Datasets:

Liu-Hy
/

GenoTEX

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="agent" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/misc.xml ADDED Viewed

	@@ -0,0 +1,7 @@

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.10" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="agent" project-jdk-type="Python SDK" />
+</project>

.idea/shelf/Uncommitted_changes_before_Checkout_at_4_2_25,_11_43 PM_[Changes]/shelved.patch ADDED Viewed

	@@ -0,0 +1,58 @@

+Index: .idea/workspace.xml
+IDEA additional info:
+Subsystem: com.intellij.openapi.diff.impl.patch.BaseRevisionTextPatchEP
+<+><?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n  <component name=\"AutoImportSettings\">\n    <option name=\"autoReloadType\" value=\"SELECTIVE\" />\n  </component>\n  <component name=\"ChangeListManager\">\n    <list default=\"true\" id=\"f42d40ca-2cc5-439e-9264-44bb0abc9084\" name=\"Changes\" comment=\"minor improvement to README\">\n      <change beforePath=\"$PROJECT_DIR$/.idea/workspace.xml\" beforeDir=\"false\" afterPath=\"$PROJECT_DIR$/.idea/workspace.xml\" afterDir=\"false\" />\n      <change beforePath=\"$PROJECT_DIR$/README.md\" beforeDir=\"false\" afterPath=\"$PROJECT_DIR$/README.md\" afterDir=\"false\" />\n      <change beforePath=\"$PROJECT_DIR$/datasheet.md\" beforeDir=\"false\" afterPath=\"$PROJECT_DIR$/datasheet.md\" afterDir=\"false\" />\n    </list>\n    <option name=\"SHOW_DIALOG\" value=\"false\" />\n    <option name=\"HIGHLIGHT_CONFLICTS\" value=\"true\" />\n    <option name=\"HIGHLIGHT_NON_ACTIVE_CHANGELIST\" value=\"false\" />\n    <option name=\"LAST_RESOLUTION\" value=\"IGNORE\" />\n  </component>\n  <component name=\"FileTemplateManagerImpl\">\n    <option name=\"RECENT_TEMPLATES\">\n      <list>\n        <option value=\"Python Script\" />\n      </list>\n    </option>\n  </component>\n  <component name=\"Git.Settings\">\n    <option name=\"RECENT_BRANCH_BY_REPOSITORY\">\n      <map>\n        <entry key=\"$PROJECT_DIR$\" value=\"main\" />\n      </map>\n    </option>\n    <option name=\"RECENT_GIT_ROOT_PATH\" value=\"$PROJECT_DIR$\" />\n  </component>\n  <component name=\"GitHubPullRequestSearchHistory\">{\n  &quot;lastFilter&quot;: {\n    &quot;state&quot;: &quot;OPEN&quot;,\n    &quot;assignee&quot;: &quot;Liu-Hy&quot;\n  }\n}</component>\n  <component name=\"GithubPullRequestsUISettings\">{\n  &quot;selectedUrlAndAccountId&quot;: {\n    &quot;url&quot;: &quot;https://github.com/Liu-Hy/GenoTex.git&quot;,\n    &quot;accountId&quot;: &quot;b4dda4eb-1de4-425c-acd8-943be7903700&quot;\n  }\n}</component>\n  <component name=\"MarkdownSettingsMigration\">\n    <option name=\"stateVersion\" value=\"1\" />\n  </component>\n  <component name=\"ProjectColorInfo\">{\n  &quot;associatedIndex&quot;: 4\n}</component>\n  <component name=\"ProjectId\" id=\"2hnGT4oo4wr7hSZdj7h2hXqMtb8\" />\n  <component name=\"ProjectLevelVcsManager\" settingsEditedManually=\"true\" />\n  <component name=\"ProjectViewState\">\n    <option name=\"hideEmptyMiddlePackages\" value=\"true\" />\n    <option name=\"showLibraryContents\" value=\"true\" />\n  </component>\n  <component name=\"PropertiesComponent\">{\n  &quot;keyToString&quot;: {\n    &quot;RunOnceActivity.OpenProjectViewOnStart&quot;: &quot;true&quot;,\n    &quot;RunOnceActivity.ShowReadmeOnStart&quot;: &quot;true&quot;,\n    &quot;RunOnceActivity.git.unshallow&quot;: &quot;true&quot;,\n    &quot;WebServerToolWindowFactoryState&quot;: &quot;false&quot;,\n    &quot;com.codeium.enabled&quot;: &quot;true&quot;,\n    &quot;git-widget-placeholder&quot;: &quot;main&quot;,\n    &quot;last_opened_file_path&quot;: &quot;/media/techt/DATA/GenoTEX&quot;,\n    &quot;node.js.detected.package.eslint&quot;: &quot;true&quot;,\n    &quot;node.js.detected.package.tslint&quot;: &quot;true&quot;,\n    &quot;node.js.selected.package.eslint&quot;: &quot;(autodetect)&quot;,\n    &quot;node.js.selected.package.tslint&quot;: &quot;(autodetect)&quot;,\n    &quot;nodejs_package_manager_path&quot;: &quot;npm&quot;,\n    &quot;settings.editor.selected.configurable&quot;: &quot;preferences.pluginManager&quot;,\n    &quot;vue.rearranger.settings.migration&quot;: &quot;true&quot;\n  }\n}</component>\n  <component name=\"SharedIndexes\">\n    <attachedChunks>\n      <set>\n        <option value=\"bundled-js-predefined-d6986cc7102b-deb605915726-JavaScript-PY-243.22562.220\" />\n        <option value=\"bundled-python-sdk-0fc6c617c4bd-9a18a617cbe4-com.jetbrains.pycharm.pro.sharedIndexes.bundled-PY-243.22562.220\" />\n      </set>\n    </attachedChunks>\n  </component>\n  <component name=\"SpellCheckerSettings\" RuntimeDictionaries=\"0\" Folders=\"0\" CustomDictionaries=\"0\" DefaultDictionary=\"application-level\" UseSingleDictionary=\"true\" transferred=\"true\" />\n  <component name=\"TaskManager\">\n    <task active=\"true\" id=\"Default\" summary=\"Default task\">\n      <changelist id=\"f42d40ca-2cc5-439e-9264-44bb0abc9084\" name=\"Changes\" comment=\"\" />\n      <created>1718222432449</created>\n      <option name=\"number\" value=\"Default\" />\n      <option name=\"presentableId\" value=\"Default\" />\n      <updated>1718222432449</updated>\n      <workItem from=\"1718222433484\" duration=\"3190000\" />\n      <workItem from=\"1718225632630\" duration=\"175000\" />\n      <workItem from=\"1718225815163\" duration=\"55000\" />\n      <workItem from=\"1718225875461\" duration=\"1228000\" />\n      <workItem from=\"1718234627043\" duration=\"3976000\" />\n      <workItem from=\"1739750023719\" duration=\"320000\" />\n      <workItem from=\"1741899941970\" duration=\"3264000\" />\n      <workItem from=\"1742180155296\" duration=\"5075000\" />\n      <workItem from=\"1742949372142\" duration=\"35000\" />\n      <workItem from=\"1742949454218\" duration=\"1684000\" />\n      <workItem from=\"1742960167676\" duration=\"1054000\" />\n      <workItem from=\"1743108609184\" duration=\"1228000\" />\n      <workItem from=\"1743296019386\" duration=\"2381000\" />\n      <workItem from=\"1743475063927\" duration=\"3866000\" />\n      <workItem