diff --git a/notebooks/bte_with_pfocr_cooccurrence.ipynb b/notebooks/bte_with_pfocr_cooccurrence.ipynb
index 72a87ce..8f40ac6 100644
--- a/notebooks/bte_with_pfocr_cooccurrence.ipynb
+++ b/notebooks/bte_with_pfocr_cooccurrence.ipynb
@@ -5,7 +5,7 @@
"colab": {
"name": "bte_with_pfocr_cooccurrence.ipynb",
"provenance": [],
- "authorship_tag": "ABX9TyO6hfzuqfwBUdlOr8CUXLfk",
+ "authorship_tag": "ABX9TyMGBEO4WsI+qAnctC5k3yPk",
"include_colab_link": true
},
"kernelspec": {
@@ -29,46 +29,64 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 49,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UZZqPBS0UppM",
- "outputId": "4dfaac8c-6842-429c-d3b5-9c3b8fbea79c"
+ "outputId": "9996ad71-f8fb-4c1f-bc40-430ede6b5de3"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
+ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (1.21.6)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.3.5)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (2.23.0)\n",
"Requirement already satisfied: requests_cache in /usr/local/lib/python3.7/dist-packages (0.9.4)\n",
+ "Requirement already satisfied: SetSimilaritySearch in /usr/local/lib/python3.7/dist-packages (0.1.7)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.2)\n",
"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2022.1)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
- "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests) (2021.10.8)\n",
- "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests) (2.10)\n",
- "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests) (3.0.4)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests) (2022.5.18.1)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests) (1.25.11)\n",
- "Requirement already satisfied: attrs<22.0,>=21.2 in /usr/local/lib/python3.7/dist-packages (from requests_cache) (21.4.0)\n",
- "Requirement already satisfied: cattrs<2.0,>=1.8 in /usr/local/lib/python3.7/dist-packages (from requests_cache) (1.10.0)\n",
+ "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests) (3.0.4)\n",
+ "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests) (2.10)\n",
"Requirement already satisfied: appdirs<2.0.0,>=1.4.4 in /usr/local/lib/python3.7/dist-packages (from requests_cache) (1.4.4)\n",
+ "Requirement already satisfied: attrs<22.0,>=21.2 in /usr/local/lib/python3.7/dist-packages (from requests_cache) (21.4.0)\n",
"Requirement already satisfied: url-normalize<2.0,>=1.4 in /usr/local/lib/python3.7/dist-packages (from requests_cache) (1.4.3)\n",
+ "Requirement already satisfied: cattrs<2.0,>=1.8 in /usr/local/lib/python3.7/dist-packages (from requests_cache) (1.10.0)\n",
"Requirement already satisfied: typing_extensions in /usr/local/lib/python3.7/dist-packages (from cattrs<2.0,>=1.8->requests_cache) (4.2.0)\n"
]
}
],
"source": [
- "!pip install numpy pandas requests requests_cache"
+ "!pip install numpy pandas requests requests_cache SetSimilaritySearch"
]
},
+ {
+ "cell_type": "code",
+ "source": [
+ "from copy import copy, deepcopy\n",
+ "import json"
+ ],
+ "metadata": {
+ "id": "uG7JfetZjcXH"
+ },
+ "execution_count": 100,
+ "outputs": []
+ },
{
"cell_type": "markdown",
"source": [
- "# BTE with PFOCR Coocurrence"
+ "# BTE with PFOCR Coocurrence\n",
+ "\n",
+ "I modified a query from the 3/17 Question of the Month to find relationships like this:\n",
+ "\n",
+ "`1 of 3 selected genes` -> `any gene` -> `Valproic Acid`"
],
"metadata": {
"id": "j9DiSC9NU_Fa"
@@ -86,9 +104,18 @@
"metadata": {
"id": "rzoUUSDeVz0a"
},
- "execution_count": 2,
+ "execution_count": 51,
"outputs": []
},
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Get BTE Results"
+ ],
+ "metadata": {
+ "id": "TxW41muraAFL"
+ }
+ },
{
"cell_type": "code",
"source": [
@@ -142,9 +169,9 @@
"base_uri": "https://localhost:8080/"
},
"id": "N71NYHWeVCj9",
- "outputId": "ff69d979-3a3b-4bc2-8f95-bd6caafa7376"
+ "outputId": "841a8dfb-1aa4-4f60-ba43-88989b390541"
},
- "execution_count": 3,
+ "execution_count": 52,
"outputs": [
{
"output_type": "stream",
@@ -158,20 +185,20 @@
{
"cell_type": "code",
