184527634Mouse Connectivity - Target Search
5Structures whose surfaces are represented by a...691663206Mouse Brain - Has Surface Mesh
6Summary structures of the midbrain688152365Midbrain
7Summary structures of the medulla688152367Medulla
8Summary structures of the striatum688152361Striatum
9Structures representing subdivisions of the mo...687527945Mouse Connectivity - Summary
10Summary structures of the hippocampal formation688152359Hippocampal Formation
11List of visual cortex structures targeted for ...514166994Allen Brain Observatory targeted structure set
12Summary structures of the olfactory areas688152358Olfactory Areas
13Curated list of non-overlapping substructures ...167587189Brain – Summary Structures
14List of structures in Isocortex layer 4667481445Isocortex layer 4
15Structures representing the major divisions of...687527670Brain - Major Divisions
16Summary structures of the pallidum688152362Pallidum
17List of Primary injection structures for BDA/A...114512892Mouse Connectivity - BDA/AAV Primary Injection...
18List of primary AND secondary injection struct...112905813Mouse Connectivity - BDA/AAV All Injection Str...
19List of structures for ABA Fine Structure Search10ABA - Fine Structure Search
20List of primary AND secondary injection struct...112905828Mouse Connectivity - Projection All Injection ...
21List of structures in Isocortex layer 6a667481449Isocortex layer 6a
22List of structures representing a areal level ...3Mouse - Areas
23List of structures in Isocortex layer 1667481440Isocortex layer 1
24Summary structures of the hypothalamus688152364Hypothalamus
25List of structures in Isocortex layer 2/3667481441Isocortex layer 2/3
26All mouse visual areas with layers396673091Mouse Cell Types - Structures
27Summary structures of the cortical subplate688152360Cortical Subplate
28Summary structures of the thalamus688152363Thalamus
29List of structures representing a coarse level...2Mouse - Coarse
30Summary structures of the isocortex688152357Isocortex
31List of Primary injection structures for Proje...114512891Mouse Connectivity - Projection Primary Inject...
32Summary structures of the pons688152366Pons
\n", "
" ], "text/plain": [ " description id \\\n", "0 List of structures in Isocortex layer 5 667481446 \n", "1 List of structures in Isocortex layer 6b 667481450 \n", "2 Summary structures of the cerebellum 688152368 \n", "3 List of structures for ABA Differential Search 12 \n", "4 List of valid structures for projection target... 184527634 \n", "5 Structures whose surfaces are represented by a... 691663206 \n", "6 Summary structures of the midbrain 688152365 \n", "7 Summary structures of the medulla 688152367 \n", "8 Summary structures of the striatum 688152361 \n", "9 Structures representing subdivisions of the mo... 687527945 \n", "10 Summary structures of the hippocampal formation 688152359 \n", "11 List of visual cortex structures targeted for ... 514166994 \n", "12 Summary structures of the olfactory areas 688152358 \n", "13 Curated list of non-overlapping substructures ... 167587189 \n", "14 List of structures in Isocortex layer 4 667481445 \n", "15 Structures representing the major divisions of... 687527670 \n", "16 Summary structures of the pallidum 688152362 \n", "17 List of Primary injection structures for BDA/A... 114512892 \n", "18 List of primary AND secondary injection struct... 112905813 \n", "19 List of structures for ABA Fine Structure Search 10 \n", "20 List of primary AND secondary injection struct... 112905828 \n", "21 List of structures in Isocortex layer 6a 667481449 \n", "22 List of structures representing a areal level ... 3 \n", "23 List of structures in Isocortex layer 1 667481440 \n", "24 Summary structures of the hypothalamus 688152364 \n", "25 List of structures in Isocortex layer 2/3 667481441 \n", "26 All mouse visual areas with layers 396673091 \n", "27 Summary structures of the cortical subplate 688152360 \n", "28 Summary structures of the thalamus 688152363 \n", "29 List of structures representing a coarse level... 2 \n", "30 Summary structures of the isocortex 688152357 \n", "31 List of Primary injection structures for Proje... 114512891 \n", "32 Summary structures of the pons 688152366 \n", "\n", " name \n", "0 Isocortex layer 5 \n", "1 Isocortex layer 6b \n", "2 Cerebellum \n", "3 ABA - Differential Search \n", "4 Mouse Connectivity - Target Search \n", "5 Mouse Brain - Has Surface Mesh \n", "6 Midbrain \n", "7 Medulla \n", "8 Striatum \n", "9 Mouse Connectivity - Summary \n", "10 Hippocampal Formation \n", "11 Allen Brain Observatory targeted structure set \n", "12 Olfactory Areas \n", "13 Brain – Summary Structures \n", "14 Isocortex layer 4 \n", "15 Brain - Major Divisions \n", "16 Pallidum \n", "17 Mouse Connectivity - BDA/AAV Primary Injection... \n", "18 Mouse Connectivity - BDA/AAV All Injection Str... \n", "19 ABA - Fine Structure Search \n", "20 Mouse Connectivity - Projection All Injection ... \n", "21 Isocortex layer 6a \n", "22 Mouse - Areas \n", "23 Isocortex layer 1 \n", "24 Hypothalamus \n", "25 Isocortex layer 2/3 \n", "26 Mouse Cell Types - Structures \n", "27 Cortical Subplate \n", "28 Thalamus \n", "29 Mouse - Coarse \n", "30 Isocortex \n", "31 Mouse Connectivity - Projection Primary Inject... \n", "32 Pons " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from allensdk.api.queries.ontologies_api import OntologiesApi\n", "\n", "oapi = OntologiesApi()\n", "\n", "# get the ids of all the structure sets in the tree\n", "structure_set_ids = structure_tree.get_structure_sets()\n", "\n", "# query the API for information on those structure sets\n", "pd.DataFrame(oapi.get_structure_sets(structure_set_ids))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On the connectivity atlas web site, you'll see that we show most of our data at a fairly coarse structure level. We did this by creating a structure set of ~300 structures, which we call the \"summary structures\". We can use the structure tree to get all of the structures in this set:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
acronymgraph_idgraph_orderidnamergb_tripletstructure_id_pathstructure_set_ids
0FRP16184Frontal pole, cerebral cortex[38, 143, 69][997, 8, 567, 688, 695, 315, 184][3, 112905828, 688152357, 691663206, 687527945...
1MOp118985Primary motor area[31, 157, 90][997, 8, 567, 688, 695, 315, 500, 985][112905828, 688152357, 691663206, 687527945, 1...
2MOs124993Secondary motor area[31, 157, 90][997, 8, 567, 688, 695, 315, 500, 993][112905828, 688152357, 691663206, 687527945, 1...
3SSp-n144353Primary somatosensory area, nose[24, 128, 100][997, 8, 567, 688, 695, 315, 453, 322, 353][112905828, 688152357, 691663206, 687527945, 1...
