diff --git a/exc_cell_splitting/README b/exc_cell_splitting/README new file mode 100644 index 0000000..abe8c42 --- /dev/null +++ b/exc_cell_splitting/README @@ -0,0 +1,4 @@ +This directory contains code to split SSCX excitatory types into subtypes. + +Author: Daniel Keller +Date: October 23, 2022 \ No newline at end of file diff --git a/exc_cell_splitting/hierarchy.json b/exc_cell_splitting/hierarchy.json new file mode 100644 index 0000000..c50cf42 --- /dev/null +++ b/exc_cell_splitting/hierarchy.json @@ -0,0 +1,18982 @@ +{ + "success": true, + "id": 0, + "start_row": 0, + "num_rows": 1, + "total_rows": 1, + "msg": [ + { + "id": 997, + "atlas_id": -1, + "ontology_id": 1, + "acronym": "root", + "name": "root", + "color_hex_triplet": "FFFFFF", + "graph_order": 0, + "st_level": 0, + "hemisphere_id": 3, + "parent_structure_id": null, + "children": [ + { + "id": 8, + "atlas_id": 0, + "ontology_id": 1, + "acronym": "grey", + "name": "Basic cell groups and regions", + "color_hex_triplet": 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"children": [ + { + "id": 1087, + "atlas_id": 701, + "ontology_id": 1, + "acronym": "pce", + "name": "precentral fissure", + "color_hex_triplet": "AAAAAA", + "graph_order": 1311, + "st_level": 8, + "hemisphere_id": 3, + "parent_structure_id": 1040, + "children": [] + }, + { + "id": 1095, + "atlas_id": 702, + "ontology_id": 1, + "acronym": "pcf", + "name": "preculminate fissure", + "color_hex_triplet": "AAAAAA", + "graph_order": 1312, + "st_level": 8, + "hemisphere_id": 3, + "parent_structure_id": 1040, + "children": [] + }, + { + "id": 1103, + "atlas_id": 703, + "ontology_id": 1, + "acronym": "pri", + "name": "primary fissure", + "color_hex_triplet": "AAAAAA", + "graph_order": 1313, + "st_level": 8, + "hemisphere_id": 3, + "parent_structure_id": 1040, + "children": [] + }, + { + "id": 1112, + "atlas_id": 704, + "ontology_id": 1, + "acronym": "psf", + "name": "posterior superior fissure", + "color_hex_triplet": "AAAAAA", + "graph_order": 1314, + "st_level": 8, + "hemisphere_id": 3, + "parent_structure_id": 1040, + "children": [] + }, + { + "id": 1119, + "atlas_id": 705, + "ontology_id": 1, + "acronym": "ppf", + "name": "prepyramidal fissure", + "color_hex_triplet": "AAAAAA", + "graph_order": 1315, + "st_level": 8, + "hemisphere_id": 3, + "parent_structure_id": 1040, + "children": [] + }, + { + "id": 3, + "atlas_id": 707, + "ontology_id": 1, + "acronym": "sec", + "name": "secondary fissure", + "color_hex_triplet": "AAAAAA", + "graph_order": 1316, + "st_level": 8, + "hemisphere_id": 3, + "parent_structure_id": 1040, + "children": [] + }, + { + "id": 11, + "atlas_id": 708, + "ontology_id": 1, + "acronym": "plf", + "name": "posterolateral fissure", + "color_hex_triplet": "AAAAAA", + "graph_order": 1317, + "st_level": 8, + "hemisphere_id": 3, + "parent_structure_id": 1040, + "children": [] + }, + { + "id": 18, + "atlas_id": 709, + "ontology_id": 1, + "acronym": "nf", + "name": "nodular fissure", + "color_hex_triplet": "AAAAAA", + "graph_order": 1318, + "st_level": 8, + "hemisphere_id": 3, + "parent_structure_id": 1040, + "children": [] + }, + { + "id": 25, + "atlas_id": 710, + "ontology_id": 1, + "acronym": "sif", + "name": "simple fissure", + "color_hex_triplet": "AAAAAA", + "graph_order": 1319, + "st_level": 8, + "hemisphere_id": 3, + "parent_structure_id": 