"source": [
- "gene_nodes = []\n",
- "for query_node_id, v in query[\"message\"][\"query_graph\"][\"nodes\"].items():\n",
+ "gene_q_node_ids = []\n",
+ "for q_node_id, v in query[\"message\"][\"query_graph\"][\"nodes\"].items():\n",
" if \"biolink:Gene\" in v[\"categories\"]:\n",
- " gene_nodes.append(query_node_id)\n",
- "print(gene_nodes)"
+ " gene_q_node_ids.append(q_node_id)\n",
+ "print(gene_q_node_ids)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KwyR8hcDZFUo",
- "outputId": "68382737-f8f3-47c2-8d6e-dfe94d0ee002"
+ "outputId": "6b734567-d04a-4f63-d066-e408143e983d"
},
- "execution_count": 4,
+ "execution_count": 53,
"outputs": [
{
"output_type": "stream",
@@ -185,843 +212,1719 @@
{
"cell_type": "code",
"source": [
- "bte_json = bte_r.json()\n",
- "bte_results = bte_json[\"message\"][\"results\"]\n",
- "all_genes = set()\n",
- "prefixes = set()\n",
- "ncbi_genes = set()\n",
+ "bte_message = bte_r.json()[\"message\"]\n",
+ "bte_results = bte_message[\"results\"]\n",
"genes_to_bte_results = dict()\n",
"for bte_result in bte_results:\n",
" bte_result_genes = []\n",
- " for gene_node in gene_nodes:\n",
- " for entry in bte_result[\"node_bindings\"][gene_node]:\n",
+ " for gene_q_node_id in gene_q_node_ids:\n",
+ " for entry in bte_result[\"node_bindings\"][gene_q_node_id]:\n",
" id = entry[\"id\"]\n",
- " all_genes.add(id)\n",
- " prefixes.add(entry[\"id\"].split(\":\")[0])\n",
" target_prefix = \"NCBIGene:\"\n",
" if id.startswith(target_prefix):\n",
- " ncbi_genes.add(entry[\"id\"][len(target_prefix):])\n",
" bte_result_genes.append(\"NCBIGene:\" + entry[\"id\"][len(target_prefix):])\n",
" genes_key = tuple(sorted(bte_result_genes))\n",
" if not genes_key in genes_to_bte_results:\n",
" genes_to_bte_results[genes_key] = []\n",
- " genes_to_bte_results[genes_key].append(bte_result)\n",
- "prefixes"
+ " genes_to_bte_results[genes_key].append(bte_result)"
+ ],
+ "metadata": {
+ "id": "Wjqcf5E4Yk_h"
+ },
+ "execution_count": 54,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Get PFOCR Data\n",
+ "\n",
+ "Download the entire JSON file we gave to BTE."
+ ],
+ "metadata": {
+ "id": "1GQojZqc0QDf"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "pfocr_url = \"https://www.dropbox.com/s/1f14t5zaseocyg6/bte_chemicals_diseases_genes.ndjson?dl=1\"\n",
+ "pfocr_request = requests.get(pfocr_url)\n",
+ "print(f\"status_code: {pfocr_request.status_code}\")\n",
+ "if pfocr_request.status_code != 200:\n",
+ " print(pfocr_request.text)\n",
+ "\n",
+ "genes_to_figids = {}\n",
+ "figid_to_genes = {}\n",
+ "figid_to_pfocr_result = {}\n",
+ "for line in pfocr_request.text.splitlines():\n",
+ " pfocr_result = json.loads(line)\n",
+ " figid = pfocr_result[\"_id\"]\n",
+ " genes = set(\n",
+ " [\"NCBIGene:\" + g for g in pfocr_result[\"associatedWith\"][\"mentions\"][\"genes\"][\"ncbigene\"]]\n",
+ " )\n",
+ " figid_to_pfocr_result[figid] = pfocr_result\n",
+ " figid_to_genes[figid] = genes\n",
+ "\n",
+ " genes_key = tuple(sorted(genes))\n",
+ " if not genes_key in genes_to_figids:\n",
+ " genes_to_figids[genes_key] = []\n",
+ " genes_to_figids[genes_key].append(figid)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
- "id": "Wjqcf5E4Yk_h",
- "outputId": "db6e7568-4b98-4fa2-9057-2e6906bd772e"
+ "id": "OyJJUHbepKp2",
+ "outputId": "7434741f-5f0d-4396-9e83-fa8a2555f34e"
},
- "execution_count": 5,
+ "execution_count": 55,
"outputs": [
{
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "{'NCBIGene'}"
- ]
- },
- "metadata": {},
- "execution_count": 5
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "status_code: 200\n"
+ ]
}
]
},
{
- "cell_type": "code",
+ "cell_type": "markdown",
"source": [
- "query_param_limit = 4000\n",
- "pfocr_results = []\n",
- "q_term = \"\"\n",
- "for ncbi_gene in ncbi_genes:\n",
- " if q_term != \"\":\n",
- " concatenator = \" OR \"\n",
- " else:\n",
- " concatenator = \"\"\n",
- "\n",
- " next_q = concatenator + \"associatedWith.mentions.genes.ncbigene:\" + ncbi_gene\n",
- "\n",
- " if len(q_term + next_q) < query_param_limit:\n",
- " q_term += next_q\n",
