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"# TraitGym [](https://colab.research.google.com/github/songlab-cal/TraitGym/blob/main/TraitGym.ipynb)\n",
"In this example we will load the Mendelian traits dataset and run variant effect prediction based on euclidean distance of GPN-Animal-Promoter embeddings of the reference and alternate sequences."
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"## Setup"
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"!pip install -q pyfaidx s3fs git+https://github.com/songlab-cal/gpn.git\n",
"!pip install -q -U transformers datasets"
],
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"colab": {
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.3/13.3 MB\u001b[0m \u001b[31m75.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.9/8.9 MB\u001b[0m \u001b[31m73.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m135.4/135.4 kB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.7/53.7 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for gpn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for pandarallel (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.12.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.7/9.7 MB\u001b[0m \u001b[31m34.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"s3fs 2024.12.0 requires fsspec==2024.12.0.*, but you have fsspec 2024.9.0 which is incompatible.\n",
"gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0m"
]
}
]
},
{
"cell_type": "code",
"source": [
"from Bio.Seq import Seq\n",
"from datasets import load_dataset\n",
"import fsspec\n",
"import gpn.model # to register AutoModel\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import polars as pl\n",
"from pyfaidx import Fasta\n",
"import seaborn as sns\n",
"from sklearn.metrics import average_precision_score\n",
"import torch\n",
"import torch.nn.functional as F\n",
"from transformers import AutoTokenizer, AutoModel, TrainingArguments, Trainer\n",
"import tempfile"
],
"metadata": {
"id": "yFgUHIlJxs2P"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"dataset_path = \"songlab/TraitGym\"\n",
"dataset_config = \"mendelian_traits\"\n",
"model_path = \"songlab/gpn-animal-promoter\"\n",
"# in the paper we average with the predictions with the reverse complement\n",
"# however for a quick evaluation you can omit this\n",
"average_rc = False"
],
"metadata": {
"id": "G9JKGeFXOB5n"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Load dataset"
],
"metadata": {
"id": "39wqWU2nw0th"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 287,
"referenced_widgets": [
"354b6ae4307840b5a73c4cb10f976d7d",
"079b1e09f86c40c1936b276c93e14271",
"6059d28fa74845b9a7ec65878187f978",
"93ac776b9acf4b1c81566c16d2408e8c",
"0b42e95b4d98494893bce8b38ec46ada",
"9f9af5e0b00f4d419927290074e398fe",
"4a63ea2b9cae4e979426205b1312f39e",
"e47ef5863d5847cf8667106d9fc95057",
"d55c9de46b6749deb738cc1b59904ff8",
"6e19e94413f946daa10f3f8f2ce8d895",
"7afe78cb30a94ce2ac4a2e6587e13764",
"5690f364ff504f6bb6d89aa074794e10",
"cc50a4e9a2404208bdc8f3fd5f9ba2eb",
"146ad95d109f4203828a42688c582711",
"53e2168f0f494113a28d251fa1c61051",
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"688266106cb54dffbe066bb3d8b33b99",
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"4073c99a09424482be157783b5142567",
"3f044b6801c444d992abffbf42b7912d",
"381192f45b1a4bdea86af2b810bb9c45",
"7357aaf41476452aab915e52ddf6bf7b",
"fde154d03164486fb4160cf1dcc516a8",
"443f5b80dd534c88aaf2a17c3e1a893b"
]
},
"id": "6p3Vk6zRvPhQ",
"outputId": "a39b7fb8-70ea-4b09-f97d-24132efcff78"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"README.md: 0%| | 0.00/563 [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "354b6ae4307840b5a73c4cb10f976d7d"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"test.parquet: 0%| | 0.00/50.2k [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "5690f364ff504f6bb6d89aa074794e10"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating test split: 0%| | 0/3380 [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "ef77fe79ff3a4bd6b9815d12c1b1ef42"
}
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Dataset({\n",
