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null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "0faeff78cc7446bab7c32806805cce5a": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "# TraitGym [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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." ], "metadata": { "id": "muWargPyv5Zi" } }, { "cell_type": "markdown", "source": [ "## Setup" ], "metadata": { "id": "5V9APVrtwLlr" } }, { "cell_type": "code", "source": [ "!pip install -q pyfaidx s3fs git+https://github.com/songlab-cal/gpn.git\n", "!pip install -q -U transformers datasets" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "II5CCc_mxEXj", "outputId": "847f69d3-de6d-48b2-81b1-5d2ba02751d5" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m183.9/183.9 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.7/77.7 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\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", "75281a09c57e4eaeb230a9737a5dba7a", "688266106cb54dffbe066bb3d8b33b99", "9ebbe4ddd37f4f2ebdd7d7ea3b5c3339", "07a56cf6486b4f338f967e249a89a470", "aa033b8bd0bc461490fa597ecb1f2b29", "d0f4f73e87104e8686a1f410c7aad9a4", "0e5176ab3e054e3bba1eacb239931176", "ef77fe79ff3a4bd6b9815d12c1b1ef42", "7b5c67519b744a769d43f16e0296c8f8", "a68e1e3900794fbb8969e54714ec4a83", "90a329c6bb4e43bbb6efb108a5fa9805", "61d106a61b474176a0d565dea76d19f4", "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\n", "
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\"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" ], "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", "
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scoreprecomputed_score
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\n" ], "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": [ "
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}, "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", "
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chromnAUPRC
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\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" ] }, "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" ], "text/html": [ "\n", "
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