from=\"1743482737360\" duration=\"1305000\" />\n      <workItem from=\"1743538025875\" duration=\"2430000\" />\n    </task>\n    <task id=\"LOCAL-00001\" summary=\"inital commit\">\n      <created>1718225127717</created>\n      <option name=\"number\" value=\"00001\" />\n      <option name=\"presentableId\" value=\"LOCAL-00001\" />\n      <option name=\"project\" value=\"LOCAL\" />\n      <updated>1718225127717</updated>\n    </task>\n    <task id=\"LOCAL-00002\" 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<created>1718236670387</created>\n      <option name=\"number\" value=\"00005\" />\n      <option name=\"presentableId\" value=\"LOCAL-00005\" />\n      <option name=\"project\" value=\"LOCAL\" />\n      <updated>1718236670387</updated>\n    </task>\n    <task id=\"LOCAL-00006\" summary=\"write datasheet\">\n      <created>1718237615776</created>\n      <option name=\"number\" value=\"00006\" />\n      <option name=\"presentableId\" value=\"LOCAL-00006\" />\n      <option name=\"project\" value=\"LOCAL\" />\n      <updated>1718237615776</updated>\n    </task>\n    <task id=\"LOCAL-00007\" summary=\"update figure\">\n      <option name=\"closed\" value=\"true\" />\n      <created>1741900734649</created>\n      <option name=\"number\" value=\"00007\" />\n      <option name=\"presentableId\" value=\"LOCAL-00007\" />\n      <option name=\"project\" value=\"LOCAL\" />\n      <updated>1741900734649</updated>\n    </task>\n    <task id=\"LOCAL-00008\" summary=\"update file\">\n      <option 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<updated>1743477806088</updated>\n    </task>\n    <task id=\"LOCAL-00011\" summary=\"update icon\">\n      <option name=\"closed\" value=\"true\" />\n      <created>1743482839005</created>\n      <option name=\"number\" value=\"00011\" />\n      <option name=\"presentableId\" value=\"LOCAL-00011\" />\n      <option name=\"project\" value=\"LOCAL\" />\n      <updated>1743482839005</updated>\n    </task>\n    <task id=\"LOCAL-00012\" summary=\"minor improvement to README\">\n      <option name=\"closed\" value=\"true\" />\n      <created>1743538115029</created>\n      <option name=\"number\" value=\"00012\" />\n      <option name=\"presentableId\" value=\"LOCAL-00012\" />\n      <option name=\"project\" value=\"LOCAL\" />\n      <updated>1743538115029</updated>\n    </task>\n    <option name=\"localTasksCounter\" value=\"13\" />\n    <servers />\n  </component>\n  <component name=\"TypeScriptGeneratedFilesManager\">\n    <option name=\"version\" value=\"3\" />\n  </component>\n  <component name=\"Vcs.Log.Tabs.Properties\">\n    <option name=\"TAB_STATES\">\n      <map>\n        <entry key=\"MAIN\">\n          <value>\n            <State />\n          </value>\n        </entry>\n      </map>\n    </option>\n  </component>\n  <component name=\"VcsManagerConfiguration\">\n    <MESSAGE value=\"inital commit\" />\n    <MESSAGE value=\"add metadata\" />\n    <MESSAGE value=\"add dataset document\" />\n    <MESSAGE value=\"write datasheet\" />\n    <MESSAGE value=\"update figure\" />\n    <MESSAGE value=\"update file\" />\n    <MESSAGE value=\"update files\" />\n    <MESSAGE value=\"update license\" />\n    <MESSAGE value=\"update icon\" />\n    <MESSAGE value=\"minor improvement to README\" />\n    <option name=\"LAST_COMMIT_MESSAGE\" value=\"minor improvement to README\" />\n  </component>\n</project>
+Subsystem: com.intellij.openapi.diff.impl.patch.CharsetEP
+<+>UTF-8
+===================================================================
+diff --git a/.idea/workspace.xml b/.idea/workspace.xml
+--- a/.idea/workspace.xml	(revision e91a2ea4cd53b6877dd2ccbad31b21a8b790c6a9)
++++ b/.idea/workspace.xml	(date 1743655369456)
+@@ -4,11 +4,7 @@
+     <option name="autoReloadType" value="SELECTIVE" />
+   </component>
+   <component name="ChangeListManager">
+-    <list default="true" id="f42d40ca-2cc5-439e-9264-44bb0abc9084" name="Changes" comment="minor improvement to README">
+-      <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
+-      <change beforePath="$PROJECT_DIR$/README.md" beforeDir="false" afterPath="$PROJECT_DIR$/README.md" afterDir="false" />
+-      <change beforePath="$PROJECT_DIR$/datasheet.md" beforeDir="false" afterPath="$PROJECT_DIR$/datasheet.md" afterDir="false" />
+-    </list>
++    <list default="true" id="f42d40ca-2cc5-439e-9264-44bb0abc9084" name="Changes" comment="minor improvement on document" />
+     <option name="SHOW_DIALOG" value="false" />
+     <option name="HIGHLIGHT_CONFLICTS" value="true" />
+     <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
+@@ -103,6 +99,7 @@
+       <workItem from="1743475063927" duration="3866000" />
+       <workItem from="1743482737360" duration="1305000" />
+       <workItem from="1743538025875" duration="2430000" />
++      <workItem from="1743654977609" duration="390000" />
+     </task>
+     <task id="LOCAL-00001" summary="inital commit">
+       <created>1718225127717</created>
+@@ -194,7 +191,15 @@
+       <option name="project" value="LOCAL" />
+       <updated>1743538115029</updated>
+     </task>
+-    <option name="localTasksCounter" value="13" />
++    <task id="LOCAL-00013" summary="minor improvement on document">
++      <option name="closed" value="true" />
++      <created>1743655091448</created>
++      <option name="number" value="00013" />
++      <option name="presentableId" value="LOCAL-00013" />
++      <option name="project" value="LOCAL" />
++      <updated>1743655091448</updated>
++    </task>
++    <option name="localTasksCounter" value="14" />
+     <servers />
+   </component>
+   <component name="TypeScriptGeneratedFilesManager">
+@@ -222,6 +227,7 @@
+     <MESSAGE value="update license" />
+     <MESSAGE value="update icon" />
+     <MESSAGE value="minor improvement to README" />
+-    <option name="LAST_COMMIT_MESSAGE" value="minor improvement to README" />
++    <MESSAGE value="minor improvement on document" />
++    <option name="LAST_COMMIT_MESSAGE" value="minor improvement on document" />
+   </component>
+ </project>