" features: ['chrom', 'pos', 'ref', 'alt', 'OMIM', 'consequence', 'label', 'tss_dist', 'match_group'],\n",
" num_rows: 3380\n",
"})"
]
},
"metadata": {},
"execution_count": 4
}
],
"source": [
"dataset = load_dataset(dataset_path, dataset_config, split=\"test\")\n",
"dataset"
]
},
{
"cell_type": "code",
"source": [
"dataset[0]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QW1mpMscyOUD",
"outputId": "965889ee-c94e-4e04-e298-01fdc83c4ee5"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'chrom': '1',\n",
" 'pos': 1425822,\n",
" 'ref': 'C',\n",
" 'alt': 'G',\n",
" 'OMIM': None,\n",
" 'consequence': 'PLS',\n",
" 'label': False,\n",
" 'tss_dist': 48,\n",
" 'match_group': 'PLS_4'}"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"source": [
"# if you just want a dataframe you can load it directly:\n",
"# pd.read_parquet(\"hf://datasets/songlab/TraitGym/mendelian_traits_matched_9/test.parquet\")\n",
"V = dataset.to_pandas()\n",
"V"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "1DBiScBWyVis",
"outputId": "56e39155-d354-4d34-e456-4ead360311e7"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" chrom pos ref alt OMIM consequence label tss_dist \\\n",
"0 1 1425822 C G None PLS False 48 \n",
"1 1 1615869 C T None PLS False 35 \n",
"2 1 1659060 G A None PLS False 47 \n",
"3 1 1659114 A G None PLS False 101 \n",
"4 1 2050958 T C None 5_prime_UTR_variant False 149 \n",
"... ... ... .. .. ... ... ... ... \n",
"3375 X 155613005 C T None PLS False 52 \n",
"3376 X 155719093 C A None 5_prime_UTR_variant False 4 \n",
"3377 X 155881342 A C None PLS False 2 \n",
"3378 X 155881414 C T None 5_prime_UTR_variant False 35 \n",
"3379 X 156020793 A G None upstream_gene_variant False 167 \n",
"\n",
" match_group \n",
"0 PLS_4 \n",
"1 PLS_0 \n",
"2 PLS_4 \n",
"3 PLS_5 \n",
"4 5_prime_UTR_variant_7 \n",
"... ... \n",
"3375 PLS_52 \n",
"3376 5_prime_UTR_variant_101 \n",
"3377 PLS_57 \n",
"3378 5_prime_UTR_variant_110 \n",
"3379 upstream_gene_variant_9 \n",
"\n",
"[3380 rows x 9 columns]"
],
"text/html": [
"\n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" chrom | \n",
" pos | \n",
" ref | \n",
" alt | \n",
" OMIM | \n",
" consequence | \n",
" label | \n",
" tss_dist | \n",
" match_group | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 1 | \n",
" 1425822 | \n",
" C | \n",
" G | \n",
" None | \n",
" PLS | \n",
" False | \n",
" 48 | \n",
" PLS_4 | \n",
"
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" \n",
" | 1 | \n",
" 1 | \n",
" 1615869 | \n",
" C | \n",
" T | \n",
" None | \n",
" PLS | \n",
" False | \n",
" 35 | \n",
" PLS_0 | \n",
"
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" \n",
" | 2 | \n",
" 1 | \n",
" 1659060 | \n",
" G | \n",
" A | \n",
" None | \n",
" PLS | \n",
" False | \n",
" 47 | \n",
" PLS_4 | \n",
"
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" \n",
" | 3 | \n",
" 1 | \n",
" 1659114 | \n",
" A | \n",
" G | \n",
" None | \n",
" PLS | \n",
" False | \n",
" 101 | \n",
" PLS_5 | \n",
"
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" \n",
" | 4 | \n",
" 1 | \n",
" 2050958 | \n",
" T | \n",
" C | \n",
" None | \n",
" 5_prime_UTR_variant | \n",
" False | \n",
" 149 | \n",
" 5_prime_UTR_variant_7 | \n",
"
\n",
" \n",
" | ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
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" ... | \n",
" ... | \n",
" ... | \n",
"
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" \n",
" | 3375 | \n",
" X | \n",
" 155613005 | \n",
" C | \n",
" T | \n",
" None | \n",
" PLS | \n",
" False | \n",
" 52 | \n",
" PLS_52 | \n",
"
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" \n",
" | 3376 | \n",
" X | \n",
" 155719093 | \n",
" C | \n",
" A | \n",
" None | \n",
" 5_prime_UTR_variant | \n",
" False | \n",
" 4 | \n",
" 5_prime_UTR_variant_101 | \n",
"
\n",
" \n",
" | 3377 | \n",
" X | \n",
" 155881342 | \n",
" A | \n",
" C | \n",
" None | \n",
" PLS | \n",
" False | \n",
" 2 | \n",
" PLS_57 | \n",
"
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" \n",
" | 3378 | \n",
" X | \n",
" 155881414 | \n",
" C | \n",
" T | \n",
" None | \n",
