---
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](https://figshare.com/articles/dataset/Feng_Tang_Dede_et_al_2022/19398332)
- **Paper:** [Sci. Adv. 8, eabm6638 (2022)](https://doi.org/10.1126/sciadv.abm6638)

## 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

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

[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

<!-- Motivation for the creation of this dataset. -->

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](https://figshare.com/articles/dataset/Feng_Tang_Dede_et_al_2022/19398332?file=34466978)

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

[Binary_essentiality_calls_analysis_Feng_et_al](https://figshare.com/articles/dataset/Feng_Tang_Dede_et_al_2022/19398332?file=34466987)

[More Information Needed]

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

[More Information Needed]

### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

[More Information Needed]

#### Who are the annotators?

<!-- This section describes the people or systems who created the annotations. -->

[More Information Needed]

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

## Citation [optional]

**BibTeX:**

```txt
@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]

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Dataset Card Authors [optional]

[More Information Needed]

## Dataset Card Contact

dwb2023