.idea/shelf/Uncommitted_changes_before_Checkout_at_4_2_25__11_43PM__Changes_.xml ADDED Viewed

	@@ -0,0 +1,4 @@

+<changelist name="Uncommitted_changes_before_Checkout_at_4_2_25,_11_43 PM_[Changes]" date="1743655421030" recycled="true" deleted="true">
+  <option name="PATH" value="$PROJECT_DIR$/.idea/shelf/Uncommitted_changes_before_Checkout_at_4_2_25,_11_43 PM_[Changes]/shelved.patch" />
+  <option name="DESCRIPTION" value="Uncommitted changes before Checkout at 4/2/25, 11:43 PM [Changes]" />
+</changelist>

.idea/shelf/Uncommitted_changes_before_Checkout_at_4_3_25,_9_57 AM_[Changes]/shelved.patch ADDED Viewed

	@@ -0,0 +1,98 @@

+Index: .idea/workspace.xml
+IDEA additional info:
+Subsystem: com.intellij.openapi.diff.impl.patch.BaseRevisionTextPatchEP
+<+><?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n  <component name=\"AutoImportSettings\">\n    <option name=\"autoReloadType\" value=\"SELECTIVE\" />\n  </component>\n  <component name=\"ChangeListManager\">\n    <list default=\"true\" id=\"f42d40ca-2cc5-439e-9264-44bb0abc9084\" name=\"Changes\" comment=\"update icon\" />\n    <option name=\"SHOW_DIALOG\" value=\"false\" />\n    <option name=\"HIGHLIGHT_CONFLICTS\" value=\"true\" />\n    <option name=\"HIGHLIGHT_NON_ACTIVE_CHANGELIST\" value=\"false\" />\n    <option name=\"LAST_RESOLUTION\" value=\"IGNORE\" />\n  </component>\n  <component name=\"FileTemplateManagerImpl\">\n    <option name=\"RECENT_TEMPLATES\">\n      <list>\n        <option value=\"Python Script\" />\n      </list>\n    </option>\n  </component>\n  <component name=\"Git.Settings\">\n    <option name=\"RECENT_BRANCH_BY_REPOSITORY\">\n      <map>\n        <entry key=\"$PROJECT_DIR$\" value=\"main\" />\n      </map>\n    </option>\n    <option 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+Subsystem: com.intellij.openapi.diff.impl.patch.CharsetEP
+<+>UTF-8
+===================================================================
+diff --git a/.idea/workspace.xml b/.idea/workspace.xml
+--- a/.idea/workspace.xml	(revision 1d9abe68f84d99350399932f9fdca58fe2977e16)
++++ b/.idea/workspace.xml	(date 1743655568613)
+@@ -4,7 +4,9 @@
+     <option name="autoReloadType" value="SELECTIVE" />
+   </component>
+   <component name="ChangeListManager">
+-    <list default="true" id="f42d40ca-2cc5-439e-9264-44bb0abc9084" name="Changes" comment="update icon" />
++    <list default="true" id="f42d40ca-2cc5-439e-9264-44bb0abc9084" name="Changes" comment="minor improvement on document">
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++    </list>
+     <option name="SHOW_DIALOG" value="false" />
+     <option name="HIGHLIGHT_CONFLICTS" value="true" />
+     <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
+@@ -49,24 +51,24 @@
+     <option name="hideEmptyMiddlePackages" value="true" />
+     <option name="showLibraryContents" value="true" />
+   </component>
+-  <component name="PropertiesComponent">{
+-  &quot;keyToString&quot;: {
+-    &quot;RunOnceActivity.OpenProjectViewOnStart&quot;: &quot;true&quot;,
+-    &quot;RunOnceActivity.ShowReadmeOnStart&quot;: &quot;true&quot;,
+-    &quot;RunOnceActivity.git.unshallow&quot;: &quot;true&quot;,
+-    &quot;WebServerToolWindowFactoryState&quot;: &quot;false&quot;,
+-    &quot;com.codeium.enabled&quot;: &quot;true&quot;,
+-    &quot;git-widget-placeholder&quot;: &quot;main&quot;,
+-    &quot;last_opened_file_path&quot;: &quot;/media/techt/DATA/GenoTEX&quot;,
+-    &quot;node.js.detected.package.eslint&quot;: &quot;true&quot;,
+-    &quot;node.js.detected.package.tslint&quot;: &quot;true&quot;,
+-    &quot;node.js.selected.package.eslint&quot;: &quot;(autodetect)&quot;,
+-    &quot;node.js.selected.package.tslint&quot;: &quot;(autodetect)&quot;,
+-    &quot;nodejs_package_manager_path&quot;: &quot;npm&quot;,
+-    &quot;settings.editor.selected.configurable&quot;: &quot;preferences.pluginManager&quot;,
+-    &quot;vue.rearranger.settings.migration&quot;: &quot;true&quot;
++  <component name="PropertiesComponent"><![CDATA[{
++  "keyToString": {
++    "RunOnceActivity.OpenProjectViewOnStart": "true",
++    "RunOnceActivity.ShowReadmeOnStart": "true",
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++    "WebServerToolWindowFactoryState": "false",
++    "com.codeium.enabled": "true",
++    "git-widget-placeholder": "1d9abe68",
++    "last_opened_file_path": "/media/techt/DATA/GenoTEX",
++    "node.js.detected.package.eslint": "true",
++    "node.js.detected.package.tslint": "true",
++    "node.js.selected.package.eslint": "(autodetect)",
++    "node.js.selected.package.tslint": "(autodetect)",
++    "nodejs_package_manager_path": "npm",
++    "settings.editor.selected.configurable": "preferences.pluginManager",
++    "vue.rearranger.settings.migration": "true"
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++}]]></component>
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+@@ -181,7 +184,15 @@
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+   <component name="TypeScriptGeneratedFilesManager">
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++    <MESSAGE value="minor improvement to README" />
++    <option name="LAST_COMMIT_MESSAGE" value="minor improvement to README" />
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+  <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
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+</project>