" 5_prime_UTR_variant | \n",
" False | \n",
" 35 | \n",
" 5_prime_UTR_variant_110 | \n",
"
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" \n",
" | 3379 | \n",
" X | \n",
" 156020793 | \n",
" A | \n",
" G | \n",
" None | \n",
" upstream_gene_variant | \n",
" False | \n",
" 167 | \n",
" upstream_gene_variant_9 | \n",
"
\n",
" \n",
"
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"
3380 rows × 9 columns
\n",
"
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"
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"
\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "V",
"summary": "{\n \"name\": \"V\",\n \"rows\": 3380,\n \"fields\": [\n {\n \"column\": \"chrom\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 19,\n \"samples\": [\n \"1\",\n \"7\",\n \"13\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pos\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 56516547,\n \"min\": 90661,\n \"max\": 248591989,\n \"num_unique_values\": 3354,\n \"samples\": [\n 233464121,\n 119367787,\n 63474189\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ref\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"G\",\n \"T\",\n \"C\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"alt\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"T\",\n \"C\",\n \"G\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"OMIM\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 113,\n \"samples\": [\n \"MIM 613978\",\n \"MIM 602771\",\n \"MIM 188740\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"consequence\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 12,\n \"samples\": [\n \"upstream_gene_variant\",\n \"pELS_flank\",\n \"PLS\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"boolean\",\n \"num_unique_values\": 2,\n \"samples\": [\n true,\n false\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"tss_dist\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 31507,\n \"min\": 0,\n \"max\": 371628,\n \"num_unique_values\": 1387,\n \"samples\": [\n 101764,\n 25171\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"match_group\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 338,\n \"samples\": [\n \"non_coding_transcript_exon_variant_66\",\n \"3_prime_UTR_variant_28\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"source": [
"## Load model"
],
"metadata": {
"id": "PyvRpy3-x9UH"
}
},
{
"cell_type": "code",
"source": [
"# Wrapper to compute a VEP score\n",
"class ModelVEP(torch.nn.Module):\n",
" def __init__(self, model_path):\n",
" super().__init__()\n",
" self.model = AutoModel.from_pretrained(\n",
" model_path,\n",
" trust_remote_code=True,\n",
" )\n",
"\n",
" def forward(\n",
" self,\n",
" input_ids_ref=None,\n",
" input_ids_alt=None,\n",
" ):\n",
" embed_ref = self.model(input_ids=input_ids_ref).last_hidden_state.reshape(len(input_ids_ref), -1)\n",
" embed_alt = self.model(input_ids=input_ids_alt).last_hidden_state.reshape(len(input_ids_ref), -1)\n",
" return F.pairwise_distance(embed_ref, embed_alt)\n",
"\n",
"\n",
"class ModelVEPAverageRC(torch.nn.Module):\n",
" def __init__(self, model_path):\n",
" super().__init__()\n",
" self.model = AutoModel.from_pretrained(\n",
" model_path,\n",
" trust_remote_code=True,\n",
" )\n",
"\n",
" def get_scores(self, input_ids_ref, input_ids_alt):\n",
" embed_ref = self.model(input_ids=input_ids_ref).last_hidden_state.reshape(len(input_ids_ref), -1)\n",
" embed_alt = self.model(input_ids=input_ids_alt).last_hidden_state.reshape(len(input_ids_ref), -1)\n",
" return F.pairwise_distance(embed_ref, embed_alt)\n",
"\n",
" def forward(\n",
" self,\n",
" input_ids_ref_fwd=None,\n",
" input_ids_alt_fwd=None,\n",
" input_ids_ref_rev=None,\n",
" input_ids_alt_rev=None,\n",
" ):\n",
" fwd = self.get_scores(input_ids_ref_fwd, input_ids_alt_fwd)\n",
" rev = self.get_scores(input_ids_ref_rev, input_ids_alt_rev)\n",
" return (fwd + rev) / 2"
],
"metadata": {
"id": "JID0TXVj5lv-"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
"window_size = 512\n",
"if average_rc:\n",
" model = ModelVEPAverageRC(model_path)\n",
"else:\n",
