README.md

---
dataset_info:
  features:
  - name: GENE_CLONE
    dtype: string
  - name: GENE
    dtype: string
  - name: WT_T0_XF498
    dtype: int64
  - name: WT_T21A_XF498
    dtype: int64
  - name: WT_T21B_XF498
    dtype: int64
  - name: LKB1 KO_T0_XF498
    dtype: int64
  - name: LKB1 KO_T21A_XF498
    dtype: int64
  - name: LKB1 KO_T21B_XF498
    dtype: int64
  - name: PTEN KO_T0_XF498
    dtype: int64
  - name: PTEN KO_T21A_XF498
    dtype: int64
  - name: PTEN KO_T21B_XF498
    dtype: int64
  - name: VHL KO_T0_XF498
    dtype: int64
  - name: VHL KO_T21A_XF498
    dtype: int64
  - name: VHL KO_T21B_XF498
    dtype: int64
  - name: WT_T0_XF646
    dtype: int64
  - name: WT_T21A_XF646
    dtype: int64
  - name: WT_T21B_XF646
    dtype: int64
  - name: CDH1 KO_T0_XF646
    dtype: int64
  - name: CDH1 KO_T21A_XF646
    dtype: int64
  - name: CDH1 KO_T21B_XF646
    dtype: int64
  - name: NF2 KO_T0_XF646
    dtype: int64
  - name: NF2 KO_T21A_XF646
    dtype: int64
  - name: NF2 KO_T21B_XF646
    dtype: int64
  - name: BAP1 KO_T0_XF646
    dtype: int64
  - name: BAP1 KO_T21A_XF646
    dtype: int64
  - name: BAP1 KO_T21B_XF646
    dtype: int64
  - name: WT_T0_XF804
    dtype: int64
  - name: WT_T21A_XF804
    dtype: int64
  - name: WT_T21B_XF804
    dtype: int64
  - name: ARID1A KD_T0_XF804
    dtype: int64
  - name: ARID1A KD_T21A_XF804
    dtype: int64
  - name: ARID1A KD_T21B_XF804
    dtype: int64
  - name: PBRM1 KO_T0_XF804
    dtype: int64
  - name: PBRM1 KO_T21A_XF804
    dtype: int64
  - name: PBRM1 KO_T21B_XF804
    dtype: int64
  - name: WT_T0_XF821
    dtype: int64
  - name: WT_T21A_XF821
    dtype: int64
  - name: WT_T21B_XF821
    dtype: int64
  - name: KEAP1 KO_T0_XF821
    dtype: int64
  - name: KEAP1 KO_T21A_XF821
    dtype: int64
  - name: KEAP1 KO_T21B_XF821
    dtype: int64
  - name: NF1 KO_T0_XF821
    dtype: int64
  - name: NF1 KO_T21A_XF821
    dtype: int64
  - name: NF1 KO_T21B_XF821
    dtype: int64
  - name: RB1 KO_T0_XF821
    dtype: int64
  - name: RB1 KO_T21A_XF821
    dtype: int64
  - name: RB1 KO_T21B_XF821
    dtype: int64
  - name: TP53 KO_T0_XF821
    dtype: int64
  - name: TP53 KO_T21A_XF821
    dtype: int64
  - name: TP53 KO_T21B_XF821
    dtype: int64
  - name: TP53BP1 KO_T0_XF443
    dtype: int64
  - name: TP53BP1 KO_T21A_XF443
    dtype: int64
  - name: TP53BP1 KO_T21B_XF443
    dtype: int64
  splits:
  - name: train
    num_bytes: 31839870
    num_examples: 71090
  download_size: 13068108
  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 PMC_35559673_table_s datasets

## 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 were originally published by Feng et al., *Sci. Adv.* 8, eabm6638 (2022), and are available via PubMed Central (PMC). The supplementary tables included in this dataset provide detailed data on raw counts, essentiality calls, Bayes factors, and synthetic lethality (SL) hits. The dataset supports research into genetic dependencies and potential therapeutic targets.

- **Curated by**: Feng et al., Sci. Adv. 8, eabm6638 (2022)
- **Funded by**: Likely supported by institutions affiliated with the authors.
- **Shared by**: Feng et al.
- **Language(s)**: Not applicable (biomedical dataset).
- **License**: CC BY 4.0

### Dataset Sources

- **Repository**: [PubMed Central](https://pubmed.ncbi.nlm.nih.gov/35559673/)
- **Paper**: [Sci. Adv. 8, eabm6638 (2022)](https://doi.org/10.1126/sciadv.abm6638)
- **Supplementary Materials**: [Tables S1-S7](https://www.science.org/doi/suppl/10.1126/sciadv.abm6638/suppl_file/sciadv.abm6638_tables_s1_to_s7.zip)

### Dataset Structure

This dataset consists of seven tables (S1-S7), each representing a different aspect of the CRISPR screen results:

