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README.md

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 ---
-license: mit
+language:
+- en
+license:
+- mit
+task_categories:
+- genomics
+- bioinformatics
+task_ids:
+- variant-analysis
+- transcript-expression
+- cancer-genomics
+pretty_name: TCGA Cancer Variant and Clinical Data
+tags:
+- cancer-genomics
+- variant-calling
+- transcriptomics
+- clinical-data
+dataset_info:
+  features:
+  - name: aliquot_id
+    dtype: string
+  - name: transcript_id
+    dtype: string
+  - name: mutated_protein
+    dtype: string
+  - name: wildtype_protein
+    dtype: string
+  - name: wgs_aliquot_id
+    dtype: string
+  - name: Cancer Type
+    dtype: string
+  - name: Cancer Stage
+    dtype: string
+  - name: Donor Survival Time
+    dtype: float
+  - name: Donor Vital Status
+    dtype: string
+  - name: Donor Age at Diagnosis
+    dtype: float
+  - name: Tumour Grade
+    dtype: string
+  - name: Donor Sex
+    dtype: string
+  - name: Histology Abbreviation
+    dtype: string
+  config_name: default
+  splits:
+  - name: train
+    num_bytes: 
+    num_examples: 
+dataset_files:
+  - name: protein_sequences_metadata.tsv
+    description: This file contains metadata on protein sequences, including transcript IDs, mutated protein sequences, wildtype sequences, and clinical information related to cancer studies from TCGA.
+    format: tsv
+    url: ./protein_sequences_metadata.tsv
 ---
+
+# TCGA Cancer Variant and Clinical Data
+
+## Dataset Description
+
+This dataset combines genetic variant information at the protein level with clinical data from The Cancer Genome Atlas (TCGA) project, curated by the International Cancer Genome Consortium (ICGC). It provides a comprehensive view of protein-altering mutations and clinical characteristics across various cancer types.
+
+### Dataset Summary
+
+The dataset includes:
+
+- Protein sequence data for both mutated and wildtype proteins
+- Clinical data for each patient, including cancer type, stage, survival time, and demographic information
+- Unique identifiers linking genomic data to clinical information
+
+### Supported Tasks and Leaderboards
+
+This dataset can support various tasks in cancer genomics and precision medicine, including:
+
+- Analysis of protein-altering mutations and their impact on cancer progression
+- Correlation studies between specific protein mutations and clinical outcomes
+- Cancer subtype classification based on genetic and clinical features
+- Survival analysis incorporating genetic and clinical data
+- Identification of potential biomarkers for cancer prognosis or treatment response
+
+### Languages
+
+The dataset is in English, but primarily consists of protein sequences, numerical data, and standardized clinical terms.
+
+## Dataset Structure
+
+### Data Instances
+
+Each row in the dataset represents a unique combination of a patient sample and a transcript. Here's an example entry:
+
+```sh
+{
+  'aliquot_id': '8fb9496e-ddb8-11e4-ad8f-5ed8e2d07381',
+  'transcript_id': 'ENST00000512632',
+  'mutated_protein': 'MACPALGLEALQPLQPEPPPE...', # (truncated for brevity)
+  'wildtype_protein': 'MACPALGLEALQPLQPEPPPE...', # (truncated for brevity)
+  'wgs_aliquot_id': '80ab6c08-c622-11e3-bf01-24c6515278c0',
+  'Cancer Type': 'Liver Cancer - RIKEN, JP',
+  'Cancer Stage': '2',
+  'Donor Survival Time': 1440.0,
+  'Donor Vital Status': 'deceased',
+  'Donor Age at Diagnosis': 67.0,
+  'Tumour Grade': 'I',
+  'Donor Sex': 'male',
+  'Histology Abbreviation': 'Liver-HCC'
+}
+```
+
+### Data Fields
+
+- `aliquot_id`: Unique identifier for the RNA sequencing sample
+- `transcript_id`: Ensembl transcript ID
+- `mutated_protein`: Amino acid sequence of the mutated protein
+- `wildtype_protein`: Amino acid sequence of the wildtype (non-mutated) protein
+- `wgs_aliquot_id`: Identifier for the whole genome sequencing data
+- `Cancer Type`: Type and origin of the cancer
+- `Cancer Stage`: Stage of the cancer at diagnosis
+- `Donor Survival Time`: Survival time of the patient in days
+- `Donor Vital Status`: Whether the patient is alive or deceased
+- `Donor Age at Diagnosis`: Age of the patient at diagnosis
+- `Tumour Grade`: Grade of the tumor
+- `Donor Sex`: Sex of the patient
+- `Histology Abbreviation`: Abbreviation for the cancer histology
+
+### Data Splits
+
+This dataset does not have explicit splits. All data is contained in a single table.
+
+## Dataset Creation
+
+### Source Data
+
+The dataset was derived from the following ICGC/TCGA sources:
+
+1. Normalized transcript expression data:
+
+   ```r
+   s3://icgc25k-open/PCAWG/transcriptome/transcript_expression/pcawg.rnaseq.transcript.expr.tpm.tsv.gz
+   ```
+
+2. Metadata linked to aliquot_id:
+
+   ```r
+   s3://icgc25k-open/PCAWG/transcriptome/metadata/rnaseq.extended.metadata.aliquot_id.V4.tsv.gz
+   ```
+
+3. SNV and Indel data:
+
+   ```r
+   s3://icgc25k-open/PCAWG/consensus_snv_indel/final_consensus_snv_indel_passonly_icgc.public.tgz
+   ```
+
+### Data Processing
+
+The data processing involved several steps:
+
+1. Downloading and extracting the source files
+2. Parsing VCF files to extract variant information
+3. Translating DNA variants to protein sequences
+4. Combining the protein sequence data with clinical data
+
+The script `set_tcga_data.py` was used to perform these processing steps.
+
+## Additional Information
+
+### Dataset Curators
+
+This dataset was curated by [Your Name/Organization] based on data from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project.
+
+### Licensing Information
+
+This dataset is released under the MIT License. Please note that usage of the source TCGA data may be subject to additional terms and conditions.
+
+### Citation Information
+
+If you use this dataset, please cite:
+
+[Your citation information]
+
+And also cite the original PCAWG project:
+
+The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020). https://doi.org/10.1038/s41586-020-1969-6
+
+### Contributions
+
+Thanks to the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium for making the original data available.

protein_sequences_metadata.tsv

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