- " else:\n",
- " payload = {\"q\": q_term}\n",
- " pfocr_r = requests.get(\"https://biothings.ncats.io/pfocr/query\", params=payload)\n",
- " print(f\"status_code: {pfocr_r.status_code}\")\n",
- " if pfocr_r.status_code != 200:\n",
- " print(pfocr_r.text)\n",
- " pfocr_results.append(pfocr_r.json())\n",
- " q_term = \"associatedWith.mentions.genes.ncbigene:\" + ncbi_gene\n",
- "\n",
- "if q_term:\n",
- " print(f\"q_term: {q_term}\")\n",
- " payload = {\"q\": q_term}\n",
- " pfocr_r = requests.get(\"https://biothings.ncats.io/pfocr/query\", params=payload)\n",
- " print(f\"status_code: {pfocr_r.status_code}\")\n",
- " if pfocr_r.status_code != 200:\n",
- " print(pfocr_r.text)\n",
- " pfocr_results.append(pfocr_r.json())\n",
- " q_term = \"\"\n",
+ "## Connect BTE Results & PFOCR"
+ ],
+ "metadata": {
+ "id": "uCW1x4PxnjYJ"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Compare Algo Performance\n",
"\n",
- "print(len(pfocr_results))"
+ "The following three algorithms all match up the BTE gene sets with the PFOCR sets, but they have different performances:\n",
+ "- Brute Force: 47s\n",
+ "- Check All BTE Results Genes Set: 5s\n",
+ "- SetSimilaritySearch: 2s"
+ ],
+ "metadata": {
+ "id": "hTrLMM_QxONz"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#### Brute Force"
+ ],
+ "metadata": {
+ "id": "ypA0HIzSxfnH"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "all_figure_genes_keys = genes_to_figids.keys()\n",
+ "bf_overlaps_2_plus = set()\n",
+ "for bte_result_genes_key in genes_to_bte_results.keys():\n",
+ " bte_result_genes = set(bte_result_genes_key)\n",
+ " for figure_genes_keys in all_figure_genes_keys:\n",
+ " figure_genes = set(figure_genes_keys)\n",
+ " if len(bte_result_genes.intersection(figure_genes)) >= 2:\n",
+ " bf_overlaps_2_plus.add((bte_result_genes_key, figure_genes_keys))\n",
+ "print(len(bf_overlaps_2_plus))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
- "id": "R96yZ6CfWc7f",
- "outputId": "e1e8f769-09e7-48e4-e4d6-ee64019b5e58"
+ "id": "InJU5egur5PI",
+ "outputId": "ff2f3640-aab2-4ece-fb3d-85bd0c073af8"
},
- "execution_count": 6,
+ "execution_count": 56,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
- "status_code: 200\n",
- "status_code: 200\n",
- "status_code: 200\n",
- "q_term: associatedWith.mentions.genes.ncbigene:4585 OR associatedWith.mentions.genes.ncbigene:23657 OR associatedWith.mentions.genes.ncbigene:3558 OR associatedWith.mentions.genes.ncbigene:2167 OR associatedWith.mentions.genes.ncbigene:36 OR associatedWith.mentions.genes.ncbigene:11118 OR associatedWith.mentions.genes.ncbigene:2169 OR associatedWith.mentions.genes.ncbigene:5950 OR associatedWith.mentions.genes.ncbigene:10038 OR associatedWith.mentions.genes.ncbigene:54556 OR associatedWith.mentions.genes.ncbigene:7450 OR associatedWith.mentions.genes.ncbigene:5111 OR associatedWith.mentions.genes.ncbigene:11083 OR associatedWith.mentions.genes.ncbigene:5927 OR associatedWith.mentions.genes.ncbigene:1026 OR associatedWith.mentions.genes.ncbigene:4582 OR associatedWith.mentions.genes.ncbigene:1543 OR associatedWith.mentions.genes.ncbigene:79633 OR associatedWith.mentions.genes.ncbigene:231 OR associatedWith.mentions.genes.ncbigene:2660 OR associatedWith.mentions.genes.ncbigene:57713 OR associatedWith.mentions.genes.ncbigene:2954 OR associatedWith.mentions.genes.ncbigene:9854 OR associatedWith.mentions.genes.ncbigene:23522 OR associatedWith.mentions.genes.ncbigene:3855 OR associatedWith.mentions.genes.ncbigene:80232 OR associatedWith.mentions.genes.ncbigene:25898 OR associatedWith.mentions.genes.ncbigene:92399 OR associatedWith.mentions.genes.ncbigene:10486 OR associatedWith.mentions.genes.ncbigene:3566 OR associatedWith.mentions.genes.ncbigene:4005 OR associatedWith.mentions.genes.ncbigene:4289 OR associatedWith.mentions.genes.ncbigene:3622 OR associatedWith.mentions.genes.ncbigene:64781 OR associatedWith.mentions.genes.ncbigene:7076 OR associatedWith.mentions.genes.ncbigene:11162 OR associatedWith.mentions.genes.ncbigene:8508 OR associatedWith.mentions.genes.ncbigene:3033 