" model = ModelVEP(model_path)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 214,
"referenced_widgets": [
"0ade8e14a15944b695d390db199b9dd9",
"c53c155008af4c4e9ae6c21598e3b0f1",
"ff9dd793206542ffa9f31a48b7e0314d",
"7146c456cbaa41498f9122fd0d29b1d2",
"dcfa4d369495418295baf7fbe9568dce",
"151e2df6bea14a43ad2705f678c99195",
"6dbf91bea24840fea03b861144a3946e",
"d1431f375a2e46838a5970cc80f61200",
"dcfa667e270c40d79dd93309da9f77fb",
"32418a73eb3b41f3be335fb89d2b877c",
"fa0e9d33904e4900984fafcdcdf6e6e7",
"3737b2439dbd4e568d366a9318c260a4",
"c02a2073a45d4b93bc30bc5ed6be8805",
"c5874ac66fd94a0b9cf43762c741118f",
"51b43b7ce3d04b548361ddcb011ac227",
"134c2b6d1ac94d1ebd41517d5f16d9b6",
"7faa6f4bb40c4f49b21b8cdd999105be",
"3a3bf3efb5334a41a7d1412a746c2e1c",
"525edb5bb9194355b730557663f00a43",
"8d88a2fedae6498bb3a5f57457333bd9",
"c08096b9eb584ca4870d13f3e5824a9b",
"74fc154af2304c2fb0ba48d14ecc85c7",
"565dd0e46c434aa7a73d6bb908bd0680",
"2b3fb166766c4f79a0efc8626c93b246",
"baf99987c9b54fe881574d800ee09f14",
"c8f25334ab334b5fac6797ab7d309f38",
"a86710668b3540d0aa8bb4a36dce8ceb",
"18028875cdaa4988bf19419280e0279a",
"e22fdcb3b13c44638b6ac147cd5a4f8c",
"d2d8169da2df4bf78acd960b4e3676b0",
"0c3e11bcd3ac4f628676209f92949f14",
"cc29c662fd6f4be0b49889c75655414d",
"acb44fc0488843469bb1f83a0c856331",
"1656307b55e241078531ac0983ee96fb",
"b2956c54417446cf9346ad51e3808075",
"df36a32b3b42485db9a27cec3cd21e31",
"77988a600cb64c7a9773f84c1725a21b",
"93c39b9dab7f4fa5ac7bc5f2c03e9175",
"10b81b4b60a34853a9663e78bb2e1abe",
"7b4126a076cb4dcbba25b04d19334b0b",
"eae24a291baa4c04a1bc8d1966a3ccae",
"c649b21f7c1642ebb880d3a62b8281ed",
"46f44a143d7f4536a93db59e033ce665",
"ed4930de91c440c0a1a7239a4ed06e5d",
"a31b47baeb624307b0bb270d81949504",
"7459011748494d4a9939220746108716",
"c923d77c086d4abb93fabc6f8d20b5b7",
"c8922ee8c0224221b2673f7618701da4",
"abd5f43ea691418898820e68d36fdd8f",
"2a38d3b1f0df48fb96b2e9256a0c564b",
"3a408803721449a1aa63a65cd51f9a1d",
"9a39944759c64b57a27f5c4e5ef935d7",
"816ad4562cbc4ff1934da1f5cd3b69c7",
"197fe37ca48b4dfeb674e8a0688102d0",
"0faeff78cc7446bab7c32806805cce5a"
]
},
"id": "_-hrLbqwxqxH",
"outputId": "ea4249ed-1ba8-4b30-9fcd-3f0efc17a93b"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/784 [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "0ade8e14a15944b695d390db199b9dd9"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"tokenizer.json: 0%| | 0.00/1.15k [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "3737b2439dbd4e568d366a9318c260a4"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"special_tokens_map.json: 0%| | 0.00/419 [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "565dd0e46c434aa7a73d6bb908bd0680"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"config.json: 0%| | 0.00/1.17k [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "1656307b55e241078531ac0983ee96fb"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"model.safetensors: 0%| | 0.00/609M [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "a31b47baeb624307b0bb270d81949504"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"dilation_schedule=[1, 2, 4, 8, 16, 32, 64, 128, 1, 2, 4, 8, 16, 32, 64, 128, 1, 2, 4, 8, 16, 32, 64, 128, 1, 2, 4, 8, 16, 32, 64, 128, 1, 2, 4, 8, 16, 32, 64, 128, 1, 2, 4, 8, 16, 32, 64, 128, 1, 2, 4, 8, 16, 32, 64, 128, 1, 2, 4, 8, 16, 32, 64, 128]\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Tokenize dataset"
],
"metadata": {
"id": "BmoMd7eyx_eS"
}
},
{
"cell_type": "code",
"source": [
"class Genome:\n",
" def __init__(self, path):\n",
" self.data = Fasta(fsspec.open(path, anon=True))\n",
"\n",
" def __call__(self, chrom, start, end, strand=\"+\"):\n",
" res = self.data[chrom][start:end]\n",
" if strand == \"-\":\n",
" res = res.reverse.complement\n",
" return str(res)"
],
"metadata": {
"id": "qiLlhmcT1BxK"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"genome = Genome(\"s3://broad-references/hg38/v0/Homo_sapiens_assembly38.fasta\")"
],
"metadata": {
"id": "50tERpTd1jGW"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def tokenize(seqs):\n",
" return tokenizer(\n",
" seqs,\n",
" padding=False,\n",
" truncation=False,\n",
