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_viash.yaml
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viash_version: 0.9.0
name: task_predict_modality
organization: openproblems-bio
version: dev
license: MIT
label: Predict Modality
keywords: [multi-omics, regression, single-cell]
summary: Predicting the profiles of one modality (e.g. protein abundance) from another (e.g. mRNA expression).
description: |
Experimental techniques to measure multiple modalities within the same single cell are increasingly becoming available.
The demand for these measurements is driven by the promise to provide a deeper insight into the state of a cell.
Yet, the modalities are also intrinsically linked. We know that DNA must be accessible (ATAC data) to produce mRNA
(expression data), and mRNA in turn is used as a template to produce protein (protein abundance). These processes
are regulated often by the same molecules that they produce: for example, a protein may bind DNA to prevent the production
of more mRNA. Understanding these regulatory processes would be transformative for synthetic biology and drug target discovery.
Any method that can predict a modality from another must have accounted for these regulatory processes, but the demand for
multi-modal data shows that this is not trivial.
references:
doi:
# Multimodal single cell data integration challenge: results and lessons learned
# Christopher Lance, Malte D. Luecken, Daniel B. Burkhardt, Robrecht Cannoodt, Pia Rautenstrauch, Anna Laddach, Aidyn Ubingazhibov, Zhi-Jie Cao, Kaiwen Deng, Sumeer Khan, Qiao Liu, Nikolay Russkikh, Gleb Ryazantsev, Uwe Ohler, NeurIPS 2021 Multimodal data integration competition participants, Angela Oliveira Pisco, Jonathan Bloom, Smita Krishnaswamy, Fabian J. Theis
# bioRxiv 2022.04.11.487796; doi: https://doi.org/10.1101/2022.04.11.487796
- 10.1101/2022.04.11.487796
bibtex:
- |
@inproceedings{luecken2021sandbox,
title={A sandbox for prediction and integration of DNA, RNA, and proteins in single cells},
author={Luecken, Malte D and Burkhardt, Daniel Bernard and Cannoodt, Robrecht and Lance, Christopher and Agrawal, Aditi and Aliee, Hananeh and Chen, Ann T and Deconinck, Louise and Detweiler, Angela M and Granados, Alejandro A and others},
booktitle={Thirty-fifth conference on neural information processing systems datasets and benchmarks track (Round 2)},
year={2021}
}
authors:
- name: Alejandro Granados
roles: [ author ]
info:
github: agranado
- name: Alex Tong
roles: [ author ]
info:
github: atong01
- name: Bastian Rieck
roles: [ author ]
info:
github: Pseudomanifold
- name: Daniel Burkhardt
roles: [ author ]
info:
github: dburkhardt
- name: Kai Waldrant
roles: [ contributor ]
info:
github: KaiWaldrant
orcid: "0009-0003-8555-1361"
- name: Kaiwen Deng
roles: [ contributor ]
info:
email: [email protected]
github: nonztalk
- name: Louise Deconinck
roles: [ author ]
info:
github: LouiseDck
- name: Robrecht Cannoodt
roles: [ author, maintainer ]
info:
github: rcannood
orcid: "0000-0003-3641-729X"
links:
issue_tracker: https://github.com/openproblems-bio/task_predict_modality/issues
repository: https://github.com/openproblems-bio/task_predict_modality
docker_registry: ghcr.io
info:
image: thumbnail.svg
test_resources:
- type: s3
path: s3://openproblems-data/resources_test/common/
dest: resources_test/common
- type: s3
path: s3://openproblems-data/resources_test/task_predict_modality/
dest: resources_test/task_predict_modality
repositories:
- name: openproblems
type: github
repo: openproblems-bio/openproblems
tag: build/main
config_mods: |
.runners[.type == "nextflow"].config.labels := { lowmem : "memory = 20.Gb", midmem : "memory = 50.Gb", highmem : "memory = 100.Gb", lowcpu : "cpus = 5", midcpu : "cpus = 15", highcpu : "cpus = 30", lowtime : "time = 1.h", midtime : "time = 4.h", hightime : "time = 8.h", veryhightime : "time = 24.h" }