README.md ADDED Viewed

	@@ -0,0 +1,371 @@

+# GenoTEX: A Benchmark for Automated Gene Expression Data Analysis
+<div align="center">
+  <img src="./imgs/genotex_logo.png" alt="GenoTEX Logo" width="200px"/>
+  <br>
+  <br>
+  <a href="https://creativecommons.org/licenses/by/4.0/">
+    <img src="https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg" alt="License: CC BY 4.0">
+  </a>
+  <a href="https://github.com/topics/ai4science">
+    <img src="https://img.shields.io/badge/AI4Science-blue.svg" alt="AI4Science">
+  </a>
+  <a href="https://github.com/topics/llm-agent">
+    <img src="https://img.shields.io/badge/LLM%20Agent-orange.svg" alt="LLM Agent">
+  </a>
+  <a href="https://github.com/topics/computational-genomics">
+    <img src="https://img.shields.io/badge/Computational%20Genomics-green.svg" alt="Computational Genomics">
+  </a>
+</div>
+GenoTEX (**Geno**mics Data Au**t**omatic **Ex**ploration Benchmark) is a benchmark dataset for the automated analysis of gene expression data to identify disease-associated genes while considering the influence of other biological factors. It provides annotated code and results for solving a wide range of gene-trait association (GTA) analysis problems, encompassing dataset selection, preprocessing, and statistical analysis, in a pipeline that follows computational genomics standards. The benchmark includes expert-curated annotations from bioinformaticians to ensure accuracy and reliability.
+The below figure illustrates our benchmark curation process. For detailed information, please refer to our [paper on arXiv](https://arxiv.org/abs/2406.15341).
+<div align="center">
+  <img src="./imgs/benchmark_pipeline.jpg" alt="Benchmark Pipeline" width="90%"/>
+</div>
+## Table of Contents
+- [Dataset Overview](#dataset-overview)
+- [Dataset Structure](#dataset-structure)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Citation](#citation)
+- [License](#license)
+<a id="dataset-overview"></a>
+## 📊 Dataset Overview
+GenoTEX provides a benchmark for evaluating automated methods for gene expression data analysis, particularly for LLM-based agents. In biomedical research, gene expression analysis is crucial for understanding biological mechanisms and advancing clinical applications such as disease marker identification and personalized medicine. However, these analyses are often repetitive, labor-intensive, and prone to errors, leading to significant time and financial burdens on research teams.
+Key features of the benchmark include:
+- **1,384 GTA analysis problems**: 132 unconditional problems and 1,252 conditional problems
+- **41.5 GB of input data**: 911 datasets with an average of 167 samples per dataset (152,415 total samples)
+- **237,907 lines of analysis code**: Carefully curated by bioinformatics experts (average 261 lines per dataset)
+- **Three evaluation tasks**: Dataset selection, data preprocessing, and statistical analysis
+- **Comprehensive gene features**: Average of 18,530 normalized gene features per dataset
+- **Significant gene discoveries**: Significant genes identified per problem
+Each problem in the benchmark involves identifying genes associated with a specific trait (e.g., a disease) while optionally considering the influence of some condition (e.g., age, gender, or a co-existing trait). This approach mimics real-life research scenarios where key genes linked to traits often vary based on the diverse physical conditions of patients.
+<a id="dataset-structure"></a>
+## 🗂️ Dataset Structure
+GenoTEX is distributed in two ways:
+1. **GitHub repository + Cloud storage**: In this approach, we host the code and documentation in the [GitHub repository](https://github.com/Liu-Hy/GenoTEX), while the data is accessible through separate cloud storage links below. This approach allows for efficient access to the analysis methods and their latest updates, while keeping the large data files separate.
+  - Data Available at: [[Google Drive](https://drive.google.com/drive/folders/1ZQ8AflAecW61SrNclaMby-6x9GLCpJoW) | [Baidu Cloud Disk](https://pan.baidu.com/s/1mKfBRiBNY0GUK6LRLnn7UA?pwd=1234)].
+  - Total data size: 82.0 GB — Please ensure you have sufficient disk space before downloading.
+2. **Complete Datasets on Data Platforms**: We also provide a complete, bundled version (code + data) on [Kaggle](https://www.kaggle.com/datasets/haoyangliu14/genotex-llm-agent-benchmark-for-genomic-analysis) and [Hugging Face Hub](https://huggingface.co/datasets/Liu-Hy/GenoTEX).
+These versions are convenient for users who prefer a single download and want to leverage the rich functionalities of these platforms.
+### The Data Part
+The data is organized into three main directories:
+```
+./
+├── input/               # Raw input data from public databases
+│   ├── GEO/             # Data from Gene Expression Omnibus
+│   │   ├── Trait_1/
+│   │   │   ├── GSE12345/
+│   │   │   │   ├── GSE12345_family.soft.gz
+│   │   │   │   └── GSE12345_series_matrix.txt.gz
+│   │   │   └── ...
+���   │   └── ...
+│   └── TCGA/            # Data from The Cancer Genome Atlas
+│       ├── TCGA_Cancer_Type_1/
+│       │   ├── TCGA.XXXX.sampleMap_XXXX_clinicalMatrix
+│       │   └── TCGA.XXXX.sampleMap_HiSeqV2_PANCAN.gz
+│       └── ...
+│
+├── output/              # Analysis output data
+│   ├── preprocess/      # Preprocessed datasets
+│   │   ├── Trait_1/
+│   │   │   ├── clinical_data/
+│   │   │   │   ├── GSE12345.csv
+│   │   │   │   ├── Xena.csv
+│   │   │   │   └── ...
+│   │   │   ├── gene_data/
+│   │   │   │   ├── GSE12345.csv
+│   │   │   │   ├── Xena.csv
+│   │   │   │   └── ...
+│   │   │   ├── cohort_info.json
+│   │   │   ├── GSE12345.csv
+│   │   │   ├── Xena.csv
+│   │   │   └── ...
+│   │   └── ...
+│   └── regress/         # Regression analysis results
+│       ├── Trait_1/
+│       │   ├── significant_genes_condition_None.json
+│       │   ├── significant_genes_condition_Condition_1.json
+│       │   └── ...
+│       └── ...
+│
+└── metadata/            # Problem specifications and domain knowledge
+    ├── task_info.json   # GTA analysis problems; domain knowledge about gene-trait associations
+    └── gene_synonym.json # Gene symbol mapping
+```
+### Notes on Data Organization:
+**1. Trait Name Normalization**: To make path operations safer, trait names are normalized by removing apostrophes (') and replacing spaces with underscores (_). For example, "Crohn's Disease" becomes "Crohns_Disease", and "Large B-cell Lymphoma" becomes "Large_B-cell_Lymphoma". This consistent formatting helps prevent path-related errors when working with the dataset.
+**2. Input Data Structure**:
+   - **GEO folder**: Organized by our predefined trait names. Each trait directory holds 1-11 cohort datasets related to this trait, which were programmatically searched under specific criteria and downloaded from [the GEO database](https://www.ncbi.nlm.nih.gov/geo/) using [Entrez Utilities](https://www.ncbi.nlm.nih.gov/books/NBK25501/). Each cohort dataset is stored as a folder named after the GEO Series (GSE) number, such as 'GSE98578'. Each cohort folder contains `.gz` files, typically one SOFT file and one matrix file, though occasionally there are multiple SOFT files or matrix files.
