{ "cells": [ { "cell_type": "markdown", "id": "cba28528-09b7-4b84-8775-1e1e7aeb8ac5", "metadata": {}, "source": [ "# Supplemental Information" ] }, { "cell_type": "markdown", "id": "e6c9a93f-4f97-415f-8190-4e74d2ae297a", "metadata": {}, "source": [ "The following analyses can be found in the Supplementary Info section of our paper. They complement and extend the analyses presented in the main paper. " ] }, { "cell_type": "code", "execution_count": 1, "id": "42e1d194-697d-4180-afeb-78cfcdf44c86", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0.1.0'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from neuprint import Client\n", "c = Client('neuprint.janelia.org', dataset='hemibrain:v1.2.1', token='') \n", "# insert personal token above. see https://connectome-neuprint.github.io/neuprint-python/docs/quickstart.html#client-and-authorization-token for instructions\n", "c.fetch_version()" ] }, { "cell_type": "markdown", "id": "72d792f6-243a-44bb-b65c-14511cb2972b", "metadata": {}, "source": [ "# Full tables of presynaptic and postsynaptic connectivity of clock neurons" ] }, { "cell_type": "code", "execution_count": 2, "id": "a0c2e045-0002-4b0d-8fe4-76de50bc77bd", "metadata": {}, "outputs": [], "source": [ "from neuron_criteria import getClock\n", "from connection_utils import clock_neuron_connections\n", "\n", "clock_df = getClock(l_lnv=True)" ] }, { "cell_type": "markdown", "id": "0e0af8cd-2eae-4986-a551-8e1bbde25b90", "metadata": {}, "source": [ "We obtain a dataframe of all **postsynaptic** connections made by clock neurons. The dataframe is pivoted to show all postsynaptic neurons along the rows and all presynaptic clock neurons along the columns. The elements indicate the weight of the connection." ] }, { "cell_type": "code", "execution_count": 3, "id": "06198c07-923f-447b-b566-5210807a87dd", "metadata": {}, "outputs": [], "source": [ "# get connections from clock neurons to anything else\n", "from neuprint import fetch_adjacencies\n", "\n", "neuron_df_clock, clock_post_conns = fetch_adjacencies(clock_df['bodyId'], None, min_total_weight=1)\n", "clock_post_conns = clock_post_conns.groupby(['bodyId_pre', 'bodyId_post'], as_index=False)['weight'].sum() " ] }, { "cell_type": "code", "execution_count": 4, "id": "30af5921-aed2-4dff-8411-d1fde6d305d4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "bodyId_pre 264083994 296544364 324846570 325529237 356818551 \\\n", "bodyId_post \n", "203253253 NaN NaN NaN NaN NaN \n", "203598647 NaN NaN NaN NaN NaN \n", "233105330 NaN 1.0 NaN NaN NaN \n", "234630133 NaN NaN NaN NaN NaN \n", "234965803 NaN NaN NaN 1.0 NaN \n", "... ... ... ... ... ... \n", "7112617046 NaN NaN NaN NaN NaN \n", "7112622017 NaN NaN 1.0 NaN NaN \n", "7112622236 NaN NaN NaN 1.0 NaN \n", "7112625263 NaN NaN NaN NaN NaN \n", "7112625363 NaN NaN NaN NaN NaN \n", "\n", "bodyId_pre 386834269 387166379 387944118 448260940 450034902 ... \\\n", "bodyId_post ... \n", "203253253 NaN NaN NaN NaN NaN ... \n", "203598647 NaN NaN NaN NaN NaN ... \n", "233105330 2.0 NaN NaN NaN NaN ... \n", "234630133 NaN NaN NaN NaN NaN ... \n", "234965803 NaN 2.0 NaN NaN NaN ... \n", "... ... ... ... ... ... ... \n", "7112617046 NaN 1.0 NaN NaN NaN ... \n", "7112622017 NaN 2.0 NaN NaN NaN ... \n", "7112622236 NaN NaN NaN NaN NaN ... \n", "7112625263 NaN NaN NaN NaN NaN ... \n", "7112625363 NaN NaN NaN NaN NaN ... \n", "\n", "bodyId_pre 2068801704 5813001741 5813010153 5813021192 5813022274 \\\n", "bodyId_post \n", "203253253 NaN NaN NaN 1.0 NaN \n", "203598647 NaN NaN NaN NaN NaN \n", "233105330 NaN NaN NaN NaN 1.0 \n", "234630133 NaN NaN NaN 3.0 NaN \n", "234965803 NaN NaN NaN NaN NaN \n", "... ... ... ... ... ... \n", "7112617046 NaN NaN NaN NaN NaN \n", "7112622017 NaN NaN 1.0 NaN NaN \n", "7112622236 NaN NaN NaN NaN NaN \n", "7112625263 NaN NaN NaN NaN NaN \n", "7112625363 NaN NaN NaN NaN NaN \n", "\n", "bodyId_pre 5813026773 5813056917 5813064789 5813069648 5813071319 \n", "bodyId_post \n", "203253253 NaN NaN NaN NaN NaN \n", "203598647 NaN NaN NaN 1.0 NaN \n", "233105330 NaN NaN NaN NaN NaN \n", "234630133 NaN 10.0 NaN NaN NaN \n", "234965803 NaN NaN NaN NaN NaN \n", "... ... ... ... ... ... \n", "7112617046 NaN NaN NaN NaN NaN \n", "7112622017 NaN NaN NaN 1.0 NaN \n", "7112622236 NaN NaN NaN NaN NaN \n", "7112625263 NaN NaN NaN NaN NaN \n", "7112625363 NaN NaN NaN NaN NaN \n", "\n", "[2899 rows x 28 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# pivot table to show all postsynaptic cells as rows and all presynaptic clock cells as columns with weights as table entries\n", "clock_post_conns = clock_post_conns.pivot(index='bodyId_post', columns='bodyId_pre', values='weight')\n", "clock_post_conns" ] }, { "cell_type": "code", "execution_count": 5, "id": "221df540-d7e9-4bb2-ae03-bf565361a457", "metadata": {}, "outputs": [], "source": [ "# export to csv\n", "clock_post_conns.to_csv('clock_post_connections_pivot.csv')" ] }, { "cell_type": "markdown", "id": "99f1dfb9-ef26-42de-ab9d-02fa6e6f0079", "metadata": {}, "source": [ "We obtain a dataframe of all **presynaptic** connections made by clock neurons. The dataframe is pivoted to show all presynaptic neurons along the rows and all postsynaptic clock neurons along the columns. The elements indicate the weight of the connection." ] }, { "cell_type": "code", "execution_count": 6, "id": "ba5dca6e-65ae-4e19-aac7-f9535c6aa087", "metadata": {}, "outputs": [], "source": [ "# get connections to clock neurons from anything else\n", "from neuprint import fetch_adjacencies\n", "\n", "neuron_df_clock, clock_pre_conns = fetch_adjacencies(None, clock_df['bodyId'], min_total_weight=1)\n", "clock_pre_conns = clock_pre_conns.groupby(['bodyId_pre', 'bodyId_post'], as_index=False)['weight'].sum() " ] }, { "cell_type": "code", "execution_count": 7, "id": "31bac10d-b3f2-4b4c-95f7-f340d21035e7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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2115 rows × 28 columns

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" ], "text/plain": [ "bodyId_post 264083994 296544364 324846570 325529237 356818551 \\\n", "bodyId_pre \n", "200326126 3.0 NaN NaN NaN NaN \n", "233105330 NaN NaN 1.0 NaN NaN \n", "263475451 NaN 2.0 NaN NaN 2.0 \n", "264083994 NaN 1.0 NaN NaN 3.0 \n", "264822904 NaN NaN NaN NaN NaN \n", "... ... ... ... ... ... \n", "7112616416 NaN NaN 1.0 NaN NaN \n", "7112616960 NaN NaN NaN NaN NaN \n", "7112617046 NaN NaN NaN NaN NaN \n", "7112622017 NaN NaN 1.0 NaN NaN \n", "7112626023 NaN NaN NaN NaN 1.0 \n", "\n", "bodyId_post 386834269 387166379 387944118 448260940 450034902 ... \\\n", "bodyId_pre ... \n", "200326126 NaN NaN NaN NaN NaN ... \n", "233105330 NaN NaN NaN NaN NaN ... \n", "263475451 NaN NaN NaN NaN NaN ... \n", "264083994 NaN NaN NaN NaN NaN ... \n", "264822904 2.0 NaN NaN NaN NaN ... \n", "... ... ... ... ... ... ... \n", "7112616416 NaN NaN 1.0 1.0 NaN ... \n", "7112616960 1.0 NaN NaN NaN NaN ... \n", "7112617046 NaN NaN NaN NaN NaN ... \n", "7112622017 NaN NaN NaN NaN NaN ... \n", "7112626023 NaN NaN NaN NaN NaN ... \n", "\n", "bodyId_post 2068801704 5813001741 5813010153 5813021192 5813022274 \\\n", "bodyId_pre \n", "200326126 NaN NaN NaN NaN 4.0 \n", "233105330 NaN NaN NaN 2.0 NaN \n", "263475451 NaN NaN NaN NaN NaN \n", "264083994 NaN NaN NaN NaN 10.0 \n", "264822904 NaN NaN NaN NaN NaN \n", "... ... ... ... ... ... \n", "7112616416 NaN NaN NaN NaN NaN \n", "7112616960 NaN NaN NaN NaN 1.0 \n", "7112617046 NaN NaN NaN NaN NaN \n", "7112622017 NaN NaN NaN NaN NaN \n", "7112626023 NaN NaN NaN NaN NaN \n", "\n", "bodyId_post 5813026773 5813056917 5813064789 5813069648 5813071319 \n", "bodyId_pre \n", "200326126 NaN NaN NaN NaN NaN \n", "233105330 NaN NaN 1.0 NaN NaN \n", "263475451 NaN