1040, + "children": [] + }, + { + "id": 34, + "atlas_id": 711, + "ontology_id": 1, + "acronym": "icf", + "name": "intercrural fissure", + "color_hex_triplet": "AAAAAA", + "graph_order": 1320, + "st_level": 8, + "hemisphere_id": 3, + "parent_structure_id": 1040, + "children": [] + }, + { + "id": 43, + "atlas_id": 712, + "ontology_id": 1, + "acronym": "apf", + "name": "ansoparamedian fissure", + "color_hex_triplet": "AAAAAA", + "graph_order": 1321, + "st_level": 8, + "hemisphere_id": 3, + "parent_structure_id": 1040, + "children": [] + }, + { + "id": 49, + "atlas_id": 713, + "ontology_id": 1, + "acronym": "ipf", + "name": "intraparafloccular fissure", + "color_hex_triplet": "AAAAAA", + "graph_order": 1322, + "st_level": 8, + "hemisphere_id": 3, + "parent_structure_id": 1040, + "children": [] + }, + { + "id": 57, + "atlas_id": 714, + "ontology_id": 1, + "acronym": "pms", + "name": "paramedian sulcus", + "color_hex_triplet": "AAAAAA", + "graph_order": 1323, + "st_level": 8, + "hemisphere_id": 3, + "parent_structure_id": 1040, + "children": [] + }, + { + "id": 65, + "atlas_id": 715, + "ontology_id": 1, + "acronym": "pfs", + "name": "parafloccular sulcus", + "color_hex_triplet": "AAAAAA", + "graph_order": 1324, + "st_level": 8, + "hemisphere_id": 3, + "parent_structure_id": 1040, + "children": [] + } + ] + }, + { + "id": 624, + "atlas_id": 926, + "ontology_id": 1, + "acronym": "IPF", + "name": "Interpeduncular fossa", + "color_hex_triplet": "AAAAAA", + "graph_order": 1325, + "st_level": 7, + "hemisphere_id": 3, + "parent_structure_id": 1024, + "children": [] + } + ] + }, + { + "id": 304325711, + "atlas_id": null, + "ontology_id": 1, + "acronym": "retina", + "name": "retina", + "color_hex_triplet": "7F2E7E", + "graph_order": 1326, + "st_level": 1, + "hemisphere_id": 3, + "parent_structure_id": 997, + "children": [] + } + ] + } + ] +} \ No newline at end of file diff --git a/exc_cell_splitting/make_new_inhib_exc_outside_SSCX.py b/exc_cell_splitting/make_new_inhib_exc_outside_SSCX.py new file mode 100644 index 0000000..65b59be --- /dev/null +++ b/exc_cell_splitting/make_new_inhib_exc_outside_SSCX.py @@ -0,0 +1,109 @@ + +############################################################################### +# Copyright 2022 Blue Brain +# Author: Daniel Keller +# October 21, 2022 +# This program makes exc and inh densities with SSCX cut out +# It also remaps exc cells to morphological fractions in those regions +# using the m-type fractions in the cxv file. All etype exc types are the same. + +# inputs: annoation atlas and hirearchy +# total neuron and inhibitory nrrd files +# mapping csv file +# outputs: (in output directory) +# exc and inhib densities without SSCX +# ME type excitatory nrrd files +############################################################################### + +#packages +import os +import re +from voxcell import VoxelData +from voxcell.nexus.voxelbrain import Atlas +import numpy as np + +#load an nrrd +def find_regions(fn_cand, root, format="path"): + #for fn_cand in fn_regions: + if 1: + expected_fn_regions = os.path.join(root, fn_cand) + if os.path.isfile(expected_fn_regions): + if format == "path": + return expected_fn_regions + elif format == "voxcell": + return VoxelData.load_nrrd(expected_fn_regions) + raise ValueError("Unknown format spec: {0}".format(format)) + return None + + +#make excitatory density +neuron_nrrd= find_regions("neuron_density_v3.nrrd",'./', "voxcell") +inh_nrrd= find_regions("gad67+_density_v3.nrrd",'./', "voxcell") +exc_nrrd=neuron_nrrd +exc_nrrd.raw=neuron_nrrd.raw-inh_nrrd.raw +exc_nrrd.raw=exc_nrrd.raw*(exc_nrrd.raw>0) + +#Do remapping + +#read in mapping file +import csv +allrows=[] +with open('mapping_cortex_all_to_exc_mtypes.csv', newline='') as csvfile: + reader = csv.reader(csvfile, delimiter=',', quotechar='|') + for row in reader: + allrows.append(row) + #print(', '.join(row)) + + +#load atlas +atlas = Atlas.open('.') +brain_regions = atlas.load_data('brain_regions') + + +#make output directory +if (os.path.exists('./output')): + print('output directory already exists') +else: + os.mkdir('output') + + +output_brain_regions=atlas.load_data('brain_regions') +PC_morphology_names=allrows[0] + +#layer IDs, starting with layer 2 and going to layer 6 +allids=[{614454286,614454294,614454296,614454298,614454300,614454302,614454304,614454306},{614454287,614454289,614454291,614454293,614454295,614454297,614454299,614454301,614454303,614454305,614454307},{12995,865,654,1047,1094,950,577,1086,182305701,1035}, {12996,921,702,1070,1128,974,625,1111,182305705,1090}, {12997,12998,686,719,889,929,1038,1062,478,510,1102,2,945,1026,9,461,182305709,182305713,862,893}] + +#loop through the layers and write out ME fractions of PCs in those layers +for count1 in range(1,len(allrows)): + print(count1) + ids=allids[count1-1] + + row=allrows[count1] + region_inds=np.transpose(np.nonzero(np.isin(brain_regions.raw, list(ids)))) + for count2 in range(1,len(PC_morphology_names)): + if row[count2]=='0': + continue + scale=float(row[count2]) + output_name='./output/'+(PC_morphology_names[count2])+'_cADpyr.nrrd' + output_array=0*exc_nrrd.raw + allval=0 + for ind in region_inds: + val=exc_nrrd.raw[ind[0],ind[1],ind[2]] + output_array[ind[0],ind[1],ind[2]]=scale*val + allval+=val + + output_brain_regions.raw=output_array + output_brain_regions.save_nrrd(output_name) + +#now remove regions from excitatory +#append layer 1 +allids.append({12993,793,558,981,480149206,1030,878,450,1006,182305693,873 }) +for ids in allids: + region_inds=np.transpose(np.nonzero(np.isin(brain_regions.raw, list(ids)))) + for ind in region_inds: + exc_nrrd.raw[ind[0],ind[1],ind[2]]=0 + inh_nrrd.raw[ind[0],ind[1],ind[2]]=0 + +#save the output +exc_nrrd.save_nrrd('./output/Generic_Excitatory_Neuron_MType_Generic_Excitatory_Neuron_EType.nrrd') +inh_nrrd.save_nrrd('./output/Generic_Inhibitory_Neuron_MType_Generic_Inhibitory_Neuron_EType.nrrd') diff --git a/exc_cell_splitting/mapping_cortex_all_to_exc_mtypes.csv b/exc_cell_splitting/mapping_cortex_all_to_exc_mtypes.csv new file mode 100644 index 0000000..9d2bc44 --- /dev/null +++ b/exc_cell_splitting/mapping_cortex_all_to_exc_mtypes.csv @@ -0,0 +1,6 @@ +layer,L2_IPC,L2_TPC:A,L2_TPC:B,L3_TPC:A,L3_TPC:B,L4_SSC,L4_TPC,L4_UPC,L5_TPC:A,L5_TPC:B,L5_TPC:C,L5_UPC,L6_BPC,L6_HPC,L6_IPC,L6_TPC:A,L6_TPC:C,L6_UPC +L2,0.082612842,0.129525291,0.787861866,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +L3,0,0,0,0.776524986,0.223475014,0,0,0,0,0,0,0,0,0,0,0,0,0 +L4,0,0,0,0,0,0.090735874,0.641289425,0.267974701,0,0,0,0,0,0,0,0,0,0 +L5,0,0,0,0,0,0,0,0,0.478764389,0.373704348,0.069744099,0.077787164,0,0,0,0,0,0 +L6,0,0,0,0,0,0,0,0,0,0,0,0,0.181041554,0.165265851,0.226502638,0.194968725,0.129358437,0.102862796