+   - **TCGA folder**: Directly downloaded from [the TCGA Hub](https://xenabrowser.net/datapages/?host=https%3A%2F%2Ftcga.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443) of [the UCSC Xena platform](https://xena.ucsc.edu/), organized by different types of cancer. We preserve the original folder names from the website, without strictly matching our predefined trait names. Each trait (cancer) folder contains a clinicalMatrix file storing sample clinical features, and a PANCAN.gz file storing gene expression data.
+<a id="preprocessing-results-structure"></a>
+**3. Preprocessing Results Structure**:
+   The 'output/preprocess/' folder is organized by predefined trait names. For each trait, it holds the clinical data, gene expression data, and the final linked data that are successfully preprocessed for each cohort into 3 separate CSV files. These CSV files are saved in '{trait_name}/clinical_data/', '{trait_name}/gene_data/', and '{trait_name}/' respectively, with the same filename '{cohort_ID}.csv'.
+   For GEO cohorts, the cohort ID is the Series number (GSE); for TCGA, since there is at most one TCGA cohort related to each trait, we directly use 'Xena' as the cohort ID.
+   Additionally, each trait subfolder contains a JSON file, which stores metadata about dataset filtering and preprocessing outcomes of all related cohorts.
+#### Example Data Formats:
+   **a. Gene Expression Data** (stored in `{trait_name}/gene_data/{cohort_ID}.csv`):
+   This file contains gene expression values with gene symbols as rows and sample IDs as columns:
+   ```
+            GSM7920782  GSM7920783  GSM7920784  ...  GSM7920825
+   A2M          13.210      13.238      14.729  ...       7.359
+   ACVR1C        5.128       5.337       5.611  ...       8.151
+   ADAM12        9.807      12.374       9.953  ...       9.266
+   ...             ...         ...         ...  ...         ...
+   ZEB2          9.534      10.488      10.553  ...       8.151
+   ```
+   **b. Clinical Data** (stored in `{trait_name}/clinical_data/{cohort_ID}.csv`):
+   This file contains clinical information with trait names as rows and sample IDs as columns:
+   ```
+                 GSM7920782  GSM7920783  ...  GSM7920825
+   Breast_Cancer        1.0         1.0  ...         0.0
+   Age                 49.0        44.0  ...        74.0
+   Gender               0.0         0.0  ...         1.0
+   ```
+   **c. Linked Dataset** (stored in `{trait_name}/{cohort_ID}.csv`):
+   This file combines clinical and gene expression data with samples as rows and all features (clinical and gene) as columns:
+   ```
+              Breast_Cancer    Age  Gender     A2M    ACVR1C    ADAM12  ...     ZEB2
+   GSM7920782           1.0   49.0     0.0  13.210     5.128     9.807   ...   9.534
+   GSM7920783           1.0   44.0     0.0  13.238     5.337    12.374   ...  10.488
+   ...                   ...   ...     ...     ...       ...       ...   ...     ...
+   GSM7920825           0.0   74.0     1.0   7.359     8.151     9.266   ...   8.151
+   ```
+   **d. Cohort Information** (stored in `{trait_name}/cohort_info.json`):
+This file contains outputs of dataset filtering (initial filtering and quality verification), and metadata about the preprocessing outcomes of each cohort related to the trait:
+   ```json
+   {
+   "GSE207847":
+     {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 60},
+   "GSE153316":
+     {...},
+   ...
+   }
+   ```
+   Each cohort entry contains the following fields:
+   - `is_gene_available` and `is_trait_available`: Indicate whether the dataset has the relevant gene expression and trait information, respectively. `is_available` is true if both are available.
+   - `is_biased`: Indicates whether the trait distribution is severely biased. For example, if a dataset about breast cancer treatment only contains positive samples, it would be considered biased and not usable for trait-gene association studies.
+   - `is_usable`: True if `is_available` is true and `is_biased` is false.
+   - `has_age` and `has_gender`: Indicate whether the dataset contains the samples' age and gender information, respectively.
+   - `sample_size`: Records the number of samples in the final linked dataset, after discarding samples with too many missing features.
+<a id="regression-results-structure"></a>
+**4. Regression Results Structure**:
+   The 'output/regress/' folder is also organized by predefined trait names. It holds the regression analysis outputs for all GTA analysis problems in our benchmark that involve the same trait. These problems are uniquely identified by a trait-condition pair.
+   The analysis output for each problem is stored in a file named "significant_genes_condition_{condition name}.json", where the condition name is either a predefined trait name, or 'Age', 'Gender', or 'None'. A 'None' condition represents an unconditional problem—"What are the significant genes related to this trait?"—without considering the influence of any conditions.
+   Each JSON file contains:
+   ```json
+   {
+       "significant_genes": {
+           "Variable": ["TRIB1", "MTMR14", "ANKFY1", ...],
+           "Coefficient": [-3.6007, 2.7751, -2.5880, ...],
+           "Absolute Coefficient": [3.6007, 2.7751, 2.5880, ...]
+       },
+       "cv_performance": {...}
+   }
+   ```
+   - `significant_genes`: A dictionary with three keys:
+     - `Variable`: List of gene symbols identified as significant
+     - `Coefficient`: The corresponding coefficients in the trained regression model
+     - `Absolute Coefficient`: The absolute values of these coefficients
+   The gene symbols are ranked by importance (absolute coefficient in Lasso models). The `cv_performance` part is used mainly for model validation and diagnostics, not part of our benchmark evaluation.
+**5. Metadata Structure**:
+   - `task_info.json`: Contains full specifications for the GTA analysis problems in our benchmark, and domain knowledge about gene-trait associations. For each trait, it includes:
+      ```json
+      {
+          "Acute_Myeloid_Leukemia":
+          {
+              "related_genes": ["DNMT3A", "FLT3", "CEBPA", "TET2", "KIT", ... ],
+              "conditions": ["Age", "Gender", "Eczema", ... ]
+          },
+          "Adrenocortical_Cancer": {
+              ...
+          }
+      }
+      ```
+     - `related_genes`: A list of genes known to be associated with the trait, sourced from [the Open Targets Platform](https://platform.opentargets.org/downloads)
+     - `conditions`: The list of conditions paired with the trait to form the GTA analysis problems in our benchmark
+   - `gene_synonym.json`: Stores the mapping from common acronyms of human gene symbols to their standard symbols, sourced from [the NCBI Gene FTP Site](https://ftp.ncbi.nlm.nih.gov/gene/DATA/). This is useful for normalizing gene symbols during preprocessing to prevent inaccuracies arising from different gene naming conventions. Standard symbols are mapped to themselves.
+      ```json
+      {