" return_token_type_ids=False,\n",
" return_attention_mask=False,\n",
" return_special_tokens_mask=False,\n",
" return_tensors=\"pt\",\n",
" )[\"input_ids\"]\n",
"\n",
"def get_tokenized_seq(vs):\n",
" # we convert from 1-based coordinate (standard in VCF) to\n",
" # 0-based, to use with Genome\n",
" chrom = np.array(vs[\"chrom\"])\n",
" n = len(chrom)\n",
" pos = np.array(vs[\"pos\"]) - 1\n",
" start = pos - window_size // 2\n",
" end = pos + window_size // 2\n",
" seq_fwd = [genome(\"chr\" + chrom[i], start[i], end[i]) for i in range(n)]\n",
" seq_fwd = np.array([list(seq.upper()) for seq in seq_fwd], dtype=\"object\")\n",
" assert seq_fwd.shape[1] == window_size\n",
" ref_fwd = np.array(vs[\"ref\"])\n",
" alt_fwd = np.array(vs[\"alt\"])\n",
" pos_fwd = window_size // 2\n",
"\n",
" def prepare_output(seq, pos, ref, alt):\n",
" assert (seq[:, pos] == ref).all(), f\"{seq[:, pos]}, {ref}\"\n",
" seq_ref = seq\n",
" seq_alt = seq.copy()\n",
" seq_alt[:, pos] = alt\n",
" return (\n",
" tokenize([\"\".join(x) for x in seq_ref]),\n",
" tokenize([\"\".join(x) for x in seq_alt]),\n",
" )\n",
"\n",
" res = {}\n",
" res[\"input_ids_ref\"], res[\"input_ids_alt\"] = prepare_output(seq_fwd, pos_fwd, ref_fwd, alt_fwd)\n",
" return res\n",
"\n",
"def get_tokenized_seq_average_rc(vs):\n",
" # we convert from 1-based coordinate (standard in VCF) to\n",
" # 0-based, to use with Genome\n",
" chrom = np.array(vs[\"chrom\"])\n",
" n = len(chrom)\n",
" pos = np.array(vs[\"pos\"]) - 1\n",
" start = pos - window_size // 2\n",
" end = pos + window_size // 2\n",
" seq_fwd = [genome(\"chr\" + chrom[i], start[i], end[i]) for i in range(n)]\n",
" seq_rev = [str(Seq(x).reverse_complement()) for x in seq_fwd]\n",
" seq_fwd = np.array([list(seq.upper()) for seq in seq_fwd], dtype=\"object\")\n",
" seq_rev = np.array([list(seq.upper()) for seq in seq_rev], dtype=\"object\")\n",
" assert seq_fwd.shape[1] == window_size\n",
" assert seq_rev.shape[1] == window_size\n",
" ref_fwd = np.array(vs[\"ref\"])\n",
" alt_fwd = np.array(vs[\"alt\"])\n",
" ref_rev = np.array([str(Seq(x).reverse_complement()) for x in ref_fwd])\n",
" alt_rev = np.array([str(Seq(x).reverse_complement()) for x in alt_fwd])\n",
" pos_fwd = window_size // 2\n",
" pos_rev = pos_fwd - 1 if window_size % 2 == 0 else pos_fwd\n",
"\n",
" def prepare_output(seq, pos, ref, alt):\n",
" assert (seq[:, pos] == ref).all(), f\"{seq[:, pos]}, {ref}\"\n",
" seq_ref = seq\n",
" seq_alt = seq.copy()\n",
" seq_alt[:, pos] = alt\n",
" return (\n",
" tokenize([\"\".join(x) for x in seq_ref]),\n",
" tokenize([\"\".join(x) for x in seq_alt]),\n",
" )\n",
"\n",
" res = {}\n",
" res[\"input_ids_ref_fwd\"], res[\"input_ids_alt_fwd\"] = prepare_output(seq_fwd, pos_fwd, ref_fwd, alt_fwd)\n",
" res[\"input_ids_ref_rev\"], res[\"input_ids_alt_rev\"] = prepare_output(seq_rev, pos_rev, ref_rev, alt_rev)\n",
" return res"
],
"metadata": {
"id": "2Nsgbz8A2Fo3"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"if average_rc:\n",
" dataset.set_transform(get_tokenized_seq_average_rc)\n",
"else:\n",
" dataset.set_transform(get_tokenized_seq)"
],
"metadata": {
"id": "zj4W7vHQ35Q3"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"dataset[0]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GLDcWQj94hu0",
"outputId": "dff9d8ca-6262-496e-9d62-c9ef0d7ae151"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'input_ids_ref': tensor([4, 4, 6, 5, 5, 4, 3, 5, 4, 4, 6, 5, 4, 6, 5, 3, 5, 6, 5, 4, 4, 6, 5, 4,\n",
" 4, 5, 6, 5, 6, 3, 4, 5, 5, 5, 4, 4, 4, 4, 3, 4, 3, 4, 4, 4, 6, 5, 4, 5,\n",
" 5, 4, 6, 4, 3, 4, 5, 4, 5, 4, 3, 6, 4, 4, 4, 3, 5, 5, 6, 4, 4, 4, 4, 6,\n",
" 4, 6, 4, 3, 6, 4, 4, 3, 4, 6, 4, 6, 5, 3, 5, 3, 4, 5, 4, 3, 5, 5, 4, 5,\n",
" 5, 3, 5, 5, 3, 4, 3, 4, 4, 5, 3, 4, 3, 3, 5, 5, 6, 5, 6, 6, 4, 3, 5, 3,\n",
" 6, 5, 3, 5, 5, 5, 3, 5, 4, 3, 5, 4, 5, 5, 5, 5, 4, 6, 5, 5, 5, 5, 4, 6,\n",
" 5, 5, 5, 3, 4, 3, 5, 5, 5, 5, 5, 6, 5, 5, 5, 5, 5, 5, 4, 6, 5, 5, 4, 4,\n",
" 6, 4, 4, 3, 5, 5, 6, 5, 5, 4, 3, 5, 4, 6, 3, 4, 6, 5, 3, 6, 5, 5, 5, 5,\n",
" 4, 3, 5, 4, 6, 3, 4, 4, 5, 5, 3, 5, 6, 4, 3, 3, 5, 4, 4, 4, 6, 6, 6, 5,\n",