OR associatedWith.mentions.genes.ncbigene:1759 OR associatedWith.mentions.genes.ncbigene:2064 OR associatedWith.mentions.genes.ncbigene:3162 OR associatedWith.mentions.genes.ncbigene:211 OR associatedWith.mentions.genes.ncbigene:7846 OR associatedWith.mentions.genes.ncbigene:58472 OR associatedWith.mentions.genes.ncbigene:23314 OR associatedWith.mentions.genes.ncbigene:2026 OR associatedWith.mentions.genes.ncbigene:80205 OR associatedWith.mentions.genes.ncbigene:79772 OR associatedWith.mentions.genes.ncbigene:335 OR associatedWith.mentions.genes.ncbigene:3866 OR associatedWith.mentions.genes.ncbigene:4023 OR associatedWith.mentions.genes.ncbigene:163050 OR associatedWith.mentions.genes.ncbigene:2735 OR associatedWith.mentions.genes.ncbigene:56052 OR associatedWith.mentions.genes.ncbigene:388610 OR associatedWith.mentions.genes.ncbigene:1728 OR associatedWith.mentions.genes.ncbigene:2744 OR associatedWith.mentions.genes.ncbigene:1975 OR associatedWith.mentions.genes.ncbigene:51310 OR associatedWith.mentions.genes.ncbigene:1371 OR associatedWith.mentions.genes.ncbigene:59 OR associatedWith.mentions.genes.ncbigene:10181 OR associatedWith.mentions.genes.ncbigene:51696 OR associatedWith.mentions.genes.ncbigene:57721 OR associatedWith.mentions.genes.ncbigene:221935 OR associatedWith.mentions.genes.ncbigene:7316 OR associatedWith.mentions.genes.ncbigene:93010 OR associatedWith.mentions.genes.ncbigene:64147 OR associatedWith.mentions.genes.ncbigene:326625 OR associatedWith.mentions.genes.ncbigene:489 OR associatedWith.mentions.genes.ncbigene:79718 OR associatedWith.mentions.genes.ncbigene:3135 OR associatedWith.mentions.genes.ncbigene:100507679 OR associatedWith.mentions.genes.ncbigene:3146 OR associatedWith.mentions.genes.ncbigene:6453 OR associatedWith.mentions.genes.ncbigene:4343 OR associatedWith.mentions.genes.ncbigene:2639 OR associatedWith.mentions.genes.ncbigene:23242 OR associatedWith.mentions.genes.ncbigene:2683 OR associatedWith.mentions.genes.ncbigene:2116 OR associatedWith.mentions.genes.ncbigene:3861 OR associatedWith.mentions.genes.ncbigene:8573 OR associatedWith.mentions.genes.ncbigene:34 OR associatedWith.mentions.genes.ncbigene:7481\n",
- "status_code: 200\n",
- "4\n"
+ "164\n"
]
}
]
},
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#### Check All BTE Results Genes Set\n",
+ "Check whether the figure gene set overlaps with `size >=2` the set made up of all BTE results genes. Note this performance may be worse if the BTE result count is larger."
+ ],
+ "metadata": {
+ "id": "bXbZKX-9xj2P"
+ }
+ },
{
"cell_type": "code",
"source": [
- "hits = []\n",
- "for pfocr_result in pfocr_results:\n",
- " hits += pfocr_result[\"hits\"]\n",
- "print(len(hits))"
+ "all_bte_result_genes_keys = genes_to_bte_results.keys()\n",
+ "all_bte_result_genes = set([]).union(\n",
+ " *[set(bte_result_genes_key) for bte_result_genes_key in all_bte_result_genes_keys]\n",
+ ")\n",
+ "bf_overlaps_2_plus = set()\n",
+ "\n",
+ "for figure_genes_keys in genes_to_figids.keys():\n",
+ " figure_genes = set(figure_genes_keys)\n",
+ " if len(figure_genes.intersection(all_bte_result_genes)) >= 2:\n",
+ " for bte_result_genes_key in all_bte_result_genes_keys:\n",
+ " bte_result_genes = set(bte_result_genes_key)\n",
+ " if len(bte_result_genes.intersection(figure_genes)) >= 2:\n",
+ " bf_overlaps_2_plus.add((bte_result_genes_key, figure_genes_keys))\n",
+ "\n",
+ "print(len(bf_overlaps_2_plus))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
- "id": "q2WCmhpNQOI-",
- "outputId": "1b4c8333-daf5-44f3-e6df-c9811e5258f9"
+ "id": "IORU-9sQtAKt",
+ "outputId": "82809f77-a623-444f-f37b-ca2131249db8"
},
- "execution_count": 7,
+ "execution_count": 57,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
- "40\n"
+ "164\n"
]
}
]
},
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#### SetSimilaritySearch\n",
+ "\n",
+ "This algorithm is sometimes too permissive, making the overlap check needed, but even so, it's faster and gets the same results once we apply the overlap check."