4SSp-bfd151329Primary somatosensory area, barrel field[24, 128, 100][997, 8, 567, 688, 695, 315, 453, 322, 329][112905828, 688152357, 691663206, 687527945, 1...
5SSp-ll165337Primary somatosensory area, lower limb[24, 128, 100][997, 8, 567, 688, 695, 315, 453, 322, 337][112905828, 688152357, 691663206, 687527945, 1...
6SSp-m172345Primary somatosensory area, mouth[24, 128, 100][997, 8, 567, 688, 695, 315, 453, 322, 345][112905828, 688152357, 691663206, 687527945, 1...
7SSp-ul179369Primary somatosensory area, upper limb[24, 128, 100][997, 8, 567, 688, 695, 315, 453, 322, 369][112905828, 688152357, 691663206, 687527945, 1...
8SSp-tr186361Primary somatosensory area, trunk[24, 128, 100][997, 8, 567, 688, 695, 315, 453, 322, 361][112905828, 688152357, 691663206, 687527945, 1...
9SSp-un193182305689Primary somatosensory area, unassigned[24, 128, 100][997, 8, 567, 688, 695, 315, 453, 322, 182305689][112905828, 688152357, 691663206, 687527945, 1...
10SSs1100378Supplemental somatosensory area[24, 128, 100][997, 8, 567, 688, 695, 315, 453, 378][112905828, 688152357, 691663206, 687527945, 1...
11GU11071057Gustatory areas[0, 156, 117][997, 8, 567, 688, 695, 315, 1057][3, 112905828, 688152357, 691663206, 687527945...
12VISC1114677Visceral area[17, 173, 131][997, 8, 567, 688, 695, 315, 677][3, 112905828, 688152357, 691663206, 687527945...
13AUDd11221011Dorsal auditory area[1, 147, 153][997, 8, 567, 688, 695, 315, 247, 1011][112905828, 688152357, 691663206, 687527945, 1...
14AUDp11361002Primary auditory area[1, 147, 153][997, 8, 567, 688, 695, 315, 247, 1002][112905828, 688152357, 691663206, 687527945, 1...
15AUDpo11431027Posterior auditory area[1, 147, 153][997, 8, 567, 688, 695, 315, 247, 1027][112905828, 688152357, 691663206, 687527945, 1...
16AUDv11501018Ventral auditory area[1, 147, 153][997, 8, 567, 688, 695, 315, 247, 1018][112905828, 688152357, 691663206, 687527945, 1...
17VISal1164402Anterolateral visual area[8, 133, 140][997, 8, 567, 688, 695, 315, 669, 402][396673091, 112905828, 688152357, 691663206, 6...
18VISam1171394Anteromedial visual area[8, 133, 140][997, 8, 567, 688, 695, 315, 669, 394][396673091, 112905828, 688152357, 691663206, 6...
19VISl1178409Lateral visual area[8, 133, 140][997, 8, 567, 688, 695, 315, 669, 409][396673091, 112905828, 688152357, 691663206, 6...
20VISp1185385Primary visual area[8, 133, 140][997, 8, 567, 688, 695, 315, 669, 385][396673091, 112905828, 688152357, 691663206, 6...
21VISpl1192425Posterolateral visual area[8, 133, 140][997, 8, 567, 688, 695, 315, 669, 425][396673091, 112905828, 688152357, 691663206, 6...
22VISpm1199533posteromedial visual area[8, 133, 140][997, 8, 567, 688, 695, 315, 669, 533][396673091, 112905828, 688152357, 691663206, 6...
23VISli1206312782574Laterointermediate area[8, 133, 140][997, 8, 567, 688, 695, 315, 669, 312782574][396673091, 112905828, 688152357, 691663206, 6...
24VISpor1213312782628Postrhinal area[8, 133, 140][997, 8, 567, 688, 695, 315, 669, 312782628][396673091, 112905828, 688152357, 691663206, 6...
25ACAd122639Anterior cingulate area, dorsal part[64, 166, 102][997, 8, 567, 688, 695, 315, 31, 39][112905828, 688152357, 691663206, 687527945, 1...
26ACAv123248Anterior cingulate area, ventral part[64, 166, 102][997, 8, 567, 688, 695, 315, 31, 48][112905828, 688152357, 691663206, 687527945, 1...
27PL1238972Prelimbic area[47, 168, 80][997, 8, 567, 688, 695, 315, 972][3, 112905828, 688152357, 691663206, 687527945...
28ILA124544Infralimbic area[89, 179, 99][997, 8, 567, 688, 695, 315, 44][3, 112905828, 688152357, 691663206, 687527945...
29ORBl1258723Orbital area, lateral part[36, 138, 94][997, 8, 567, 688, 695, 315, 714, 723][112905828, 688152357, 691663206, 687527945, 1...
...........................
286PPY19981069Parapyramidal nucleus[255, 179, 217][997, 8, 343, 1065, 354, 370, 1069][112905828, 691663206, 687527945, 12, 68815236...
287LAV11002209Lateral vestibular nucleus[255, 179, 217][997, 8, 343, 1065, 354, 370, 701, 209][112905828, 691663206, 687527945, 12, 68815236...
288MV11003202Medial vestibular nucleus[255, 179, 217][997, 8, 343, 1065, 354, 370, 701, 202][112905828, 691663206, 687527945, 12, 68815236...
289SPIV11004225Spinal vestibular nucleus[255, 179, 217][997, 8, 343, 1065, 354, 370, 701, 225][112905828, 691663206, 687527945, 12, 68815236...
290SUV11005217Superior vestibular nucleus[255, 179, 217][997, 8, 343, 1065, 354, 370, 701, 217][112905828, 691663206, 687527945, 12, 68815236...
291x11006765Nucleus x[255, 179, 217][997, 8, 343, 1065, 354, 370, 765][112905828, 691663206, 687527945, 12, 68815236...
292XII11007773Hypoglossal nucleus[255, 179, 217][997, 8, 343, 1065, 354, 370, 773][112905828, 691663206, 687527945, 10, 12, 6881...
293y11008781Nucleus y[255, 179, 217][997, 8, 343, 1065, 354, 370, 781][112905828, 691663206, 687527945, 12, 68815236...
294RM11011206Nucleus raphe magnus[255, 198, 226][997, 8, 343, 1065, 354, 379, 206][112905828, 691663206, 687527945, 12, 68815236...
295RPA11012230Nucleus raphe pallidus[255, 198, 226][997, 8, 343, 1065, 354, 379, 230][112905828, 691663206, 687527945, 12, 68815236...
296RO11013222Nucleus raphe obscurus[255, 198, 226][997, 8, 343, 1065, 354, 379, 222][112905828, 691663206, 687527945, 12, 68815236...