+          "Acronym_1": "Standard_Symbol",
+          "Acronym_2": "Standard_Symbol",
+          "Standard_Symbol": "Standard_Symbol",
+          ...
+      }
+      ```
+### The Code Part
+```
+./
+├── code/                # Analysis code
+│   ├── Trait_1/
+│   │   ├── GSE12345.ipynb
+│   │   ├── Xena.ipynb
+│   │   └── ...
+│   ├── ...
+│   └── regress.py       # Regression analysis implementation
+│
+├── tools/               # Function tools for gene expression data analysis
+├── utils/               # Helper functions for experimentation and evaluation
+├── download/            # Scripts for downloading datasets
+├── datasheet.md         # Datasheets for Datasets documentation
+├── metadata.json        # Croissant metadata in JSON-LD format
+└── requirements.txt     # Package dependencies
+```
+The code part of the benchmark includes:
+- **code/**: Contains our code for gene expression data analysis. The main part is the code for preprocessing each cohort dataset, organized by predefined trait names. We provide the code as Jupyter Notebook files with the name '{cohort_ID}.ipynb', showing the output of each step to facilitate interactive analysis. `regress.py` implements our regression analysis method in fixed logic, for solving the GTA analysis problems in our benchmark.
+- **tools/**: Contains the function tools that are accessible to both human bioinformaticians and LLM agents for gene expression data analysis.
+- **utils/**: Contains the helper functions used for this project outside of the data analysis tasks, e.g., experiment logging, evaluation metrics, etc.
+- **download/**: Contains the scripts for programmatically searching and downloading input gene expression datasets, and acquiring domain knowledge files from public repositories. It also includes the script for selecting important trait-condition pairs to form our GTA analysis problems.
+- **Documentation files**: `datasheet.md` provides the [Datasheets for Datasets](https://arxiv.org/abs/1803.09010) documentation of our benchmark, and `metadata.json` provides [the Croissant metadata](https://github.com/mlcommons/croissant) in [JSON-LD](https://json-ld.org/) format.
+<a id="installation"></a>
+## 📥 Installation
+**1. Download the dataset**
+- **For the GitHub version**
+    (1). Clone this repository
+      ```bash
+      git clone https://github.com/Liu-Hy/GenoTEX.git
+      cd GenoTEX
+      ```
+    (2). Download the data folders ('metadata', 'input', 'output') from the provided cloud storage links, and place them in the root directory of this repository.
+- **For the bundled version**
+  Download the dataset folder containing code and data directly from the data platform.
+**2. (For Kaggle users only) Recompress files**
+Kaggle automatically unzips all `.gz` files, but our code requires certain files to remain compressed. Run the provided script to recompress these files (this will also save significant disk space):
+   ```bash
+   python recompress_files.py
+   ```
+**3. Create and activate a conda environment**
+   ```bash
+   conda create -n genotex python=3.10
+   conda activate genotex
+   pip install -r requirements.txt
+   ```
+<a id="usage"></a>
+## 💻 Usage
+### Exploring the Benchmark
+The GenoTEX benchmark code is organized into two complementary components. First, you'll find Jupyter notebooks in the `./code/{trait_name}/` directories that handle dataset-specific preprocessing. These notebooks prepare individual datasets by cleaning, standardizing, and integrating the data, but they don't perform the actual association studies.
+The statistical analysis that identifies genes associated with traits is centralized in the `./code/regress.py` script. This script automatically selects the optimal cohort(s) from all preprocessed datasets for each problem, applies appropriate statistical models, and performs hyperparameter tuning to identify significant genes.
+This design separates data preparation from statistical modeling, enabling consistent methodology across all analyses while maximizing statistical power through automatic cohort selection. To run the complete pipeline, first execute the preprocessing notebooks for individual datasets, then run the regress.py script to perform association studies across all problems.
+### Evaluating Your Own Methods
+After developing your automated method for gene expression data analysis, you can evaluate it against our benchmark:
+1. Ensure your method produces output following the same format as our benchmark. Your output should be organized in the same structure as our `./output` directory, with:
+   - Preprocessed datasets and JSON files in the same folder structure and file format as described in [Preprocessing Results Structure](#preprocessing-results-structure)
+   - Regression results with gene lists ranked by importance, in the same JSON format as described in [Regression Results Structure](#regression-results-structure)
+2. Run the evaluation script:
+   ```bash
+   python eval.py -p {prediction_directory} -r {reference_directory} -t selection preprocessing analysis -s gene clinical linked
+   ```
+   Where:
+   - `-p`, `--pred-dir`: Path to the directory containing your prediction results, required.
+   - `-r`, `--ref-dir`: Path to the directory containing reference benchmark results (default: "./output")
+   - `-t`, `--tasks`: Specific tasks to evaluate: "selection" (dataset filtering and selection), "preprocessing" (data preprocessing), "analysis" (statistical analysis) - omit to evaluate all tasks
+   - `-s`, `--preprocess-subtasks`: Specific preprocessing aspects to evaluate: "gene" (expression data), "clinical" (trait data), "linked" (final linked data) - omit to evaluate all subtasks
+The evaluation produces metrics for each task:
+- **Dataset selection and filtering**: Evaluates the ability to identify useful datasets and select the optimal ones for analysis
+- **Data preprocessing**: Measures how well the method processes and integrates data from different sources
+- **Statistical analysis**: Assesses the accuracy of identifying significant genes related to traits
+The script will report errors arising from non-conformance in format, but you will need to correct any formatting issues to ensure accurate evaluation.
+<a id="citation"></a>
+## 📝 Citation
+If you use GenoTEX in your research, please cite our paper:
+```bibtex
+@misc{liu2025genotex,
+      title={GenoTEX: A Benchmark for Automated Gene Expression Data Analysis in Alignment with Bioinformaticians},
+      author={Haoyang Liu and Shuyu Chen and Ye Zhang and Haohan Wang},
+      year={2025},
+      eprint={2406.15341},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2406.15341},
+}
+```
+<a id="license"></a>
+## ⚖️ License
+The GenoTEX dataset is released under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which allows for broad usage while protecting the rights of the creators. The authors bear full responsibility for ensuring that the dataset adheres to this license and for any potential violations of rights. For the full license text, please see the [LICENSE.md](./LICENSE.md) file in this repository.