1. **Table S1**: Raw counts for all CRISPR screens in this study.
   - **File Mapping**: `sciadv.abm6638_table_s1.xlsx`
   
2. **Table S2**: Binary essentiality calls matrix.
   - **File Mapping**: `sciadv.abm6638_table_s2.xlsx`
   
3. **Table S3**: Quantile-normalized Bayes factor (QBF) matrix.
   - **File Mapping**: `sciadv.abm6638_table_s3.xlsx`
   
4. **Table S5**: Total SL hits identified for each TSG KO screen.
   - **File Mapping**: `sciadv.abm6638_table_s5.xlsx`
   
5. **Table S6**: Shared SL hits across each TSG KO screen.
   - **File Mapping**: `sciadv.abm6638_table_s6.xlsx`
   
6. **Table S7**: Unique SL hits for each TSG KO screen.
   - **File Mapping**: `sciadv.abm6638_table_s7.xlsx`

### Dataset Creation

#### Curation Rationale

This dataset was curated to facilitate research into the vulnerabilities of cancer cells deficient in tumor suppressor genes. The binary essentiality calls, synthetic lethality (SL) hits, and other data allow researchers to explore genetic interactions that could serve as potential therapeutic targets. The methodology behind the CRISPR screens and SL hit identification was detailed by Feng et al. in their 2022 study.

#### Data Collection and Processing

Data were collected from genome-wide CRISPR screens performed on isogenic knockout cells. The data were processed to produce raw counts, binary essentiality calls, and genetic interaction matrices, including shared and unique synthetic lethal hits. 

Relevant references describing the data processing and methods can be found in the following sources:
- *Evaluation and design of genome-wide CRISPR/SpCas9 knockout screens* (PMID: 28655737)
- *High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities* (PMID: 26627737)
- *Identifying chemogenetic interactions from CRISPR screens with drugZ* (PMID: 31439014)

#### Who are the source data producers?

The data were produced by Feng et al., as part of their research published in *Science Advances*. The researchers were affiliated with academic institutions engaged in cancer genomics and CRISPR screening methodologies.

### Annotations

#### Annotation Process

Annotations were primarily focused on identifying shared and unique synthetic lethality hits across tumor suppressor knockout screens. Automated processing tools like CRISPR analysis pipelines were employed for initial hit identification, followed by manual validation based on genetic interactions.

#### Who are the annotators?

The original authors, including experts in CRISPR screening and cancer genomics, performed the annotations. No third-party annotations were added.

### Bias, Risks, and Limitations

The dataset is limited to specific cancer cell lines and tumor suppressor gene knockouts. As a result, the findings may not be generalizable across all cancer types. Users should exercise caution when interpreting results outside the experimental context.

### Recommendations

Users should consult the references provided to better understand the experimental design and limitations. The dataset is best suited for research applications in cancer genomics, genetic interactions, and therapeutic target discovery.

## Citation

**BibTeX:**

```txt
@article{
  doi:10.1126/sciadv.abm6638,
  author = {Xu Feng  and Mengfan Tang  and Merve Dede  and Dan Su  and Guangsheng Pei  and Dadi Jiang  and Chao Wang  and Zhen Chen  and Mi Li  and Litong Nie  and Yun Xiong  and Siting Li  and Jeong-Min Park  and Huimin Zhang  and Min Huang  and Klaudia Szymonowicz  and Zhongming Zhao  and Traver Hart  and Junjie Chen },
  title = {Genome-wide CRISPR screens using isogenic cells reveal vulnerabilities conferred by loss of tumor suppressors},
  journal = {Science Advances},
  volume = {8},
  number = {19},
  pages = {eabm6638},
  year = {2022},
  doi = {10.1126/sciadv.abm6638},
  URL = {https://www.science.org/doi/abs/10.1126/sciadv.abm6638},
  eprint = {https://www.science.org/doi/pdf/10.1126/sciadv.abm6638},
  abstract = {Exploiting cancer vulnerabilities is critical for the discovery of anticancer drugs. However, tumor suppressors cannot be directly targeted because of their loss of function. To uncover specific vulnerabilities for cells with deficiency in any given tumor suppressor(s), we performed genome-scale CRISPR loss-of-function screens using a panel of isogenic knockout cells we generated for 12 common tumor suppressors. Here, we provide a comprehensive and comparative dataset for genetic interactions between the whole-genome protein-coding genes and a panel of tumor suppressor genes, which allows us to uncover known and new high-confidence synthetic lethal interactions. Mining this dataset, we uncover essential paralog gene pairs, which could be a common mechanism for interpreting synthetic lethality. Moreover, we propose that some tumor suppressors could be targeted to suppress proliferation of cells with deficiency in other tumor suppressors. This dataset provides valuable information that can be further exploited for targeted cancer therapy. Whole-genome CRISPR screens uncover synthetic lethal interactions for tumor suppressors.}
}
```

## Dataset Card Contact

dwb2023