task_label_projection/_viash.yaml at main · openproblems-bio/task_label_projection · GitHub

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viash_version: 0.9.4

name: task_label_projection
organization: openproblems-bio
version: dev

license: MIT
keywords: [ "single-cell", "label projection", "cell type annotation" ]
links:
  issue_tracker: https://github.com/openproblems-bio/task_label_projection/issues
  repository: https://github.com/openproblems-bio/task_label_projection
  docker_registry: ghcr.io

label: Label projection
summary: Automated cell type annotation from rich, labeled reference data
description: |
  A major challenge for integrating single cell datasets is creating matching
  cell type annotations for each cell. One of the most common strategies for
  annotating cell types is referred to as
  ["cluster-then-annotate"](https://www.nature.com/articles/s41576-018-0088-9)
  whereby cells are aggregated into clusters based on feature similarity and
  then manually characterized based on differential gene expression or previously
  identified marker genes. Recently, methods have emerged to build on this
  strategy and annotate cells using
  [known marker genes](https://www.nature.com/articles/s41592-019-0535-3).
  However, these strategies pose a difficulty for integrating atlas-scale
  datasets as the particular annotations may not match.

  To ensure that the cell type labels in newly generated datasets match
  existing reference datasets, some methods align cells to a previously
  annotated [reference dataset](https://academic.oup.com/bioinformatics/article/35/22/4688/54802990)
  and then _project_ labels from the reference to the new dataset.

  Here, we compare methods for annotation based on a reference dataset.
  The datasets consist of two or more samples of single cell profiles that
  have been manually annotated with matching labels. These datasets are then
  split into training and test batches, and the task of each method is to
  train a cell type classifer on the training set and project those labels
  onto the test set.
# references:
#   doi:
#     - 10.21203/rs.3.rs-4181617/v1
#   bibtex:
#     - |
#       @article{doe_2021_template,
#         doi = {10.21203/rs.3.rs-4181617/v1},
#         url = {https://doi.org/10.21203/rs.3.rs-4181617/v1},
#         author = {Doe, John},
#         title = {A template for creating new tasks},
#         publisher = {Research Square},
#         year = {2021},
#       }

info:
  image: thumbnail.svg
  test_resources:
    - type: s3
      path: s3://openproblems-data/resources_test/common/cxg_immune_cell_atlas/
      dest: resources_test/common/cxg_immune_cell_atlas
    - type: s3
      path: s3://openproblems-data/resources_test/task_label_projection/
      dest: resources_test/task_label_projection

authors:
  - name: "Nikolay Markov"
    roles: [ author, maintainer ]
    info:
      github: mxposed
  - name: "Scott Gigante"
    roles: [ author ]
    info:
      github: scottgigante
      orcid: "0000-0002-4544-2764"
  - name: Robrecht Cannoodt
    roles: [ author ]
    info:
      github: rcannood
      orcid: "0000-0003-3641-729X"
  - name: Luke Zappia
    roles: [author]
    info:
      github: lazappi
      orcid: 0000-0001-7744-8565
  - name: Sai Nirmayi Yasa
    roles: [author]
    info:
      github: sainirmayi
      orcid: 0009-0003-6319-9803
  - name: Jeremie Kalfon
    roles: [author]
    info:
      github: jkobject
      orcid: 0000-0002-2818-9728
  - name: Marius Lange
    roles: [contributor]
    info:
      github: marius1311
      orcid: 0000-0002-4846-1266

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" }

repositories:
  - name: openproblems
    type: github
    repo: openproblems-bio/openproblems
    tag: build/main