" 3, 3, 3, 6, 5, 3, 5, 5, 6, 4, 6, 6, 6, 5, 6, 5, 6, 5, 5, 5, 5, 5, 3, 5,\n",
" 4, 6, 5, 4, 3, 5, 4, 4, 4, 4, 5, 4, 4, 4, 5, 5, 4, 3, 4, 3, 5, 5, 5, 3,\n",
" 5, 5, 5, 3, 5, 5, 4, 5, 5, 4, 4, 4, 4, 5, 5, 6, 5, 5, 5, 3, 3, 5, 5, 5,\n",
" 4, 4, 4, 6, 5, 4, 3, 3, 5, 5, 4, 4, 6, 5, 4, 4, 4, 3, 5, 3, 5, 4, 3, 4,\n",
" 4, 3, 5, 5, 3, 5, 4, 6, 5, 5, 4, 6, 5, 5, 5, 5, 4, 6, 5, 5, 3, 5, 3, 4,\n",
" 6, 5, 5, 4, 5, 4, 6, 5, 4, 6, 5, 5, 3, 5, 4, 6, 5, 4, 3, 5, 4, 6, 5, 4,\n",
" 4, 6, 5, 5, 4, 3, 5, 3, 5, 5, 5, 5, 3, 3, 3, 4, 6, 5, 3, 5, 5, 6, 5, 5,\n",
" 5, 5, 5, 6, 5, 6, 5, 5, 4, 6, 4, 6, 5, 5, 5, 3, 4, 3, 5, 3, 4, 3, 6, 4,\n",
" 3, 5, 5, 5, 4, 3, 5, 5, 3, 4, 4, 6, 4, 4, 4, 6, 3, 5, 4, 4, 3, 5, 5, 5,\n",
" 6, 4, 4, 3, 4, 5, 5, 6, 4, 6, 5, 5, 4, 6, 5, 5, 5, 5, 5, 3, 6, 5, 6, 5,\n",
" 5, 3, 4, 6, 5, 5, 4, 6, 5, 4, 3, 3, 4, 5, 6, 5, 5, 5, 5, 4, 5, 5, 5, 5,\n",
" 6, 6, 3, 3, 3, 5, 5, 6, 4, 6, 6, 4, 4, 3, 5, 5, 5, 4, 3, 5, 4, 4, 4, 6,\n",
" 5, 5, 3, 5, 3, 5, 4, 3]),\n",
" 'input_ids_alt': tensor([4, 4, 6, 5, 5, 4, 3, 5, 4, 4, 6, 5, 4, 6, 5, 3, 5, 6, 5, 4, 4, 6, 5, 4,\n",
" 4, 5, 6, 5, 6, 3, 4, 5, 5, 5, 4, 4, 4, 4, 3, 4, 3, 4, 4, 4, 6, 5, 4, 5,\n",
" 5, 4, 6, 4, 3, 4, 5, 4, 5, 4, 3, 6, 4, 4, 4, 3, 5, 5, 6, 4, 4, 4, 4, 6,\n",
" 4, 6, 4, 3, 6, 4, 4, 3, 4, 6, 4, 6, 5, 3, 5, 3, 4, 5, 4, 3, 5, 5, 4, 5,\n",
" 5, 3, 5, 5, 3, 4, 3, 4, 4, 5, 3, 4, 3, 3, 5, 5, 6, 5, 6, 6, 4, 3, 5, 3,\n",
" 6, 5, 3, 5, 5, 5, 3, 5, 4, 3, 5, 4, 5, 5, 5, 5, 4, 6, 5, 5, 5, 5, 4, 6,\n",
" 5, 5, 5, 3, 4, 3, 5, 5, 5, 5, 5, 6, 5, 5, 5, 5, 5, 5, 4, 6, 5, 5, 4, 4,\n",
" 6, 4, 4, 3, 5, 5, 6, 5, 5, 4, 3, 5, 4, 6, 3, 4, 6, 5, 3, 6, 5, 5, 5, 5,\n",
" 4, 3, 5, 4, 6, 3, 4, 4, 5, 5, 3, 5, 6, 4, 3, 3, 5, 4, 4, 4, 6, 6, 6, 5,\n",
" 3, 3, 3, 6, 5, 3, 5, 5, 6, 4, 6, 6, 6, 5, 6, 5, 6, 5, 5, 5, 5, 5, 3, 5,\n",
" 4, 6, 5, 4, 3, 5, 4, 4, 4, 4, 5, 4, 4, 4, 5, 5, 5, 3, 4, 3, 5, 5, 5, 3,\n",
" 5, 5, 5, 3, 5, 5, 4, 5, 5, 4, 4, 4, 4, 5, 5, 6, 5, 5, 5, 3, 3, 5, 5, 5,\n",
" 4, 4, 4, 6, 5, 4, 3, 3, 5, 5, 4, 4, 6, 5, 4, 4, 4, 3, 5, 3, 5, 4, 3, 4,\n",
" 4, 3, 5, 5, 3, 5, 4, 6, 5, 5, 4, 6, 5, 5, 5, 5, 4, 6, 5, 5, 3, 5, 3, 4,\n",
" 6, 5, 5, 4, 5, 4, 6, 5, 4, 6, 5, 5, 3, 5, 4, 6, 5, 4, 3, 5, 4, 6, 5, 4,\n",
" 4, 6, 5, 5, 4, 3, 5, 3, 5, 5, 5, 5, 3, 3, 3, 4, 6, 5, 3, 5, 5, 6, 5, 5,\n",
" 5, 5, 5, 6, 5, 6, 5, 5, 4, 6, 4, 6, 5, 5, 5, 3, 4, 3, 5, 3, 4, 3, 6, 4,\n",
" 3, 5, 5, 5, 4, 3, 5, 5, 3, 4, 4, 6, 4, 4, 4, 6, 3, 5, 4, 4, 3, 5, 5, 5,\n",
" 6, 4, 4, 3, 4, 5, 5, 6, 4, 6, 5, 5, 4, 6, 5, 5, 5, 5, 5, 3, 6, 5, 6, 5,\n",
" 5, 3, 4, 6, 5, 5, 4, 6, 5, 4, 3, 3, 4, 5, 6, 5, 5, 5, 5, 4, 5, 5, 5, 5,\n",
" 6, 6, 3, 3, 3, 5, 5, 6, 4, 6, 6, 4, 4, 3, 5, 5, 5, 4, 3, 5, 4, 4, 4, 6,\n",
" 5, 5, 3, 5, 3, 5, 4, 3])}"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "markdown",
"source": [
"## Run inference"
],
"metadata": {
"id": "-lz_YC-G539T"
}
},
{
"cell_type": "code",
"source": [
"training_args = TrainingArguments(\n",
" output_dir=tempfile.TemporaryDirectory().name,\n",
" per_device_eval_batch_size=128,\n",
" # pyfaidx does not allow multiple workers\n",
" # for longer jobs you can use the gpn.data.Genome class,\n",
" # which loads a local fasta file into memory and can use multiple\n",
" # workers, see e.g.\n",
" # https://github.com/songlab-cal/gpn/blob/main/gpn/ss/run_vep_embed_dist.py\n",
" dataloader_num_workers=0,\n",
" remove_unused_columns=False,\n",
" torch_compile=False,\n",
" fp16=True,\n",
" report_to=\"none\",\n",
")\n",
"trainer = Trainer(model=model, args=training_args)\n",
"preds = trainer.predict(test_dataset=dataset).predictions\n",
"preds.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"id": "z7cS7RDC5DWJ",
"outputId": "db12461a-41ac-4539-8bc6-5f781cf10d03"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"text/html": []
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(3380,)"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "markdown",
"source": [
"## Compute metrics"
],
"metadata": {
"id": "ijChgnmI55fn"
}
},
{
"cell_type": "code",
"source": [
"V[\"score\"] = preds"
],
"metadata": {
"id": "84DkGrmPN5nN"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# let's compare to the precomputed scores\n",
"# this is using RC averaging\n",
