metadata

dataset_info:
  features:
    - name: GENE_CLONE
      dtype: string
    - name: GENE
      dtype: string
    - name: 293A_WT_T0_XF498
      dtype: int64
    - name: 293A_WT_T22-A_XF498
      dtype: int64
    - name: 293A_WT_T22-B_XF498
      dtype: int64
    - name: 293A_LKB1_T0_XF498
      dtype: int64
    - name: 293A_LKB1_T22-A_XF498
      dtype: int64
    - name: 293A_LKB1_T22-B_XF498
      dtype: int64
    - name: 293A_PTEN_T0_XF498
      dtype: int64
    - name: 293A_PTEN_T22-A_XF498
      dtype: int64
    - name: 293A_PTEN_T22-B_XF498
      dtype: int64
    - name: 293A_VHL_T0_XF498
      dtype: int64
    - name: 293A_VHL_T22-A_XF498
      dtype: int64
    - name: 293A_VHL_T22-B_XF498
      dtype: int64
    - name: 293A_WT_T0_XF_646
      dtype: int64
    - name: 293A_WT_T21_A_XF_646
      dtype: int64
    - name: 293A_WT_T21_B_XF_646
      dtype: int64
    - name: 293A_CDH1_T0
      dtype: int64
    - name: 293A_CDH1_T24_A_XF_646
      dtype: int64
    - name: 293A_CDH1_T24_B_XF_646
      dtype: int64
    - name: 293A_NF2_T0_XF_646
      dtype: int64
    - name: 293A_NF2_T24_A_XF_646
      dtype: int64
    - name: 293A_NF2_T24_B_XF_646
      dtype: int64
    - name: 293A_BAP1_16_T0_XF_646
      dtype: int64
    - name: 293A_BAP1_T25_A_XF_646
      dtype: int64
    - name: 293A_BAP1_T25_B_XF_646
      dtype: int64
    - name: 293A_WT_T0_XF_804
      dtype: int64
    - name: 293A_WT_T20_A_XF_804
      dtype: int64
    - name: 293A_WT_T20_B_XF_804
      dtype: int64
    - name: 293A_ARID1A_T0_XF_804
      dtype: int64
    - name: 293A_ARID1A_T21_A_XF_804
      dtype: int64
    - name: 293A_ARID1A_T21_B_XF_804
      dtype: int64
    - name: 293A_PBRM1_T0_XF_804
      dtype: int64
    - name: 293A_PBRM1_T25_A_XF_804
      dtype: int64
    - name: 293A_PBRM1_T25_B_XF_804
      dtype: int64
    - name: 293A_WT_T0_XF_821
      dtype: int64
    - name: 293A_WT_T21_A_XF_821
      dtype: int64
    - name: 293A_WT_T21_B_XF_821
      dtype: int64
    - name: 293A_KEAP1_T0_XF_821
      dtype: int64
    - name: 293A_KEAP1_T22_A_XF_821
      dtype: int64
    - name: 293A_KEAP1_T22_B_XF_821
      dtype: int64
    - name: 293A_NF1_T0_XF_821
      dtype: int64
    - name: 293A_NF1_T24_A_XF_821
      dtype: int64
    - name: 293A_NF1_T24_B_XF_821
      dtype: int64
    - name: 293A_RB1_T0_XF_821
      dtype: int64
    - name: 293A_RB1_T21_A_XF_821
      dtype: int64
    - name: 293A_RB1_T21_B_XF_821
      dtype: int64
    - name: 293A_TP53_T0_XF_821
      dtype: int64
    - name: 293A_TP53_T21_A_XF_821
      dtype: int64
    - name: 293A_TP53_T21_B_XF_821
      dtype: int64
    - name: 293A_TP53BP1_T0_XF_443
      dtype: int64
    - name: 293A_TP53BP1_T22-A_XF_443
      dtype: int64
    - name: 293A_TP53BP1_T22-B_XF_443
      dtype: int64
  splits:
    - name: train
      num_bytes: 31839870
      num_examples: 71090
  download_size: 13100019
  dataset_size: 31839870
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-4.0
tags:
  - biology
  - chemistry
  - medical

Dataset Card for Dataset Name

crispr-raw-read-counts: Table_S1_Raw_read_counts_master_Xu_Feng_Tm_sup_screens

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

[More Information Needed]

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_S1_Raw_read_counts_master_Xu_Feng_Tm_sup_screens

Data Collection and Processing

Binary_essentiality_calls_analysis_Feng_et_al

[More Information Needed]

Who are the source data producers?

[More Information Needed]

Annotations [optional]

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

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"
}

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Dataset Card Authors [optional]

[More Information Needed]

Dataset Card Contact

dwb2023