297LING11020912Lingula (I)[255, 252, 145][997, 8, 512, 528, 645, 912][112905828, 691663206, 687527945, 12, 68815236...
298CENT11024920Central lobule[255, 252, 145][997, 8, 512, 528, 645, 920][112905828, 691663206, 687527945, 12, 68815236...
299CUL11033928Culmen[255, 252, 145][997, 8, 512, 528, 645, 928][112905828, 691663206, 687527945, 12, 68815236...
300DEC11046936Declive (VI)[255, 252, 145][997, 8, 512, 528, 645, 936][112905828, 691663206, 687527945, 12, 68815236...
301FOTU11050944Folium-tuber vermis (VII)[255, 252, 145][997, 8, 512, 528, 645, 944][112905828, 691663206, 687527945, 12, 68815236...
302PYR11054951Pyramus (VIII)[255, 252, 145][997, 8, 512, 528, 645, 951][112905828, 691663206, 687527945, 12, 68815236...
303UVU11058957Uvula (IX)[255, 252, 145][997, 8, 512, 528, 645, 957][112905828, 691663206, 687527945, 12, 68815236...
304NOD11062968Nodulus (X)[255, 252, 145][997, 8, 512, 528, 645, 968][112905828, 691663206, 687527945, 12, 68815236...
305SIM110671007Simple lobule[255, 252, 145][997, 8, 512, 528, 1073, 1007][112905828, 691663206, 687527945, 12, 68815236...
306AN110711017Ansiform lobule[255, 252, 145][997, 8, 512, 528, 1073, 1017][112905828, 691663206, 687527945, 12, 68815236...
307PRM110801025Paramedian lobule[255, 252, 145][997, 8, 512, 528, 1073, 1025][112905828, 691663206, 687527945, 12, 68815236...
308COPY110841033Copula pyramidis[255, 252, 145][997, 8, 512, 528, 1073, 1033][112905828, 691663206, 687527945, 12, 68815236...
309PFL110881041Paraflocculus[255, 252, 145][997, 8, 512, 528, 1073, 1041][112905828, 691663206, 687527945, 12, 68815236...
310FL110921049Flocculus[255, 252, 145][997, 8, 512, 528, 1073, 1049][112905828, 691663206, 687527945, 12, 68815236...
311FN11097989Fastigial nucleus[255, 253, 188][997, 8, 512, 519, 989][112905828, 691663206, 687527945, 12, 68815236...
312IP1109891Interposed nucleus[255, 253, 188][997, 8, 512, 519, 91][112905828, 691663206, 687527945, 12, 68815236...
313DN11099846Dentate nucleus[255, 253, 188][997, 8, 512, 519, 846][112905828, 691663206, 687527945, 12, 68815236...
314VeCB11100589508455Vestibulocerebellar nucleus[255, 253, 188][997, 8, 512, 519, 589508455][112905828, 691663206, 688152368, 184527634, 1...
315fiber tracts111011009fiber tracts[204, 204, 204][997, 1009][687527945, 184527634, 167587189, 691663206]
\n", "

316 rows × 8 columns

\n", "
" ], "text/plain": [ " acronym graph_id graph_order id \\\n", "0 FRP 1 6 184 \n", "1 MOp 1 18 985 \n", "2 MOs 1 24 993 \n", "3 SSp-n 1 44 353 \n", "4 SSp-bfd 1 51 329 \n", "5 SSp-ll 1 65 337 \n", "6 SSp-m 1 72 345 \n", "7 SSp-ul 1 79 369 \n", "8 SSp-tr 1 86 361 \n", "9 SSp-un 1 93 182305689 \n", "10 SSs 1 100 378 \n", "11 GU 1 107 1057 \n", "12 VISC 1 114 677 \n", "13 AUDd 1 122 1011 \n", "14 AUDp 1 136 1002 \n", "15 AUDpo 1 143 1027 \n", "16 AUDv 1 150 1018 \n", "17 VISal 1 164 402 \n", "18 VISam 1 171 394 \n", "19 VISl 1 178 409 \n", "20 VISp 1 185 385 \n", "21 VISpl 1 192 425 \n", "22 VISpm 1 199 533 \n", "23 VISli 1 206 312782574 \n", "24 VISpor 1 213 312782628 \n", "25 ACAd 1 226 39 \n", "26 ACAv 1 232 48 \n", "27 PL 1 238 972 \n", "28 ILA 1 245 44 \n", "29 ORBl 1 258 723 \n", ".. ... ... ... ... \n", "286 PPY 1 998 1069 \n", "287 LAV 1 1002 209 \n", "288 MV 1 1003 202 \n", "289 SPIV 1 1004 225 \n", "290 SUV 1 1005 217 \n", "291 x 1 1006 765 \n", "292 XII 1 1007 773 \n", "293 y 1 1008 781 \n", "294 RM 1 1011 206 \n", "295 RPA 1 1012 230 \n", "296 RO 1 1013 222 \n", "297 LING 1 1020 912 \n", "298 CENT 1 1024 920 \n", "299 CUL 1 1033 928 \n", "300 DEC 1 1046 936 \n", "301 FOTU 1 1050 944 \n", "302 PYR 1 1054 951 \n", "303 UVU 1 1058 957 \n", "304 NOD 1 1062 968 \n", "305 SIM 1 1067 1007 \n", "306 AN 1 1071 1017 \n", "307 PRM 1 1080 1025 \n", "308 COPY 1 1084 1033 \n", "309 PFL 1 1088 1041 \n", "310 FL 1 1092 1049 \n", "311 FN 1 1097 989 \n", "312 IP 1 1098 91 \n", "313 DN 1 1099 846 \n", "314 VeCB 1 1100 589508455 \n", "315 fiber tracts 1 1101 1009 \n", "\n", " name rgb_triplet \\\n", "0 Frontal pole, cerebral cortex [38, 143, 69] \n", "1 Primary motor area [31, 157, 90] \n", "2 Secondary motor area [31, 157, 90] \n", "3 Primary somatosensory area, nose [24, 128, 100] \n", "4 Primary somatosensory area, barrel field [24, 128, 100] \n", "5 Primary somatosensory area, lower limb [24, 128, 100] \n", "6 Primary somatosensory area, mouth [24, 128, 100] \n", "7 Primary somatosensory area, upper limb [24, 128, 100] \n", "8 Primary somatosensory area, trunk [24, 128, 100] \n", "9 Primary somatosensory area, unassigned [24, 128, 100] \n", "10 Supplemental somatosensory area [24, 128, 100] \n", "11 Gustatory areas [0, 156, 117] \n", "12 Visceral area [17, 173, 131] \n", "13 Dorsal auditory area [1, 147, 153] \n", "14 Primary auditory area [1, 147, 153] \n", "15 Posterior auditory area [1, 147, 153] \n", "16 Ventral auditory area [1, 147, 153] \n", "17 Anterolateral visual area [8, 133, 140] \n", "18 Anteromedial visual area [8, 133, 140] \n", "19 Lateral visual area [8, 133, 140] \n", "20 Primary visual area [8, 133, 140] \n", "21 Posterolateral visual area [8, 133, 140] \n", "22 posteromedial visual area [8, 133, 140] \n", "23 Laterointermediate area [8, 133, 140] \n", "24 Postrhinal area [8, 133, 140] \n", "25 Anterior cingulate area, dorsal part [64, 166, 102] \n", "26 Anterior cingulate area, ventral part [64, 166, 102] \n", "27 Prelimbic area [47, 168, 80] \n", "28 Infralimbic area [89, 179, 99] \n", "29 Orbital area, lateral part [36, 138, 94] \n", ".. ... ... \n", "286 Parapyramidal nucleus [255, 179, 217] \n", "287 Lateral vestibular nucleus [255, 179, 217] \n", "288 Medial