+ ],
+ "metadata": {
+ "id": "jXFbvMrTx8sS"
+ }
+ },
{
"cell_type": "code",
"source": [
- "pfocr_id_to_genes = dict()\n",
- "pfocr_id_to_pfocr_result = dict()\n",
- "for hit in hits:\n",
- " pfocr_id = hit[\"_id\"]\n",
- " pfocr_id_to_genes[pfocr_id] = set(\n",
- " \"NCBIGene:\" + g for g in hit[\"associatedWith\"][\"mentions\"][\"genes\"][\"ncbigene\"]\n",
- " )\n",
- " pfocr_id_to_pfocr_result[pfocr_id] = {\n",
- " \"figureUrl\": hit[\"associatedWith\"][\"figureUrl\"],\n",
- " \"pmc\": hit[\"associatedWith\"][\"pmc\"],\n",
- " \"score\": hit[\"_score\"],\n",
- " }"
+ "from SetSimilaritySearch import SearchIndex\n",
+ "\n",
+ "pfocr_gene_sets = list(genes_to_figids.keys())\n",
+ "index = SearchIndex(pfocr_gene_sets, similarity_func_name=\"containment\", \n",
+ " similarity_threshold=0.8)\n",
+ "\n",
+ "sss_overlaps_2_plus = set()\n",
+ "for bte_result_genes_key in genes_to_bte_results.keys():\n",
+ " bte_result_genes = set(bte_result_genes_key)\n",
+ " results = index.query(bte_result_genes)\n",
+ " for result in results:\n",
+ " figure_genes = pfocr_gene_sets[result[0]]\n",
+ " if len(bte_result_genes.intersection(figure_genes)) >= 2:\n",
+ " sss_overlaps_2_plus.add((bte_result_genes_key, tuple(sorted(figure_genes))))\n",
+ "print(len(sss_overlaps_2_plus))"
],
"metadata": {
- "id": "Dz7mPvLxTPC8"
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gWWRq_rdq2vC",
+ "outputId": "0c97fb02-5c60-45aa-add1-c20145c21daa"
},
- "execution_count": 8,
- "outputs": []
+ "execution_count": 58,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "164\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Choose and Apply SetSimilaritySearch\n",
+ "\n",
+ "SetSimilaritySearch was fastest, so let's choose it and use it to augment the BTE results."
+ ],
+ "metadata": {
+ "id": "2EzVeKVcn_2L"
+ }
},
{
"cell_type": "code",
"source": [
- "print(len(pfocr_id_to_genes.keys()))\n",
- "len(genes_to_bte_results.keys())"
+ "from SetSimilaritySearch import SearchIndex\n",
+ "\n",
+ "pfocr_gene_sets = list(genes_to_figids.keys())\n",
+ "index = SearchIndex(pfocr_gene_sets, similarity_func_name=\"containment\", \n",
+ " similarity_threshold=0.8)\n",
+ "\n",
+ "overlaps_2_plus_count = 0\n",
+ "for bte_result_genes_key, genes_bte_results in genes_to_bte_results.items():\n",
+ " bte_result_genes = set(bte_result_genes_key)\n",
+ " results = index.query(bte_result_genes)\n",
+ " #if results:\n",
+ " # print(\"------\")\n",
+ " # print(f'bte_result_genes: {bte_result_genes}')\n",
+ " for result in results:\n",
+ " figure_genes = pfocr_gene_sets[result[0]]\n",
+ " score = result[1]\n",
+ " figids = genes_to_figids[tuple(sorted(figure_genes))]\n",
+ " #print(f'{figids} ({score}) - {figure_genes}')\n",
+ " common = bte_result_genes.intersection(figure_genes)\n",
+ " #print(f'intersection: {common}')\n",
+ " if len(common) >= 2:\n",
+ " for figid in figids:\n",
+ " overlaps_2_plus_count += 1\n",
+ " pfocr_result = figid_to_pfocr_result[figid]\n",
+ " #print(\"--------\")\n",
+ " #print(f'{figid} - {figure_genes}')\n",
+ " for bte_result in genes_bte_results:\n",
+ " #print(f'bte_result: {sorted(bte_result_genes)}')\n",
+ " #print(f'common: {bte_result_genes.intersection(figure_genes)}')\n",
+ " nodes = set()\n",
+ " for q_node_id, values in bte_result[\"node_bindings\"].items():\n",
+ " for value in values:\n",
+ " id = value[\"id\"]\n",
+ " if id in figure_genes:\n",
+ " nodes.add(q_node_id)\n",
+ " if not \"pfocr\" in bte_result:\n",
+ " bte_result[\"pfocr\"] = []\n",
+ " pfocr_entry = copy(pfocr_result)\n",
+ " pfocr_entry[\"nodes\"] = sorted(nodes)\n",
+ " pfocr_entry[\"score\"] = score\n",
+ " bte_result[\"pfocr\"].append(pfocr_entry)\n",
+ "print(len(overlaps_2_plus_count))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
- "id": "6dWDZ2Igb2g9",
