metadata

dataset_info:
  features:
    - name: 'Unnamed: 0'
      dtype: string
    - name: 293A_KEAP1_T22_AB
      dtype: int64
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      dtype: int64
    - name: 293A_TP53_T21_AB
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      dtype: int64
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    - name: 293A_NF2NUMBER2_3_T24_AB
      dtype: int64
    - name: 293A_g53BP1#1_T22_AB
      dtype: int64
  splits:
    - name: train
      num_bytes: 2770271
      num_examples: 18053
  download_size: 278773
  dataset_size: 2770271
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-4.0

Dataset Card for Dataset Name

crispr-binary-calls: Table_S2_binary_calls

Dataset Details

Dataset Description

This dataset contains the results of genome-wide CRISPR screens using isogenic knockout cells to uncover vulnerabilities in tumor suppressor-deficient cancer cells. The data was originally published by Feng et al., Sci. Adv. 8, eabm6638 (2022) and is available on Figshare.

Curated by: Feng et al., Sci. Adv. 8, eabm6638 (2022)
Funded by: Not explicitly specified, but likely supported by institutions associated with the authors.
Shared by: Feng et al.
Language(s) (NLP): Not applicable (this is a biomedical dataset).
License: CC BY 4.0

Dataset Sources [optional]

Repository: Figshare - Feng, Tang, Dede et al. 2022
Paper: Sci. Adv. 8, eabm6638 (2022)

Uses

Direct Use

This dataset can be used for identifying genetic dependencies and vulnerabilities in cancer research, especially related to tumor suppressor genes. Potential applications include:

Identification of potential therapeutic targets.
Understanding genetic interactions in cancer progression.
Training machine learning models for genomic data analysis.

Out-of-Scope Use

This dataset should not be used for:

Applications outside of research without proper domain expertise.
Misinterpretation of the results to derive clinical conclusions without appropriate validation.
Malicious use to generate unverified claims about genetic predispositions.

Dataset Structure

The dataset is organized with each column representing a different experimental condition, and each row representing the outcome of a CRISPR knockout experiment on a specific Tumor Suppressor gene or target.

Splits

Train: Contains the entirety of the dataset for analysis. No explicit validation or test splits are provided.

Dataset Creation

Curation Rationale

Confirm the methodology behind the binary essentiality calls in Genome-wide CRISPR Screens Using Isogenic Cells Reveal Vulnerabilities Conferred by Loss of Tumor Suppressors manuscript by Feng et al.

[More Information Needed]

Source Data

Table_S2_binary_calls.txt

Data Collection and Processing

Binary_essentiality_calls_analysis_Feng_et_al

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Who are the source data producers?

[More Information Needed]

Annotations [optional]

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation [optional]

BibTeX:

@article{ Hart2022, author = "Traver Hart and Merve Dede", title = "{Feng, Tang, Dede et al 2022}", year = "2022", month = "3", url = "https://figshare.com/articles/dataset/Feng_Tang_Dede_et_al_2022/19398332", doi = "10.6084/m9.figshare.19398332.v1" }

APA:

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Glossary [optional]

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More Information [optional]

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Dataset Card Authors [optional]

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Dataset Card Contact

dwb2023