vestibular nucleus [255, 179, 217] \n", "289 Spinal vestibular nucleus [255, 179, 217] \n", "290 Superior vestibular nucleus [255, 179, 217] \n", "291 Nucleus x [255, 179, 217] \n", "292 Hypoglossal nucleus [255, 179, 217] \n", "293 Nucleus y [255, 179, 217] \n", "294 Nucleus raphe magnus [255, 198, 226] \n", "295 Nucleus raphe pallidus [255, 198, 226] \n", "296 Nucleus raphe obscurus [255, 198, 226] \n", "297 Lingula (I) [255, 252, 145] \n", "298 Central lobule [255, 252, 145] \n", "299 Culmen [255, 252, 145] \n", "300 Declive (VI) [255, 252, 145] \n", "301 Folium-tuber vermis (VII) [255, 252, 145] \n", "302 Pyramus (VIII) [255, 252, 145] \n", "303 Uvula (IX) [255, 252, 145] \n", "304 Nodulus (X) [255, 252, 145] \n", "305 Simple lobule [255, 252, 145] \n", "306 Ansiform lobule [255, 252, 145] \n", "307 Paramedian lobule [255, 252, 145] \n", "308 Copula pyramidis [255, 252, 145] \n", "309 Paraflocculus [255, 252, 145] \n", "310 Flocculus [255, 252, 145] \n", "311 Fastigial nucleus [255, 253, 188] \n", "312 Interposed nucleus [255, 253, 188] \n", "313 Dentate nucleus [255, 253, 188] \n", "314 Vestibulocerebellar nucleus [255, 253, 188] \n", "315 fiber tracts [204, 204, 204] \n", "\n", " structure_id_path \\\n", "0 [997, 8, 567, 688, 695, 315, 184] \n", "1 [997, 8, 567, 688, 695, 315, 500, 985] \n", "2 [997, 8, 567, 688, 695, 315, 500, 993] \n", "3 [997, 8, 567, 688, 695, 315, 453, 322, 353] \n", "4 [997, 8, 567, 688, 695, 315, 453, 322, 329] \n", "5 [997, 8, 567, 688, 695, 315, 453, 322, 337] \n", "6 [997, 8, 567, 688, 695, 315, 453, 322, 345] \n", "7 [997, 8, 567, 688, 695, 315, 453, 322, 369] \n", "8 [997, 8, 567, 688, 695, 315, 453, 322, 361] \n", "9 [997, 8, 567, 688, 695, 315, 453, 322, 182305689] \n", "10 [997, 8, 567, 688, 695, 315, 453, 378] \n", "11 [997, 8, 567, 688, 695, 315, 1057] \n", "12 [997, 8, 567, 688, 695, 315, 677] \n", "13 [997, 8, 567, 688, 695, 315, 247, 1011] \n", "14 [997, 8, 567, 688, 695, 315, 247, 1002] \n", "15 [997, 8, 567, 688, 695, 315, 247, 1027] \n", "16 [997, 8, 567, 688, 695, 315, 247, 1018] \n", "17 [997, 8, 567, 688, 695, 315, 669, 402] \n", "18 [997, 8, 567, 688, 695, 315, 669, 394] \n", "19 [997, 8, 567, 688, 695, 315, 669, 409] \n", "20 [997, 8, 567, 688, 695, 315, 669, 385] \n", "21 [997, 8, 567, 688, 695, 315, 669, 425] \n", "22 [997, 8, 567, 688, 695, 315, 669, 533] \n", "23 [997, 8, 567, 688, 695, 315, 669, 312782574] \n", "24 [997, 8, 567, 688, 695, 315, 669, 312782628] \n", "25 [997, 8, 567, 688, 695, 315, 31, 39] \n", "26 [997, 8, 567, 688, 695, 315, 31, 48] \n", "27 [997, 8, 567, 688, 695, 315, 972] \n", "28 [997, 8, 567, 688, 695, 315, 44] \n", "29 [997, 8, 567, 688, 695, 315, 714, 723] \n", ".. ... \n", "286 [997, 8, 343, 1065, 354, 370, 1069] \n", "287 [997, 8, 343, 1065, 354, 370, 701, 209] \n", "288 [997, 8, 343, 1065, 354, 370, 701, 202] \n", "289 [997, 8, 343, 1065, 354, 370, 701, 225] \n", "290 [997, 8, 343, 1065, 354, 370, 701, 217] \n", "291 [997, 8, 343, 1065, 354, 370, 765] \n", "292 [997, 8, 343, 1065, 354, 370, 773] \n", "293 [997, 8, 343, 1065, 354, 370, 781] \n", "294 [997, 8, 343, 1065, 354, 379, 206] \n", "295 [997, 8, 343, 1065, 354, 379, 230] \n", "296 [997, 8, 343, 1065, 354, 379, 222] \n", "297 [997, 8, 512, 528, 645, 912] \n", "298 [997, 8, 512, 528, 645, 920] \n", "299 [997, 8, 512, 528, 645, 928] \n", "300 [997, 8, 512, 528, 645, 936] \n", "301 [997, 8, 512, 528, 645, 944] \n", "302 [997, 8, 512, 528, 645, 951] \n", "303 [997, 8, 512, 528, 645, 957] \n", "304 [997, 8, 512, 528, 645, 968] \n", "305 [997, 8, 512, 528, 1073, 1007] \n", "306 [997, 8, 512, 528, 1073, 1017] \n", "307 [997, 8, 512, 528, 1073, 1025] \n", "308 [997, 8, 512, 528, 1073, 1033] \n", "309 [997, 8, 512, 528, 1073, 1041] \n", "310 [997, 8, 512, 528, 1073, 1049] \n", "311 [997, 8, 512, 519, 989] \n", "312 [997, 8, 512, 519, 91] \n", "313 [997, 8, 512, 519, 846] \n", "314 [997, 8, 512, 519, 589508455] \n", "315 [997, 1009] \n", "\n", " structure_set_ids \n", "0 [3, 112905828, 688152357, 691663206, 687527945... \n", "1 [112905828, 688152357, 691663206, 687527945, 1... \n", "2 [112905828, 688152357, 691663206, 687527945, 1... \n", "3 [112905828, 688152357, 691663206, 687527945, 1... \n", "4 [112905828, 688152357, 691663206, 687527945, 1... \n", "5 [112905828, 688152357, 691663206, 687527945, 1... \n", "6 [112905828, 688152357, 691663206, 687527945, 1... \n", "7 [112905828, 688152357, 691663206, 687527945, 1... \n", "8 [112905828, 688152357, 691663206, 687527945, 1... \n", "9 [112905828, 688152357, 691663206, 687527945, 1... \n", "10 [112905828, 688152357, 691663206, 687527945, 1... \n", "11 [3, 112905828, 688152357, 691663206, 687527945... \n", "12 [3, 112905828, 688152357, 691663206, 687527945... \n", "13 [112905828, 688152357, 691663206, 687527945, 1... \n", "14 [112905828, 688152357, 691663206, 687527945, 1... \n", "15 [112905828, 688152357, 691663206, 687527945, 1... \n", "16 [112905828, 688152357, 691663206, 687527945, 1... \n", "17 [396673091, 112905828, 688152357, 691663206, 6... \n", "18 [396673091, 112905828, 688152357, 691663206, 6... \n", "19 [396673091, 112905828, 688152357, 691663206, 6... \n", "20 [396673091, 112905828, 688152357, 691663206, 6... \n", "21 [396673091, 112905828, 688152357, 691663206, 6... \n", "22 [396673091, 112905828, 688152357, 691663206, 6... \n", "23 [396673091, 112905828, 688152357, 691663206, 6... \n", "24 [396673091, 112905828, 688152357, 691663206, 6... \n", "25 [112905828, 688152357, 691663206, 687527945, 1... \n", "26 [112905828, 688152357, 691663206, 687527945, 1... \n", "27 [3, 112905828, 