README.md

---
license: cc-by-nc-nd-4.0
language:
- en
tags:
- 3D
- Bioimaging
- Medical
- Molecule
- RNA
- knockout
- Genetics
- interaction
pretty_name: >-
  Clustering phenotype populations by genome-wide RNAi and multiparametric
  imaging
size_categories:
- 10K<n<100K
---

# Clustering phenotype populations by genome-wide RNAi and multiparametric imaging

<!-- Provide a quick summary of the dataset. -->

**52224 images of a high-content RNAi knockdown screening**. 

*Author one-liner*: "To cluster genes and predict function on a genome-wide scale, we measured the effects of 22 839 siRNA-mediated knockdowns on HeLa cells. Each siRNA effect was summarized by a phenotypic profile."

<img src="https://cdn-uploads.huggingface.co/production/uploads/6808daf29d3434b451624026/_qZwsligf6c_OksRvTlJa.png" alt="cell image example" width="1000"/>

## Dataset Details


Organism: human (Homo sapiens)
Cell type: HeLa
Imaging method: Fluorescence microscopy

Study metadata: `metadata/S-EPMC2913390.json` - authors and IDs (BioStudies extract); `metadata/msb201025-s1.pdf` - Supplementary information (incl. variable definitions); `metadata/msb201025-s1.xls` - biological process annotation for some wells.


### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

Genetic screens for phenotypic similarity have made key contributions to associating genes with biological processes. With RNA interference (RNAi), highly parallel phenotyping of loss-of-function effects in cells has become feasible. One of the current challenges however is the computational categorization of visual phenotypes and the prediction of biological function and processes. In this study, we describe a combined computational and experimental approach to discover novel gene functions and explore functional relationships. We performed a genome-wide RNAi screen in human cells and used quantitative descriptors derived from high-throughput imaging to generate multiparametric phenotypic profiles. We show that profiles predicted functions of genes by phenotypic similarity. Specifically, we examined several candidates including the largely uncharacterized gene DONSON, which shared phenotype similarity with known factors of DNA damage response (DDR) and genomic integrity. Experimental evidence supports that DONSON is a novel centrosomal protein required for DDR signalling and genomic integrity. Multiparametric phenotyping by automated imaging and computational annotation is a powerful method for functional discovery and mapping the landscape of phenotypic responses to cellular perturbations.

- **Curated by:** University of Dundee
- **Funded by:** IDR: BBSRC (Ref: BB/M018423/1), 688945 (Euro-BioImaging Prep Phase II), 653493 (Global BioImaging Project), 654248 (CORBEL), Wellcome Trust (Ref: 212962/Z/18/Z)
- **Shared by:** IDR, Stefan Dvoretskii (stefan.dvoretskii@dkfz-heidelberg.de)
- **License:** [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/)

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:** [IDR](https://idr.openmicroscopy.org/study/idr0012/), [Bioimage Archive](https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD845), 
- **Paper [optional]:** [Clustering phenotype populations by genome‐wide RNAi and multiparametric imaging](https://doi.org/10.1038/msb.2010.25)

## Uses

<!-- Address questions around how the dataset is intended to be used. -->
Machine Learning pipelines for Bioimaging, drug screening data.

## 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. -->

Dataset is structured in [High-Content-Screening structure](https://docs.openmicroscopy.org/ome-model/5.5.7/developers/screen-plate-well.html) of OMERO. 
Images are **16-bit PNG** converted from OME-Zarr. **NB!: be aware of automatic 8-bit conversion by image tools, e.g. OpenCV!**

Metadata is provided in the tabular format (CSV) as well as JSON-LD objects.

## Dataset Creation

### Curation Rationale

Making Bioimaging experiment data available for AI models.

### Source Data

[More information on experiments](https://uk1s3.embassy.ebi.ac.uk/bia-integrator-data/pages/S-BIAD845.html)

#### Data Collection and Processing

[Data collection overview](https://uk1s3.embassy.ebi.ac.uk/bia-integrator-data/pages/S-BIAD845.html)

#### Who are the source data producers?

Florian Fuchs, Gregoire Pau, Dominique Kranz, Oleg Sklyar, Christoph Budjan, Sandra Steinbrink, Thomas Horn, Angelika Pedal, Wolfgang Huber m.boutros@dkfz.de, and Michael Boutros

### Annotations [optional]

[Annotation files on Github](https://github.com/IDR/idr0012-fuchs-cellmorph/tree/master/screenA)

#### Annotation process

https://idr.openmicroscopy.org/about/screens.html

#### Who are the annotators?

IDR staff and original scientists

#### 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. -->

No human PII / PHI has been noticed in the samples.

## Bias, Risks, and Limitations

Experiments could have contained errors / artifacts. Please refer to the original paper for more details.

### Recommendations

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

**NB!: be aware of automatic 8-bit conversion by image tools, e.g. OpenCV!**

Original Zarr files contain a better resolution / additional lazy loading capabilities.

## Citation 
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

@article{fuchs2010clustering,
  title={Clustering phenotype populations by genome-wide RNAi and multiparametric imaging},
  author={Fuchs, Florian and Pau, Gregoire and Kranz, Dominique and Sklyar, Oleg and Budjan, Christoph and Steinbrink, Sandra and Horn, Thomas and Pedal, Angelika and Huber, Wolfgang and Boutros, Michael},
  journal={Molecular systems biology},
  volume={6},
  number={1},
  pages={370},
  year={2010},
  publisher={John Wiley \& Sons, Ltd Chichester, UK}
}

**APA:**

Fuchs, F., Pau, G., Kranz, D., Sklyar, O., Budjan, C., Steinbrink, S., ... & Boutros, M. (2010). Clustering phenotype populations by genome‐wide RNAi and multiparametric imaging. Molecular systems biology, 6(1), 370.
## Glossary 
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

[IDR glossary Google doc](https://docs.google.com/spreadsheets/d/1S9of23dD8vY1QUv90RV_-Ugu0h6yTeNobuj92-OoSl8/)

## More Information 

[IDR annotation](https://idr.openmicroscopy.org/study/idr0012/)


## Dataset Card Contact

[Stefan Dvoretskii](https://steved.netlify.app/)