"V[\"precomputed_score\"] = pd.read_parquet(\"hf://datasets/songlab/TraitGym/mendelian_traits_matched_9/features/GPN_final_EuclideanDistance.parquet\").score.values\n",
"V[[\"score\", \"precomputed_score\"]].corr()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 112
},
"id": "HheYRetBSher",
"outputId": "5a9dccc1-bb6f-4bc1-8c71-d8dedd776022"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" score precomputed_score\n",
"score 1.000000 0.956949\n",
"precomputed_score 0.956949 1.000000"
],
"text/html": [
"\n",
" \n",
"
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"\n",
"
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" \n",
" \n",
" | \n",
" score | \n",
" precomputed_score | \n",
"
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" \n",
" \n",
" \n",
" | score | \n",
" 1.000000 | \n",
" 0.956949 | \n",
"
\n",
" \n",
" | precomputed_score | \n",
" 0.956949 | \n",
" 1.000000 | \n",
"
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" \n",
"
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"
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"
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"
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],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"summary": "{\n \"name\": \"V[[\\\"score\\\", \\\"precomputed_score\\\"]]\",\n \"rows\": 2,\n \"fields\": [\n {\n \"column\": \"score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.030441660786716115,\n \"min\": 0.9569489904542648,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.9569489904542648,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"precomputed_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.030441660786716115,\n \"min\": 0.9569489904542648,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.0,\n 0.9569489904542648\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"source": [
"plt.figure(figsize=(2, 2))\n",
"sns.histplot(\n",
" data=V, x=\"score\", bins=30, hue=\"label\", stat=\"density\",\n",
" common_norm=False, common_bins=True,\n",
")\n",
"sns.despine();"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 234
},
"id": "4y9p5gEtQpcg",
"outputId": "74aca40a-2123-4a50-c0bd-a062e8cbfb15"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# global AUPRC\n",
"average_precision_score(V.label, V.score)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "REMKfC6MTXiO",
"outputId": "1cbf068a-6b7c-4d6f-d865-ac30f347703f"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.3050859084098214"
]
},
"metadata": {},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"source": [
"# AUPRC by chrom\n",
"res_by_chrom = []\n",
"for chrom in V.chrom.unique():\n",
" V_chrom = V[V.chrom == chrom]\n",
" res_by_chrom.append([chrom, len(V_chrom), average_precision_score(V_chrom.label, V_chrom.score)])\n",
"res_by_chrom = pd.DataFrame(res_by_chrom, columns=[\"chrom\", \"n\", \"AUPRC\"])\n",
"res_by_chrom"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 645
},
"id": "KC4NnIFPUbVe",
"outputId": "27bf63e3-3b5d-4ee8-e046-94ae74314293"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" chrom n AUPRC\n",
"0 1 210 0.285684\n",
"1 2 230 0.317321\n",
"2 3 310 0.151283\n",
"3 5 20 1.000000\n",
"4 6 30 0.347619\n",
"5 7 210 0.192152\n",
"6 8 70 0.120540\n",
"7 9 240 0.086625\n",
"8 10 190 0.183487\n",
"9 11 480 0.285151\n",
"10 12 30 0.500000\n",
"11 13 210 0.466352\n",
"12 14 40 0.125368\n",
"13 16 80 0.467061\n",
"14 17 60 0.558547\n",
"15 19 400 0.324191\n",
"16 20 50 0.441667\n",
"17 22 20 0.500000\n",
"18 X 500 0.629526"
],
"text/html": [
"\n",
" \n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" chrom | \n",
" n | \n",
" AUPRC | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 1 | \n",
" 210 | \n",
" 0.285684 | \n",
"
\n",
" \n",
" | 1 | \n",
" 2 | \n",
" 230 | \n",
" 0.317321 | \n",
"
\n",
" \n",
" | 2 | \n",
" 3 | \n",
" 310 | \n",
" 0.151283 | \n",
"
\n",
" \n",
" | 3 | \n",
" 5 | \n",
" 20 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" | 4 | \n",
" 6 | \n",
" 30 | \n",
" 0.347619 | \n",
"
\n",
" \n",
" | 5 | \n",
" 7 | \n",
" 210 | \n",
" 0.192152 | \n",