688152357, 691663206, 687527945... \n", "28 [3, 112905828, 688152357, 691663206, 687527945... \n", "29 [112905828, 688152357, 691663206, 687527945, 1... \n", ".. ... \n", "286 [112905828, 691663206, 687527945, 12, 68815236... \n", "287 [112905828, 691663206, 687527945, 12, 68815236... \n", "288 [112905828, 691663206, 687527945, 12, 68815236... \n", "289 [112905828, 691663206, 687527945, 12, 68815236... \n", "290 [112905828, 691663206, 687527945, 12, 68815236... \n", "291 [112905828, 691663206, 687527945, 12, 68815236... \n", "292 [112905828, 691663206, 687527945, 10, 12, 6881... \n", "293 [112905828, 691663206, 687527945, 12, 68815236... \n", "294 [112905828, 691663206, 687527945, 12, 68815236... \n", "295 [112905828, 691663206, 687527945, 12, 68815236... \n", "296 [112905828, 691663206, 687527945, 12, 68815236... \n", "297 [112905828, 691663206, 687527945, 12, 68815236... \n", "298 [112905828, 691663206, 687527945, 12, 68815236... \n", "299 [112905828, 691663206, 687527945, 12, 68815236... \n", "300 [112905828, 691663206, 687527945, 12, 68815236... \n", "301 [112905828, 691663206, 687527945, 12, 68815236... \n", "302 [112905828, 691663206, 687527945, 12, 68815236... \n", "303 [112905828, 691663206, 687527945, 12, 68815236... \n", "304 [112905828, 691663206, 687527945, 12, 68815236... \n", "305 [112905828, 691663206, 687527945, 12, 68815236... \n", "306 [112905828, 691663206, 687527945, 12, 68815236... \n", "307 [112905828, 691663206, 687527945, 12, 68815236... \n", "308 [112905828, 691663206, 687527945, 12, 68815236... \n", "309 [112905828, 691663206, 687527945, 12, 68815236... \n", "310 [112905828, 691663206, 687527945, 12, 68815236... \n", "311 [112905828, 691663206, 687527945, 12, 68815236... \n", "312 [112905828, 691663206, 687527945, 12, 68815236... \n", "313 [112905828, 691663206, 687527945, 12, 68815236... \n", "314 [112905828, 691663206, 688152368, 184527634, 1... \n", "315 [687527945, 184527634, 167587189, 691663206] \n", "\n", "[316 rows x 8 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# From the above table, \"Mouse Connectivity - Summary\" has id 167587189\n", "summary_structures = structure_tree.get_structures_by_set_id([167587189])\n", "pd.DataFrame(summary_structures)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is how you can filter experiments by transgenic line:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1211 cre cortical experiments\n", "105 Rbp4 cortical experiments\n" ] } ], "source": [ "# fetch the experiments that have injections in the isocortex of cre-positive mice\n", "isocortex = structure_tree.get_structures_by_name(['Isocortex'])[0]\n", "cre_cortical_experiments = mcc.get_experiments(cre=True, \n", " injection_structure_ids=[isocortex['id']])\n", "\n", "print(\"%d cre cortical experiments\" % len(cre_cortical_experiments))\n", "\n", "# same as before, but restrict the cre line\n", "rbp4_cortical_experiments = mcc.get_experiments(cre=[ 'Rbp4-Cre_KL100' ], \n", " injection_structure_ids=[isocortex['id']])\n", "\n", "\n", "print(\"%d Rbp4 cortical experiments\" % len(rbp4_cortical_experiments))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Structure Signal Unionization\n", "\n", "The ProjectionStructureUnionizes API data tells you how much signal there was in a given structure and experiment. It contains the density of projecting signal, volume of projecting signal, and other information. `MouseConnectivityCache` provides methods for querying and storing this data." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "33 VISp experiments\n", "29204 VISp non-injection, cortical structure unionizes\n" ] } ], "source": [ "# find wild-type injections into primary visual area\n", "visp = structure_tree.get_structures_by_acronym(['VISp'])[0]\n", "visp_experiments = mcc.get_experiments(cre=False, \n", " injection_structure_ids=[visp['id']])\n", "\n", "print(\"%d VISp experiments\" % len(visp_experiments))\n", "\n", "structure_unionizes = mcc.get_structure_unionizes([ e['id'] for e in visp_experiments ], \n", " is_injection=False,\n", " structure_ids=[isocortex['id']],\n", " include_descendants=True)\n", "\n", "print(\"%d VISp non-injection, cortical structure unionizes\" % len(structure_unionizes))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
hemisphere_ididis_injectionmax_voxel_densitymax_voxel_xmax_voxel_ymax_voxel_znormalized_projection_volumeprojection_densityprojection_energyprojection_intensityprojection_volumeexperiment_idstructure_idsum_pixel_intensitysum_pixelssum_projection_pixel_intensitysum_projection_pixelsvolume
03636132051False0.135948570068074900.0000450.0000900.011458126.7264300.00003030729714110305.779040e+10269614784.03.089182e+062.437678e+040.330278
12636125427False0.2391974670109069500.0002180.0001980.057760292.1183470.0001453072971413201.609707e+11598041920.03.454268e+071.182489e+050.732601
21636130167False1.0000009210199027900.0280520.205331319.5335391556.1887210.0186163072971413054.455195e+1074012400.02.364944e+101.519703e+070.090665
31636130887False0.0000000000.0000000.0000000.0000000.0000000.00000030729714126.701981e+0951414300.00.000000e+000.000000e+000.062983
41636128375False0.0000000000.0000000.0000000.0000000.0000000.00000030729714110102.252017e+1081598496.00.000000e+000.000000e+000.099958
\n", "