- "outputId": "196f9fae-efbb-461e-c154-9eba630173e1"
+ "id": "aZX7IzakKCnH",
+ "outputId": "dabcdf7a-f7d1-4f7a-821a-b21d19ce5a13"
},
- "execution_count": 9,
+ "execution_count": 59,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
- "36\n"
+ "172\n"
]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "\n",
+ "kg_nodes = bte_message[\"knowledge_graph\"][\"nodes\"]\n",
+ "\n",
+ "bte_rows = []\n",
+ "for bte_result in bte_results:\n",
+ " bte_row_template = {}\n",
+ " for q_node_id, value in bte_result[\"node_bindings\"].items():\n",
+ " node_labels = []\n",
+ " for v in value:\n",
+ " id = v[\"id\"]\n",
+ " name = kg_nodes[id][\"name\"]\n",
+ " #node_labels.append(f'{name} ({id})')\n",
+ " node_labels.append(name)\n",
+ " bte_row_template[q_node_id] = \",\".join(node_labels)\n",
+ " # just taking the first one for now\n",
+ " bte_row_template[q_node_id + \"_identifier\"] = value[0][\"id\"]\n",
+ "\n",
+ " bte_row_template[\"score\"] = bte_result[\"score\"]\n",
+ " \n",
+ " if \"pfocr\" in bte_result:\n",
+ " for pfocr_result in bte_result[\"pfocr\"]:\n",
+ " bte_row = deepcopy(bte_row_template)\n",
+ " bte_row[\"figure_title\"] = pfocr_result[\"associatedWith\"][\"title\"]\n",
+ " bte_row[\"figid\"] = pfocr_result[\"_id\"]\n",
+ " bte_row[\"figure_url\"] = pfocr_result[\"associatedWith\"][\"figureUrl\"]\n",
+ " bte_row[\"pfocr_score\"] = pfocr_result[\"score\"]\n",
+ " bte_rows.append(bte_row)\n",
+ " else:\n",
+ " bte_rows.append(bte_row_template)\n",
+ " \n",
+ "bte_df = pd.DataFrame(bte_rows)\n",
+ "bte_df"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 904
},
+ "id": "XdofZ-R62QM9",
+ "outputId": "d70a6c5a-b9c2-4958-a14d-aa38a0aec294"
+ },
+ "execution_count": 118,
+ "outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "356"
+ " n2 n2_identifier n1 n1_identifier n0 \\\n",
+ "0 Valproic acid PUBCHEM.COMPOUND:3121 HMOX1 NCBIGene:3162 ALAS1 \n",
+ "1 Valproic acid PUBCHEM.COMPOUND:3121 HMOX1 NCBIGene:3162 ALAS1 \n",
+ "2 Valproic acid PUBCHEM.COMPOUND:3121 HMOX1 NCBIGene:3162 ALAS1 \n",
+ "3 Valproic acid PUBCHEM.COMPOUND:3121 ATP5MG NCBIGene:10632 TRUB2 \n",
+ "4 Valproic acid PUBCHEM.COMPOUND:3121 ATF2 NCBIGene:1386 KRT7 \n",
+ ".. ... ... ... ... ... \n",
+ "454 Valproic acid PUBCHEM.COMPOUND:3121 SLC7A11 NCBIGene:23657 ALAS1 \n",
+ "455 Valproic acid PUBCHEM.COMPOUND:3121 HPRT1 NCBIGene:3251 ALAS1 \n",
+ "456 Valproic acid PUBCHEM.COMPOUND:3121 CFTR NCBIGene:1080 KRT7 \n",
+ "457 Valproic acid PUBCHEM.COMPOUND:3121 LAMP2 NCBIGene:3920 TRUB2 \n",
+ "458 Valproic acid PUBCHEM.COMPOUND:3121 FABP2 NCBIGene:2169 ALAS1 \n",
+ "\n",
+ " n0_identifier score figure_title \\\n",
+ "0 NCBIGene:211 0 Activated Oncogenic Pathway Modifies Iron Netw... \n",
+ "1 NCBIGene:211 0 The heme biosynthetic pathway and aspects of i... \n",
+ "2 NCBIGene:211 0 Pathogenesis and Clinical Features of the Acut... \n",
+ "3 NCBIGene:26995 0 NaN \n",
+ "4 NCBIGene:3855 0 Transcriptional landscape of mouse-aged ovarie... \n",
+ ".. ... ... ... \n",
+ "454 NCBIGene:211 0 NaN \n",
+ "455 NCBIGene:211 0 Metabolic pathway differences between LLO118 a... \n",
+ "456 NCBIGene:3855 0 Network of pancreatic cancer-target genes \n",
+ "457 NCBIGene:26995 0 NaN \n",
+ "458 NCBIGene:211 0 NaN \n",
+ "\n",
+ " figid \\\n",
+ "0 PMC5293201__pcbi.1005352.g001.jpg \n",
+ "1 PMC4279155__metabolites-04-00977-g001.jpg \n",
+ "2 PMC6754303__nihms-1526961-f0002.jpg \n",
+ "3 NaN \n",
+ "4 PMC6281605__41420_2018_121_Fig2_HTML.jpg \n",
+ ".. ... \n",
+ "454 NaN \n",
+ "455 PMC7500861__nihms-1628382-f0005.jpg \n",
+ "456 PMC6826333__or-42-06-2561-g01.jpg \n",
+ "457 NaN \n",
+ "458 NaN \n",
+ "\n",
+ " figure_url pfocr_score \n",
+ "0 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5... 1.0 \n",
+ "1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4... 1.0 \n",
