Datasets:

dwb2023
/

pmc_35559673_table_s7

@@ -44,115 +44,86 @@ tags:
 - chemistry
 - medical
 ---
-# Dataset Card for Dataset Name
-<!-- Provide a quick summary of the dataset. -->
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
 ## Dataset Details
 ### Dataset Description
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-- **Curated by:** [More Information Needed]
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-<!-- Provide the basic links for the dataset. -->
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-## Uses
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-### Direct Use
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-### Out-of-Scope Use
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-### Source Data
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-### Annotations [optional]
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-#### Annotation process
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 ### Recommendations
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-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-## Citation [optional]
 **BibTeX:**
@@ -173,20 +144,6 @@ Users should be made aware of the risks, biases and limitations of the dataset.
 }
 ```
-## Glossary [optional]
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 - 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:**
 }
 ```
 ## Dataset Card Contact
+dwb2023