NaN 1.0 NaN 1.0 \n", "264083994 NaN NaN NaN 81.0 1.0 \n", "264822904 NaN NaN NaN NaN NaN \n", "... ... ... ... ... ... \n", "7112616416 NaN NaN NaN NaN NaN \n", "7112616960 NaN 1.0 NaN 4.0 NaN \n", "7112617046 NaN NaN NaN 3.0 NaN \n", "7112622017 NaN NaN NaN NaN NaN \n", "7112626023 NaN NaN NaN NaN NaN \n", "\n", "[2115 rows x 28 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# pivot table to show all presynaptic cells as rows and all postsynaptic clock cells as columns with weights as table entries\n", "clock_pre_conns = clock_pre_conns.pivot(index='bodyId_pre', columns='bodyId_post', values='weight')\n", "clock_pre_conns" ] }, { "cell_type": "code", "execution_count": 8, "id": "f75a33b5-1be0-4566-851d-0450125e8c61", "metadata": {}, "outputs": [], "source": [ "# export to csv\n", "clock_pre_conns.to_csv('clock_pre_connections_pivot.csv')" ] }, { "cell_type": "markdown", "id": "7c27b0b0-c1e7-41fb-9da4-e660091a4620", "metadata": {}, "source": [ "# SuperVenn diagrams for LNs" ] }, { "cell_type": "markdown", "id": "ede3f698-0706-44d3-88b2-66cd1957c12f", "metadata": {}, "source": [ "To determine whether the 5th sLNv had more connectivity in common with the other sLNvs or the LNds, we produced SuperVenn diagrams. These horizontally display layout synaptic partners and use overlap of color blocks along the vertical axis to indicate shared partners." ] }, { "cell_type": "code", "execution_count": 13, "id": "ba67df4e-1fdc-4011-917e-57e841da0e21", "metadata": {}, "outputs": [], "source": [ "from neuron_criteria import bodyIds_by_type\n", "\n", "ids_by_type = bodyIds_by_type(clock_df)" ] }, { "cell_type": "markdown", "id": "2083e6ef", "metadata": {}, "source": [ "First we look at its similarity with s-LNv inputs and outputs as compared to just the s-LNvs by themselves." ] }, { "cell_type": "code", "execution_count": 3, "id": "241a1148", "metadata": {}, "outputs": [], "source": [ "conn_df_out = clock_neuron_connections(clock_df, \"out\", min_weight = 3)" ] }, { "cell_type": "code", "execution_count": 14, "id": "2ddf5f91", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from visualization_utils import supervenn_comps\n", "\n", "morn_5th_ids = ids_by_type['s-LNv'] + ids_by_type['5th s-LNv']\n", "supervenn_comps(conn_df_out, clock_df, morn_5th_ids, \"out\", \"5th with s-LNv\", weighted = False, annot_width = 5)" ] }, { "cell_type": "code", "execution_count": 12, "id": "faf0d3b4", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "slnv_ids = ids_by_type['s-LNv']\n", "\n", "supervenn_comps(conn_df_out, clock_df, slnv_ids, \"out\", \"s-LNv\", weighted = False)" ] }, { "cell_type": "code", "execution_count": 17, "id": "e7342a6d", "metadata": {}, "outputs": [], "source": [ "conn_df_in = clock_neuron_connections(clock_df, \"in\", min_weight = 3)" ] }, { "cell_type": "code", "execution_count": 14, "id": "0d71456d", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "supervenn_comps(conn_df_in, clock_df, morn_5th_ids, \"in\", \"5th with s-LNv\", weighted = False, annot_width = 2)" ] }, { "cell_type": "code", "execution_count": 15, "id": "2e1d442a", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "supervenn_comps(conn_df_in, clock_df, slnv_ids, \"in\", \"s-LNv\", weighted = False)" ] }, { "cell_type": "markdown", "id": "0038f90a", "metadata": {}, "source": [ "Then we look at the 5th with LNds instead," ] }, { "cell_type": "code", "execution_count": 15, "id": "c1ea050f", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "evening_ids = clock_df.loc[clock_df['phase']=='evening', 'bodyId'].reset_index()['bodyId']\n", "supervenn_comps(conn_df_out, clock_df, evening_ids, \"out\", \"5th with LNd\", weighted = False, annot_width = 20)" ] }, { "cell_type": "code", "execution_count": 18, "id": "6694deec", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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