datasheet.md ADDED Viewed

	@@ -0,0 +1,239 @@

+# GenoTEX Datasheet
+## Motivation
+**For what purpose was the dataset created?**
+The GenoTEX dataset was created to support the evaluation and development of automated methods for gene expression data analysis, particularly for LLM-based agents. In biomedical research, gene expression analysis is crucial for understanding biological mechanisms and advancing clinical applications such as disease marker identification and personalized medicine. However, these analyses are often repetitive, labor-intensive, and prone to errors, leading to significant time and financial burdens on research teams (estimated at around $848.3 million annually, with costs expected to increase at a CAGR of 12% to 16% by 2030). GenoTEX aims to facilitate the advancement of AI methods capable of automating these complex tasks, addressing the need for more efficient and cost-effective data analysis solutions in genetics research.
+**Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?**
+The dataset was created by a team of researchers led by Haohan Wang from the UIUC DREAM Lab, with core members including Haoyang Liu, Shuyu Chen, and Ye Zhang. The project was conducted as part of their research in AI4Science, specifically focusing on AI-driven methods for biomedical data analysis.
+**Who funded the creation of the dataset?**
+This research was supported by the National AI Research Resource (NAIRR) under grant number 240283. An initial version of this work was supported by the Accelerate Foundation Models Research (AFMR) initiative from Microsoft Research.
+## Composition
+**What do the instances that comprise the dataset represent?**
+The dataset represents 1,384 gene-trait association (GTA) analysis problems, each uniquely identified by a (trait, condition) pair. Each problem represents a scientific inquiry to identify significant genes associated with a specific trait (e.g., a disease) while accounting for the influence of a condition (e.g., age, gender, or another trait). The condition is either another trait, or 'Age', 'Gender', or 'None' for unconditional problems.
+**How many instances are there in total (of each type, if appropriate)?**
+The dataset includes:
+- 1,384 GTA analysis problems (132 unconditional problems and 1,252 conditional problems)
+- 911 input datasets from GEO and TCGA related to 132 traits
+- 41.5 GB of input data with 152,415 total samples (average of 167 samples per dataset)
+- 237,907 lines of analysis code (average 261 lines per dataset)
+**Does the dataset contain all possible instances or is it a sample from a larger set?**
+The dataset is a sample from a larger set. Our [sampling strategy](#sampling-strategy) is answered in a following question.
+**What data does each instance consist of?**
+For each problem, the dataset contains:
+1. Input data: Raw gene expression datasets from public databases (GEO and TCGA) associated with the relevant trait (and condition, if applicable). The raw data includes gene expression measurements from multiple samples, along with clinical information about these samples.
+2. Analysis code: Annotated code for data preprocessing and statistical analysis
+3. Output data: Preprocessed datasets and the significant genes identified from statistical analyses
+Note that each GTA analysis problem reuses datasets related to its trait and condition. For example, if problem_1 is (trait_A, trait_B) and problem_2 is (trait_A, trait_C), then both problems use the preprocessed datasets for trait_A.
+**Is there a label or target associated with each instance?**
+Yes, each GTA analysis problem has associated "reference answer" in the form of significant genes identified by expert bioinformaticians following a standardized analysis pipeline. These are stored in JSON files with gene symbols, their coefficients, and absolute coefficients in the trained regression model.
+**Is any information missing from individual instances?**
+Some datasets may have missing information, such as age or gender data for certain samples. The `cohort_info.json` files document which additional clinical features (e.g., age, gender) are available for each dataset.
+**Are relationships between individual instances made explicit?**
+Yes, relationships between problems, conditions, and traits are explicitly documented in the metadata. The `task_info.json` file maps each trait to its related genes and conditions, making the relationship structure clear.
+**Are there recommended data splits?**
+No, GenoTEX does not specify training/validation/testing splits. The benchmark evaluates automated methods for gene expression data analysis, which typically employ agents that leverage the reasoning and programming capabilities of foundation models, either with or without additional fine-tuning. Since these methods do not rely on supervised learning from the benchmark itself, traditional data splits are unnecessary.
+**Are there any errors, sources of noise, or redundancies in the dataset?**
+The dataset acknowledges inherent ambiguity in gene selection due to specific choices made during cohort-specific feature encoding, where multiple reasonable approaches often exist. However, the high Inter-Annotator Agreement (IAA) with an F₁ score of 94.73% for dataset filtering and AUROC score of 0.89 demonstrates high consistency among annotators, validating the reliability of the benchmark.
+**Is the dataset self-contained, or does it link to or otherwise rely on external resources?**
+The dataset is mostly self-contained, with all necessary components included. The raw data is sourced from public repositories (GEO and TCGA), and gene-trait associations and gene synonym mappings are included in the metadata directory. The original sources (GEO, TCGA, Open Targets Platform, NCBI Genes database) are still maintained by their respective organizations and should remain available, though the specific versions used in GenoTEX are captured in the dataset to ensure reproducibility.
+**Does the dataset contain data that might be considered confidential?**
+No, the dataset does not contain confidential data. All source data was obtained from public repositories, and the authors have ensured that no personally identifiable information is included.
+**Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?**
+No, the dataset contains gene expression data and clinical information from anonymized samples for scientific research purposes and does not contain offensive or anxiety-inducing content.
+**Does the dataset identify any subpopulations?**
+The dataset includes demographic information such as age and gender where available in the source data. The `cohort_info.json` files indicate which datasets contain age and gender information for their samples.
+**Is it possible to identify individuals from the dataset?**
+No, the dataset does not allow for the identification of individuals. Throughout the curation process, the authors carefully examined each dataset to ensure the absence of personally identifiable information and compliance with all relevant standards.
+**Does the dataset contain data that might be considered sensitive in any way?**
+The dataset contains health-related data (gene expression and disease status), but these are anonymized and aggregated from public sources. The authors have taken care to ensure compliance with ethical standards for working with such data.
+## Collection Process
+**How was the data associated with each instance acquired?**
+The input data was obtained from public gene expression databases: The Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Domain knowledge was acquired from the Open Targets Platform for gene-trait associations and the NCBI Genes database for gene synonym mapping.
+The analysis code and results were directly created by a team of bioinformaticians following standardized guidelines developed by the research team.
+**What mechanisms or procedures were used to collect the data?**
+The input datasets were programmatically searched and downloaded from the GEO database using Entrez Utilities and from the TCGA Hub of the UCSC Xena platform. The scripts used for this process are included in the './download/' directory of the repository.
+For the analysis part, a team of 4 researchers designed the problem list and developed example code for solving GTA analysis problems. They extracted common patterns from these examples to develop guidelines for the entire benchmark. Then, a team of 9 bioinformaticians was assembled and trained to analyze the complete set of problems following these guidelines. They submitted their analysis code and results weekly over a period of 20 weeks.
+<a id="sampling-strategy"></a>**If the dataset is a sample from a larger set, what was the sampling strategy?**
+The sampling strategy involved two aspects:
+1. **Problem selection**: The selection of trait-condition pairs involved both domain expertise and data-driven approaches. The researchers applied manually designed rules to determine which pairs to include or exclude based on trait categories. For undecided pairs, they measured trait-condition association by calculating Jaccard similarity between gene sets related to each trait and condition, using data from the Open Targets Platform. They selected pairs with Jaccard similarity exceeding 0.1, as these likely share underlying genetic mechanisms, offering valuable insights into complex trait-condition interactions.