" ], "text/plain": [ " hemisphere_id id is_injection max_voxel_density max_voxel_x \\\n", "0 3 636132051 False 0.135948 5700 \n", "1 2 636125427 False 0.239197 4670 \n", "2 1 636130167 False 1.000000 9210 \n", "3 1 636130887 False 0.000000 0 \n", "4 1 636128375 False 0.000000 0 \n", "\n", " max_voxel_y max_voxel_z normalized_projection_volume projection_density \\\n", "0 680 7490 0.000045 0.000090 \n", "1 1090 6950 0.000218 0.000198 \n", "2 1990 2790 0.028052 0.205331 \n", "3 0 0 0.000000 0.000000 \n", "4 0 0 0.000000 0.000000 \n", "\n", " projection_energy projection_intensity projection_volume experiment_id \\\n", "0 0.011458 126.726430 0.000030 307297141 \n", "1 0.057760 292.118347 0.000145 307297141 \n", "2 319.533539 1556.188721 0.018616 307297141 \n", "3 0.000000 0.000000 0.000000 307297141 \n", "4 0.000000 0.000000 0.000000 307297141 \n", "\n", " structure_id sum_pixel_intensity sum_pixels \\\n", "0 1030 5.779040e+10 269614784.0 \n", "1 320 1.609707e+11 598041920.0 \n", "2 305 4.455195e+10 74012400.0 \n", "3 2 6.701981e+09 51414300.0 \n", "4 1010 2.252017e+10 81598496.0 \n", "\n", " sum_projection_pixel_intensity sum_projection_pixels volume \n", "0 3.089182e+06 2.437678e+04 0.330278 \n", "1 3.454268e+07 1.182489e+05 0.732601 \n", "2 2.364944e+10 1.519703e+07 0.090665 \n", "3 0.000000e+00 0.000000e+00 0.062983 \n", "4 0.000000e+00 0.000000e+00 0.099958 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "structure_unionizes.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a rather large table, even for a relatively small number of experiments. You can filter it down to a smaller list of structures like this." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "18 large, dense, cortical, non-injection unionizes, 18 structures\n", "0 Lateral visual area\n", "1 Rostrolateral visual area\n", "2 Postrhinal area\n", "3 Visual areas\n", "4 Lateral visual area\n", "5 Primary visual area, layer 6a\n", "6 Lateral visual area\n", "7 Primary visual area\n", "8 Lateral visual area\n", "9 Lateral visual area\n", "10 Lateral visual area\n", "11 Lateral visual area\n", "12 Lateral visual area\n", "13 Postrhinal area\n", "14 Lateral visual area\n", "15 Primary visual area, layer 1\n", "16 Primary visual area\n", "17 Rostrolateral visual area\n", "Name: name, dtype: object\n" ] }, { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
hemisphere_ididis_injectionmax_voxel_densitymax_voxel_xmax_voxel_ymax_voxel_znormalized_projection_volumeprojection_densityprojection_energyprojection_intensityprojection_volumeexperiment_idstructure_idsum_pixel_intensitysum_pixelssum_projection_pixel_intensitysum_projection_pixelsvolume
2292636125869False1.09650182092200.5820580.5985481902.6730723178.8121920.3862753072971414091.172856e+125.268182e+081.002363e+123.153262e+080.645352
15382633272766False1.07830150083400.8364580.6278881960.4083773122.2237850.3411441138871624179.620248e+114.435262e+088.694925e+112.784850e+080.543320
18412630240820False1.09310318096900.6266140.7444794059.9214305453.3733450.5100671802964243127826282.350927e+125.592923e+082.270683e+124.163813e+080.685133
23062630239708False1.09170230096904.2411600.5221392128.8693924077.2060613.4523291802964246691.279820e+135.397464e+091.149050e+132.818228e+096.611893
24763630241279False1.09440235024400.7122320.5326262363.3976544437.2538590.5797611802964244092.262464e+128.885676e+082.100039e+124.732744e+081.088495
25482630239270False1.09200174083900.4112290.6361542327.4543463658.6325680.334743180296424331.103850e+124.295492e+089.997563e+112.732596e+080.526198
36682636166751False1.09590205095300.6192440.8175094431.4480655420.6746060.4987233073216744092.314397e+124.980019e+082.206870e+124.071209e+080.610052
58562636151215False1.09500126084601.3795410.5081112117.8363364168.0604261.4602123075586463855.716203e+122.345965e+094.968369e+121.192010e+092.873807
60542636151229False1.08720152092700.3750170.5938512496.2825404203.5498900.3969473075586464091.524682e+125.456559e+081.362111e+123.240383e+080.668428
69312636132495False1.09330163094200.6580850.5009011653.5095113301.0708310.3384213073209604091.054259e+125.515297e+089.119596e+112.762617e+080.675624
73333636150146False1.08780198024801.0593700.5065982611.7041695155.3788580.6024293077432534092.724830e+129.707481e+082.535307e+124.917789e+081.189166
78082636148419False1.09360173094300.8725330.9661955588.1823545783.7032590.4961813077432534092.355657e+124.192173e+082.342663e+124.050454e+080.513541
150812636088739False1.09090144092900.9256160.6693051829.8516432733.9583750.4521823071379804091.121455e+125.515096e+081.009181e+123.691281e+080.675599
174432636156255False1.09210268093900.3999480.6127324104.7800686699.1475690.4331553090044923127826282.587937e+125.770818e+082.368794e+123.535963e+080.706925
204482634237507False1.09440169092300.6054850.7310854958.1948046781.9654200.4796643072964334092.804687e+125.355905e+082.655562e+123.915623e+080.656098
221542636098918False1.0968068075700.6796370.5114932051.5808114010.9682620.3524123075937475931.278747e+125.624387e+081.153888e+122.876833e+080.688987
224712636098800False1.09720110082803.3771590.5161291825.2024433536.3277151.7511593075937473855.794928e+122.769688e+095.055242e+121.429517e+093.392868
276752634218630False1.08250177087700.3035680.5002341285.3477462569.4954120.2758803093727164176.793426e+114.502058e+085.786710e+112.252080e+080.551502
\n", "