+ "2 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6... 1.0 \n",
+ "3 NaN NaN \n",
+ "4 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6... 1.0 \n",
+ ".. ... ... \n",
+ "454 NaN NaN \n",
+ "455 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7... 1.0 \n",
+ "456 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6... 1.0 \n",
+ "457 NaN NaN \n",
+ "458 NaN NaN \n",
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+ ],
+ "text/html": [
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+ " NCBIGene:211 | \n",
+ " 0 | \n",
+ " Activated Oncogenic Pathway Modifies Iron Netw... | \n",
+ " PMC5293201__pcbi.1005352.g001.jpg | \n",
+ " https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5... | \n",
+ " 1.0 | \n",
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+ " PMC6754303__nihms-1526961-f0002.jpg | \n",
+ " https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6... | \n",
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+ " ATF2 | \n",
+ " NCBIGene:1386 | \n",
+ " KRT7 | \n",
+ " NCBIGene:3855 | \n",
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+ " Transcriptional landscape of mouse-aged ovarie... | \n",
+ " PMC6281605__41420_2018_121_Fig2_HTML.jpg | \n",
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\n",
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+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
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+ " 0 | \n",
+ " Metabolic pathway differences between LLO118 a... | \n",
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\n",
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+ " KRT7 | \n",
+ " NCBIGene:3855 | \n",
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+ " PMC6826333__or-42-06-2561-g01.jpg | \n",
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459 rows × 11 columns
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+ "\n",
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+ "
\n",
+ "
\n",
+ " "
]
},
"metadata": {},
- "execution_count": 9
+ "execution_count": 118
}
]
},
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The following table is also [available as a Google Sheet](https://docs.google.com/spreadsheets/d/1yHBTxs6SghJGk-wQP7JbmpZ5Wbq6OY6Yz7PEtwd4j_w/edit?usp=sharing)."
+ ],
+ "metadata": {
+ "id": "FYO4IO4HDGxm"
+ }
+ },
{
"cell_type": "code",
"source": [
- "from copy import copy\n",
- "\n",
- "\n",
- "cooccurring_bte_results = []\n",
- "for g0_tuple, genes_bte_results in genes_to_bte_results.items():\n",
- " g0 = set(g0_tuple)\n",
- " for pfocr_id, g1 in pfocr_id_to_genes.items():\n",
- " if len(g0.intersection(g1)) >= 2:\n",
- " for bte_result in genes_bte_results:\n",
- " matches = list()\n",
- " nodes = set()\n",
- " for query_node_id, values in bte_result[\"node_bindings\"].items():\n",
- " for value in values:\n",
- " id = value[\"id\"]\n",
- " if id in g0:\n",
- " nodes.add(query_node_id)\n",
- " bte_result[\"pfocr\"] = copy(pfocr_id_to_pfocr_result[pfocr_id])\n",
- " bte_result[\"pfocr\"][\"nodes\"] = sorted(nodes)\n",
- " cooccurring_bte_results += genes_bte_results\n",
- "cooccurring_bte_results"
+ "bte_df.rename(columns={\"figure_url\": \"figure_count\"}).groupby([\"n0\", \"n1\", \"n2\"])[[\"figure_count\"]].count().sort_values(\"figure_count\", ascending=False)"
],
"metadata": {
"colab": {
- "base_uri": "https://localhost:8080/"
+ "base_uri": "https://localhost:8080/",
+ "height": 455
},
- "id": "jp_798WAfkNb",
- "outputId": "016b0159-e65c-4996-83b1-0e9cc5df6a82"
+ "id": "aWy5v2oEAXfH",
+ "outputId": "71fc053d-4ca4-4ea5-9fe3-9b44d1c73fcb"
},
- "execution_count": 10,
+ "execution_count": 120,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "[{'edge_bindings': {'e01': [{'id': '5fc6cc476f4bcb068460c5d299db52dd'}],\n",