"
\n",
" \n",
" | 6 | \n",
" 8 | \n",
" 70 | \n",
" 0.120540 | \n",
"
\n",
" \n",
" | 7 | \n",
" 9 | \n",
" 240 | \n",
" 0.086625 | \n",
"
\n",
" \n",
" | 8 | \n",
" 10 | \n",
" 190 | \n",
" 0.183487 | \n",
"
\n",
" \n",
" | 9 | \n",
" 11 | \n",
" 480 | \n",
" 0.285151 | \n",
"
\n",
" \n",
" | 10 | \n",
" 12 | \n",
" 30 | \n",
" 0.500000 | \n",
"
\n",
" \n",
" | 11 | \n",
" 13 | \n",
" 210 | \n",
" 0.466352 | \n",
"
\n",
" \n",
" | 12 | \n",
" 14 | \n",
" 40 | \n",
" 0.125368 | \n",
"
\n",
" \n",
" | 13 | \n",
" 16 | \n",
" 80 | \n",
" 0.467061 | \n",
"
\n",
" \n",
" | 14 | \n",
" 17 | \n",
" 60 | \n",
" 0.558547 | \n",
"
\n",
" \n",
" | 15 | \n",
" 19 | \n",
" 400 | \n",
" 0.324191 | \n",
"
\n",
" \n",
" | 16 | \n",
" 20 | \n",
" 50 | \n",
" 0.441667 | \n",
"
\n",
" \n",
" | 17 | \n",
" 22 | \n",
" 20 | \n",
" 0.500000 | \n",
"
\n",
" \n",
" | 18 | \n",
" X | \n",
" 500 | \n",
" 0.629526 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "res_by_chrom",
"summary": "{\n \"name\": \"res_by_chrom\",\n \"rows\": 19,\n \"fields\": [\n {\n \"column\": \"chrom\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 19,\n \"samples\": [\n \"1\",\n \"7\",\n \"13\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"n\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 155,\n \"min\": 20,\n \"max\": 500,\n \"num_unique_values\": 15,\n \"samples\": [\n 40,\n 60,\n 210\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"AUPRC\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.22281536138652086,\n \"min\": 0.08662464205313769,\n \"max\": 1.0,\n \"num_unique_values\": 18,\n \"samples\": [\n 0.28568421491122165,\n 0.3173206756653251,\n 0.18348692401355995\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"source": [
"# Weighted average\n",
"\n",
"def stat(df):\n",
" weight = df[\"n\"] / df[\"n\"].sum()\n",
" return (df[\"AUPRC\"] * weight).sum()\n",
"\n",
"stat(res_by_chrom)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8B9kgGTlVDHX",
"outputId": "80ece55e-4180-49c8-f635-24cb765dede5"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.3304637185530955"
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},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"source": [
"def bootstrap_se(df, stat, n_bootstraps=1000):\n",
" df = pl.DataFrame(df)\n",
" return (\n",
" pl.Series([\n",
" stat(df.sample(len(df), with_replacement=True, seed=i))\n",
" for i in range(n_bootstraps)]\n",
" )\n",
" .std()\n",
" )\n",
"\n",
"bootstrap_se(res_by_chrom, stat)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "chbtzAYSVkag",
"outputId": "5477c618-e026-4258-f3c0-a18d5b3406eb"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.05398842833086511"
]
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"source": [
"# compare with official results (which use RC averaging)\n",
"pd.read_csv(\"hf://datasets/songlab/TraitGym/mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN_final_EuclideanDistance.plus.score.csv\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81
},
"id": "HgvdWsZkV7sR",
"outputId": "d7538db1-0234-4387-f1fe-7551a7c53db7"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" model metric score se\n",
"0 GPN_final_EuclideanDistance.plus.score AUPRC 0.346963 0.056745"
],
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"summary": "{\n \"name\": \"pd\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"model\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"GPN_final_EuclideanDistance.plus.score\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"metric\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"AUPRC\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.3469633009736218,\n \"max\": 0.3469633009736218,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.3469633009736218\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"se\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.056745382840026,\n \"max\": 0.056745382840026,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.056745382840026\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
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}