" ], "text/plain": [ " hemisphere_id id is_injection max_voxel_density max_voxel_x \\\n", "229 2 636125869 False 1.0 9650 \n", "1538 2 633272766 False 1.0 7830 \n", "1841 2 630240820 False 1.0 9310 \n", "2306 2 630239708 False 1.0 9170 \n", "2476 3 630241279 False 1.0 9440 \n", "2548 2 630239270 False 1.0 9200 \n", "3668 2 636166751 False 1.0 9590 \n", "5856 2 636151215 False 1.0 9500 \n", "6054 2 636151229 False 1.0 8720 \n", "6931 2 636132495 False 1.0 9330 \n", "7333 3 636150146 False 1.0 8780 \n", "7808 2 636148419 False 1.0 9360 \n", "15081 2 636088739 False 1.0 9090 \n", "17443 2 636156255 False 1.0 9210 \n", "20448 2 634237507 False 1.0 9440 \n", "22154 2 636098918 False 1.0 9680 \n", "22471 2 636098800 False 1.0 9720 \n", "27675 2 634218630 False 1.0 8250 \n", "\n", " max_voxel_y max_voxel_z normalized_projection_volume \\\n", "229 1820 9220 0.582058 \n", "1538 1500 8340 0.836458 \n", "1841 3180 9690 0.626614 \n", "2306 2300 9690 4.241160 \n", "2476 2350 2440 0.712232 \n", "2548 1740 8390 0.411229 \n", "3668 2050 9530 0.619244 \n", "5856 1260 8460 1.379541 \n", "6054 1520 9270 0.375017 \n", "6931 1630 9420 0.658085 \n", "7333 1980 2480 1.059370 \n", "7808 1730 9430 0.872533 \n", "15081 1440 9290 0.925616 \n", "17443 2680 9390 0.399948 \n", "20448 1690 9230 0.605485 \n", "22154 680 7570 0.679637 \n", "22471 1100 8280 3.377159 \n", "27675 1770 8770 0.303568 \n", "\n", " projection_density projection_energy projection_intensity \\\n", "229 0.598548 1902.673072 3178.812192 \n", "1538 0.627888 1960.408377 3122.223785 \n", "1841 0.744479 4059.921430 5453.373345 \n", "2306 0.522139 2128.869392 4077.206061 \n", "2476 0.532626 2363.397654 4437.253859 \n", "2548 0.636154 2327.454346 3658.632568 \n", "3668 0.817509 4431.448065 5420.674606 \n", "5856 0.508111 2117.836336 4168.060426 \n", "6054 0.593851 2496.282540 4203.549890 \n", "6931 0.500901 1653.509511 3301.070831 \n", "7333 0.506598 2611.704169 5155.378858 \n", "7808 0.966195 5588.182354 5783.703259 \n", "15081 0.669305 1829.851643 2733.958375 \n", "17443 0.612732 4104.780068 6699.147569 \n", "20448 0.731085 4958.194804 6781.965420 \n", "22154 0.511493 2051.580811 4010.968262 \n", "22471 0.516129 1825.202443 3536.327715 \n", "27675 0.500234 1285.347746 2569.495412 \n", "\n", " projection_volume experiment_id structure_id sum_pixel_intensity \\\n", "229 0.386275 307297141 409 1.172856e+12 \n", "1538 0.341144 113887162 417 9.620248e+11 \n", "1841 0.510067 180296424 312782628 2.350927e+12 \n", "2306 3.452329 180296424 669 1.279820e+13 \n", "2476 0.579761 180296424 409 2.262464e+12 \n", "2548 0.334743 180296424 33 1.103850e+12 \n", "3668 0.498723 307321674 409 2.314397e+12 \n", "5856 1.460212 307558646 385 5.716203e+12 \n", "6054 0.396947 307558646 409 1.524682e+12 \n", "6931 0.338421 307320960 409 1.054259e+12 \n", "7333 0.602429 307743253 409 2.724830e+12 \n", "7808 0.496181 307743253 409 2.355657e+12 \n", "15081 0.452182 307137980 409 1.121455e+12 \n", "17443 0.433155 309004492 312782628 2.587937e+12 \n", "20448 0.479664 307296433 409 2.804687e+12 \n", "22154 0.352412 307593747 593 1.278747e+12 \n", "22471 1.751159 307593747 385 5.794928e+12 \n", "27675 0.275880 309372716 417 6.793426e+11 \n", "\n", " sum_pixels sum_projection_pixel_intensity sum_projection_pixels \\\n", "229 5.268182e+08 1.002363e+12 3.153262e+08 \n", "1538 4.435262e+08 8.694925e+11 2.784850e+08 \n", "1841 5.592923e+08 2.270683e+12 4.163813e+08 \n", "2306 5.397464e+09 1.149050e+13 2.818228e+09 \n", "2476 8.885676e+08 2.100039e+12 4.732744e+08 \n", "2548 4.295492e+08 9.997563e+11 2.732596e+08 \n", "3668 4.980019e+08 2.206870e+12 4.071209e+08 \n", "5856 2.345965e+09 4.968369e+12 1.192010e+09 \n", "6054 5.456559e+08 1.362111e+12 3.240383e+08 \n", "6931 5.515297e+08 9.119596e+11 2.762617e+08 \n", "7333 9.707481e+08 2.535307e+12 4.917789e+08 \n", "7808 4.192173e+08 2.342663e+12 4.050454e+08 \n", "15081 5.515096e+08 1.009181e+12 3.691281e+08 \n", "17443 5.770818e+08 2.368794e+12 3.535963e+08 \n", "20448 5.355905e+08 2.655562e+12 3.915623e+08 \n", "22154 5.624387e+08 1.153888e+12 2.876833e+08 \n", "22471 2.769688e+09 5.055242e+12 1.429517e+09 \n", "27675 4.502058e+08 5.786710e+11 2.252080e+08 \n", "\n", " volume \n", "229 0.645352 \n", "1538 0.543320 \n", "1841 0.685133 \n", "2306 6.611893 \n", "2476 1.088495 \n", "2548 0.526198 \n", "3668 0.610052 \n", "5856 2.873807 \n", "6054 0.668428 \n", "6931 0.675624 \n", "7333 1.189166 \n", "7808 0.513541 \n", "15081 0.675599 \n", "17443 0.706925 \n", "20448 0.656098 \n", "22154 0.688987 \n", "22471 3.392868 \n", "27675 0.551502 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dense_unionizes = structure_unionizes[ structure_unionizes.projection_density > .5 ]\n", "large_unionizes = dense_unionizes[ dense_unionizes.volume > .5 ]\n", "large_structures = pd.DataFrame(structure_tree.nodes(large_unionizes.structure_id))\n", "\n", "print(\"%d large, dense, cortical, non-injection unionizes, %d structures\" % ( len(large_unionizes), len(large_structures) ))\n", "\n", "print(large_structures.name)\n", "\n", "large_unionizes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generating a Projection Matrix\n", "The `MouseConnectivityCache` class provides a helper method for converting ProjectionStructureUnionize records for a set of experiments and structures into a matrix. This code snippet demonstrates how to make a matrix of projection density values in auditory sub-structures for cre-negative VISp experiments. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5cAAANSCAYAAADxsDriAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiSpM5NLSZIkSVJnJpeSJEmSpM7+fy7FHfgbfX3uAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "%matplotlib inline\n", "\n", "visp_experiment_ids = [ e['id'] for e in visp_experiments ]\n", "ctx_children = structure_tree.child_ids( [isocortex['id']] )[0]\n", "\n", "pm = mcc.get_projection_matrix(experiment_ids = visp_experiment_ids, \n", " projection_structure_ids = ctx_children,\n", " hemisphere_ids= [2], # right hemisphere, ipsilateral\n", " parameter = 'projection_density')\n", "\n", "row_labels = pm['rows'] # these are just experiment ids\n", "column_labels = [ c['label'] for c in pm['columns'] ] \n", "matrix = pm['matrix']\n", "\n", "fig, ax = plt.subplots(figsize=(15,15))\n", "heatmap = ax.pcolor(matrix, cmap=plt.cm.afmhot)\n", "\n", "# put the major ticks at the middle of each cell\n", "ax.set_xticks(np.arange(matrix.shape[1])+0.5, minor=False)\n", "ax.set_yticks(np.arange(matrix.shape[0])+0.5, minor=False)\n", "\n", "ax.set_xlim([0, matrix.shape[1]])\n", "ax.set_ylim([0, matrix.shape[0]]) \n", "\n", "# want a more natural, table-like display\n", "ax.invert_yaxis()\n", "ax.xaxis.tick_top()\n", "\n", "ax.set_xticklabels(column_labels, minor=False)\n", "ax.set_yticklabels(row_labels, minor=False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Manipulating Grid Data\n", "\n", "The `MouseConnectivityCache` class also helps you download and open every experiment's projection grid data volume. By default it will download 25um volumes, but you could also download data at other resolutions if you prefer (10um, 50um, 100um).\n", "\n", "This demonstrates how you can load the projection density for a particular experiment. It also shows how to download the template volume to which all grid data is registered. Voxels in that template have been structurally annotated by neuroanatomists and the resulting labels stored in a separate annotation volume image." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# we'll take this experiment - an injection into the primary somatosensory - as an example\n", "experiment_id = 181599674" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'keyvaluepairs': {}, 'type': 'double', 'dimension': 3, 'space': 'left-posterior-superior', 'sizes': [528, 320, 456], 'space directions': [['25', '0', '0'], ['0', '25', '0'], ['0', '0', '25']], 'kinds': ['domain', 'domain', 'domain'], 'endian': 'little', 'encoding': 'gzip', 'space origin': ['0', '0', '0']}\n", "(528, 320, 456) (528, 320, 456) (528, 320, 456)\n" ] } ], "source": [ "# projection density: number of projecting pixels / voxel volume\n", "pd, pd_info = mcc.get_projection_density(experiment_id)\n", "\n", "# injection density: number of projecting pixels in injection site / voxel volume\n", "ind, ind_info = mcc.get_injection_density(experiment_id)\n", "\n", "# injection fraction: number of pixels in injection site / voxel volume\n", "inf, inf_info = mcc.get_injection_fraction(experiment_id)\n", "\n", "# data mask:\n", "# binary mask indicating which voxels contain valid data\n", "dm, dm_info = mcc.get_data_mask(experiment_id)\n", "\n", "template, template_info = mcc.get_template_volume()\n", "annot, annot_info = mcc.get_annotation_volume()\n", "\n", "# in addition to the annotation volume, you can get binary masks for individual structures\n", "# in this case, we'll get one for the isocortex\n", "cortex_mask, cm_info = mcc.get_structure_mask(315)\n", "\n", "print(pd_info)\n", "print(pd.shape, template.shape, annot.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you have these loaded, you can use matplotlib see what they look like." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3AAAADcCAYAAAAx3EPfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# compute the maximum intensity projection (along the anterior-posterior axis) of the projection data\n", "pd_mip = pd.max(axis=0)\n", "ind_mip = ind.max(axis=0)\n", "inf_mip = inf.max(axis=0)\n", "\n", "# show that slice of all volumes side-by-side\n", "f, pr_axes = plt.subplots(1, 3, figsize=(15, 6))\n", "\n", "pr_axes[0].imshow(pd_mip, cmap='hot', aspect='equal')\n", "pr_axes[0].set_title(\"projection density MaxIP\")\n", "\n", "pr_axes[1].imshow(ind_mip, cmap='hot', aspect='equal')\n", "pr_axes[1].set_title(\"injection density MaxIP\")\n", "\n", "pr_axes[2].imshow(inf_mip, cmap='hot', aspect='equal')\n", "pr_axes[2].set_title(\"injection fraction MaxIP\")\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3AAAADcCAYAAAAx3EPfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Look at a slice from the average template and annotation volumes\n", "\n", "# pick a slice to show\n", "slice_idx = 264\n", "\n", "f, ccf_axes = plt.subplots(1, 3, figsize=(15, 6))\n", "\n", "ccf_axes[0].imshow(template[slice_idx,:,:], cmap='gray', aspect='equal', vmin=template.min(), vmax=template.max())\n", "ccf_axes[0].set_title(\"registration template\")\n", "\n", "ccf_axes[1].imshow(annot[slice_idx,:,:], cmap='gray', aspect='equal', vmin=0, vmax=2000)\n", "ccf_axes[1].set_title(\"annotation volume\")\n", "\n", "ccf_axes[2].imshow(cortex_mask[slice_idx,:,:], cmap='gray', aspect='equal', vmin=0, vmax=1)\n", "ccf_axes[2].set_title(\"isocortex mask\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On occasion the TissueCyte microscope fails to acquire a tile. In this case the data from that tile should not be used for analysis. The data mask associated with each experiment can be used to determine which portions of the grid data came from correctly acquired tiles.\n", "\n", "In this experiment, a missed tile can be seen in the data mask as a dark warped square. The values in the mask exist within [0, 1], describing the fraction of each voxel that was correctly acquired" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUIAAADzCAYAAADzTB/4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, data_mask_axis = plt.subplots(figsize=(5, 6))\n", "\n", "data_mask_axis.imshow(dm[81, :, :], cmap='hot', aspect='equal', vmin=0, vmax=1)\n", "data_mask_axis.set_title('data mask')\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 1 }