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- " {'id': '551bd928ba58c60f465375306b81bbac'},\n",
- " {'id': '6f6dd5467485ea133e64cb76b405c7d2'}]},\n",
- " 'node_bindings': {'n0': [{'id': 'NCBIGene:211'}],\n",
- " 'n1': [{'id': 'NCBIGene:5599'}],\n",
- " 'n2': [{'id': 'PUBCHEM.COMPOUND:3121'}]},\n",
- " 'pfocr': {'figureUrl': 'https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5354998/bin/nihms843846f10.jpg',\n",
- " 'nodes': ['n0', 'n1'],\n",
- " 'pmc': 'PMC5354998',\n",
- " 'score': 52.49851},\n",
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- " {'edge_bindings': {'e01': [{'id': '0eb02f5d33311f1ab445417b47fd6e11'},\n",
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- " {'id': 'bd1e5ef859f20039e1940e5a1ff9fb93'}]},\n",
- " 'node_bindings': {'n0': [{'id': 'NCBIGene:3855'}],\n",
- " 'n1': [{'id': 'NCBIGene:5111'}],\n",
- " 'n2': [{'id': 'PUBCHEM.COMPOUND:3121'}]},\n",
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- " 'pfocr': {'figureUrl': 'https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6826333/bin/or-42-06-2561-g01.jpg',\n",
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- " 'n1': [{'id': 'NCBIGene:2099'}],\n",
- " 'n2': [{'id': 'PUBCHEM.COMPOUND:3121'}]},\n",
- " 'pfocr': {'figureUrl': 'https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6826333/bin/or-42-06-2561-g01.jpg',\n",
- " 'nodes': ['n0', 'n1'],\n",
- " 'pmc': 'PMC6826333',\n",
- " 'score': 104.711754},\n",
- " 'score': 0},\n",
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- " 'node_bindings': {'n0': [{'id': 'NCBIGene:3855'}],\n",
- " 'n1': [{'id': 'NCBIGene:7316'}],\n",
- " 'n2': [{'id': 'PUBCHEM.COMPOUND:3121'}]},\n",
- " 'pfocr': {'figureUrl': 'https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4677515/bin/fig-1.jpg',\n",
- " 'nodes': ['n0', 'n1'],\n",
- " 'pmc': 'PMC4677515',\n",
- " 'score': 51.81472},\n",
- " 'score': 0},\n",
- " {'edge_bindings': {'e01': [{'id': '5ecc3d9468a155111a726935dc84affa'}],\n",
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+ " figure_count\n",
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+ "... ...\n",
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},
"metadata": {},
- "execution_count": 10
+ "execution_count": 120
}
]
},
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Display some figures"
+ ],
+ "metadata": {
+ "id": "H4oQCpp-oUZU"
+ }
+ },
{
"cell_type": "code",
"source": [
- "print(f\"{len(cooccurring_bte_results)} BTE results (out of {len(bte_results)} total) have pfocr figures\")"
+ "from IPython.display import Image\n",
+ "from IPython.core.display import HTML \n",
+ "\n",
+ "\n",
+ "for i, df in bte_df[(bte_df[\"n0_identifier\"] == \"NCBIGene:211\") & (bte_df[\"n1_identifier\"] == \"NCBIGene:210\")][[\"figure_url\", \"figure_title\"]].iterrows():\n",
+ " figure_title = df[\"figure_title\"]\n",
+ " figure_url = df[\"figure_url\"]\n",
+ " display(Image(url=figure_url))\n",
+ " print(figure_title)\n",
+ " print(\"\")\n",
+ " print(\"\")\n",
+ " print(\"\")\n"
],
"metadata": {
"colab": {
- "base_uri": "https://localhost:8080/"
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
},
- "id": "N1RxoljKoelD",
- "outputId": "6107c3ea-38e5-4070-fe44-8e92310d769b"
+ "id": "1iqomqP3t1C5",
+ "outputId": "8882419c-7213-4b54-eba3-becd28733c28"
},
- "execution_count": 11,
+ "execution_count": 140,
"outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {}
+ },
{
"output_type": "stream",
"name": "stdout",
"text": [
- "49 BTE results (out of 356 total) have pfocr figures\n"
+ "Evolution of the Tetrapyrrole Biosynthetic Pathway in Secondary Algae: Conservation, Redundancy and Replacement\n",
+ "\n",
+ "\n",
+ "\n"
]
- }
- ]
+ },
+ {
+ "output_type": "display_data",
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