Update README.md

README.md

@@ -1,188 +1,190 @@
----
-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
-
+---
+language:
+- en
+license:
+- mit
+task_categories:
+- table-to-text
+task_ids:
+- entity-linking-retrieval
+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: null
+    num_examples: null
+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
+size_categories:
+- 10K<n<100K
+---
+
+# 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.