+2. **Dataset collection**: For each trait, the researchers selectively downloaded gene expression datasets:
+   - **GEO data**: For each trait, only the top 10 cohort datasets that satisfied specific criteria were downloaded, using GEO's default ranking that considers recency and quality. The search was limited to GEO's manually curated subset (GEO DataSets) with the following criteria:
+     - Sample size: 30-10,000
+     - Organism: Human
+     - Publication year: 2010-2025
+     - Data types: "expression profiling by array", "expression profiling by high throughput sequencing", "genome variation profiling by high throughput sequencing", "genome variation profiling by snp array", "snp genotyping by snp array", "third party reanalysis"
+     - Technical requirements: Must have both a matrix file (>0.1MB, <100MB) and a family file (<100MB)
+   - **TCGA data**: All available data from the UCSC Xena Data Hubs were included due to their reasonable size and high quality.
+**Who was involved in the data collection process and how were they compensated?**
+The data collection and analysis involved a core team of 4 researchers who designed the problem list and guidelines, and a team of 9 bioinformaticians who conducted the analyses. Information about compensation is not provided in the available materials.
+**Over what timeframe was the data collected?**
+The analysis code and results were developed and submitted by bioinformaticians over two phases totaling 20 weeks: the main development phase from February to May 2024, and a supplementary phase in January 2025 to incorporate more recent data.
+**Were any ethical review processes conducted?**
+The authors mention engaging in extensive discussions and consultations to address ethical considerations and legal requirements throughout the curation process. They carefully examined each dataset to ensure the absence of personally identifiable information and compliance with all relevant standards.
+## Preprocessing/cleaning/labeling
+**Was any preprocessing/cleaning/labeling of the data done?**
+Yes, extensive preprocessing was performed on the raw gene expression data according to a standardized pipeline. The preprocessing steps included:
+1. **Dataset filtering and selection**: Filtering out irrelevant datasets and selecting the best dataset for each GTA analysis problem based on relevance, quality, and sample size.
+2. **Gene expression data preprocessing**: For microarray data, starting with raw datasets identified by probe IDs and mapping these to gene symbols using platform-specific gene annotation data. For RNA-seq data, handling sequence reads that require alignment to a reference genome. Normalizing and deduplicating gene symbols by querying a local gene database to prevent inaccuracies arising from different gene naming conventions.
+3. **Trait data extraction**: Identifying attributes containing variable information of interest, designing conversion rules, and writing functions to encode attributes into binary or numerical variables. This often required inferring information based on an understanding of the data measurement and collection process described in the metadata, combined with necessary domain knowledge.
+4. **Data linking**: Linking the preprocessed gene data with the extracted trait data based on sample IDs to create a data table containing both genetic and clinical features for the same samples.
+The preprocessing also involved common operations such as missing value imputation and column matching.
+**Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data?**
+Yes, both the raw data (in the 'input' directory) and the preprocessed data (in the 'output/preprocess' directory) are included in the dataset.
+**Is the software used to preprocess/clean/label the data available?**
+Yes, the code used for preprocessing is available in the 'code/' directory of the repository, organized by predefined trait names. Each file (e.g., 'GSE12345.ipynb') contains the code for preprocessing a specific cohort dataset. The 'tools/' directory contains function tools used for gene expression data analysis.
+## Uses
+**Has the dataset been used for any tasks already?**
+Yes, GenoTEX has been used to evaluate GenoAgent, a team of LLM-based agents proposed by the authors as a baseline method for automating gene expression data analysis. The evaluation assessed performance on three tasks: dataset selection, data preprocessing, and statistical analysis.
+**Is there a repository that links to any or all papers or systems that use the dataset?**
+The GitHub repository (https://github.com/Liu-Hy/GenoTEX) serves as the primary hub for the dataset and includes references to the associated paper.
+**What (other) tasks could the dataset be used for?**
+GenoTEX could be used for:
+1. Developing and evaluating automated methods for gene expression data analysis
+2. Training machine learning models to identify disease-associated genes
+3. Studying the influence of conditions on gene-trait relationships
+4. Benchmarking different approaches to dataset selection, preprocessing, and statistical analysis in genomics
+5. Teaching and educational purposes in bioinformatics and computational genomics
+**Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?**
+GenoTEX includes expert-curated annotations following best practices in the bioinformatics community. However, the set of selected genes is sensitive to specific choices made during cohort-specific feature encoding, where multiple reasonable approaches often exist. This inherent ambiguity in gene selection should be considered when evaluating methods against this benchmark.
+**Are there tasks for which the dataset should not be used?**
+GenoTEX should NOT be used for:
+1. Making clinical decisions without additional validation through biological experiments or clinical trials
+2. Claiming definitive "ground truth" about gene-disease relationships, as these analyses provide valuable insights but must ultimately be combined with interventional biological experiments or clinical trials to confirm the significance of identified genes
+3. Developing methods that ignore the inherent complexity and ambiguity in gene expression analysis
+## Distribution
+**Will the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created?**
+Yes, GenoTEX is publicly available for research purposes.
+**How will the dataset will be distributed? Does the dataset have a digital object identifier (DOI)?**
+GenoTEX is distributed in two main ways:
+1.  **GitHub Repository + Cloud Storage**: The [GitHub repository](https://github.com/Liu-Hy/GenoTEX) hosts the code and documentation, with data accessible via cloud storage links (Google Drive/Baidu Cloud Disk). This is good for accessing the latest code updates.
+2.  **Complete Bundled Datasets**: Available on [Kaggle](https://www.kaggle.com/datasets/haoyangliu14/genotex-llm-agent-benchmark-for-genomic-analysis) and [Hugging Face Hub](https://huggingface.co/datasets/Liu-Hy/GenoTEX), containing both code and data for convenience.
+The dataset's DOI is: [https://doi.org/10.34740/kaggle/dsv/11309048](https://doi.org/10.34740/kaggle/dsv/11309048)
+**When will the dataset be distributed?**
+GenoTEX has already been distributed, as indicated by the availability of the GitHub repository and the publication of the associated paper on arXiv.
+**Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)?**
+Yes, GenoTEX is released under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which allows for broad usage while protecting the rights of the creators.
+**Have any third parties imposed IP-based or other restrictions on the data associated with the instances?**
+The original data sources (GEO, TCGA, Open Targets Platform, NCBI Genes database) are public resources with their own terms of use, but they generally allow for research use with proper attribution.
+**Do any export controls or other regulatory restrictions apply to the dataset or to individual instances?**
+No, there are no export controls or other regulatory restrictions that apply to GenoTEX. The dataset consists of publicly available gene expression data and analysis code, which are not subject to special regulatory controls.
+## Maintenance
+**Who will be supporting/hosting/maintaining the dataset?**
+GenoTEX is hosted on GitHub and maintained by Haoyang Liu (the first author) and other researchers from the UIUC DREAM Lab.
+**How can the owner/curator/manager of the dataset be contacted?**
+Users are welcome to discuss issues and/or make pull requests on the GitHub repository. For specific inquiries or collaborations, users can contact [email protected].
+**Is there an erratum?**
+We do not provide an explicit erratum. However, we will address any identified or reported issues in the dataset and make timely updates. We will provide changelogs to document the updates between stable releases.
+**Will the dataset be updated?**
+Yes, we will continue to update GenoTEX based on feedback from the community and our subsequent research findings related to the dataset.
+**If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances?**
+GenoTEX contains anonymized data from public repositories with no personally identifiable information. We have not set specific retention limits as this data is already publicly available.
+**If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?**
+Contributions can be made through the GitHub repository using standard mechanisms such as pull requests.