_viash.yaml

viash_version: 0.9.0

# Step 1: Change the name of the task.
# example: task_name_of_this_task
name: task_dimensionality_reduction
organization: openproblems-bio
version: dev

license: MIT
# Step 2: Add keywords to describe the task.
keywords: [single-cell, openproblems, benchmark]
# Step 3: Update the `task_template` to the name of the task from step 1.
links:
  issue_tracker: https://github.com/openproblems-bio/task_dimensionality_reduction/issues
  repository: https://github.com/openproblems-bio/task_dimensionality_reduction
  docker_registry: ghcr.io

# Step 4: Update the label, summary and description.
# A unique, human-readable, short label. Used for creating summary tables and visualisations.
label: Dimensionality Reduction for Visualization
summary: Reduction of high-dimensional datasets to 2D for visualization & interpretation.
description: |
  Data visualisation is an important part of all stages of single-cell analysis, from
  initial quality control to interpretation and presentation of final results. For bulk RNA-seq
  studies, linear dimensionality reduction techniques such as PCA and MDS are commonly used
  to visualise the variation between samples. While these methods are highly effective they
  can only be used to show the first few components of variation which cannot fully represent
  the increased complexity and number of observations in single-cell datasets. For this reason
  non-linear techniques (most notably t-SNE and UMAP) have become the standard for visualising
  single-cell studies. These methods attempt to compress a dataset into a two-dimensional space
  while attempting to capture as much of the variance between observations as possible. Many
  methods for solving this problem now exist. In general these methods try to preserve distances,
  while some additionally consider aspects such as density within the embedded space or conservation
  of continuous trajectories. Despite almost every single-cell study using one of these visualisations
  there has been debate as to whether they can effectively capture the variation in single-cell
  datasets [@chari2023speciousart].

  The dimensionality reduction task attempts to quantify the ability of methods to embed the
  information present in complex single-cell studies into a two-dimensional space. Thus, this task
  is specifically designed for dimensionality reduction for visualisation and does not consider other
  uses of dimensionality reduction in standard single-cell workflows such as improving the
  signal-to-noise ratio (and in fact several of the methods use PCA as a pre-processing step for this
  reason). Unlike most tasks, methods for the dimensionality reduction task must accept a matrix
  containing expression values normalised to 10,000 counts per cell and log transformed (log-10k) and
  produce a two-dimensional coordinate for each cell. Pre-normalised matrices are required to
  enforce consistency between the metric evaluation (which generally requires normalised data) and
  the method runs. When these are not consistent, methods that use the same normalisation as used in
  the metric tend to score more highly. For some methods we also evaluate the pre-processing
  recommended by the method.
# A list of references to relevant literature. Each reference should be a DOI or a bibtex entry
# 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
  # Step 5: Replace the task_template to the name of the task.
  test_resources:
    - type: s3
      path: s3://openproblems-data/resources_test/common/cxg_mouse_pancreas_atlas/
      dest: resources_test/common/cxg_mouse_pancreas_atlas/
    - type: s3
      path: s3://openproblems-data/resources_test/task_dimensionality_reduction/
      dest: resources_test/task_dimensionality_reduction

# Step 6: Update the authors of the task.
authors:
  - name: Luke Zappia
    roles: [maintainer, author]
    info:
      github: lazappi
      orcid: 0000-0001-7744-8565
  - name: Michael Vinyard
    roles: [author]
    info:
      github: mvinyard
  - name: Michal Klein
    roles: [author]
    info:
      github: michalk8
  - name: Scott Gigante
    roles: [author]
    info:
      github: scottgigante
      orcid: "0000-0002-4544-2764"
  - name: Ben DeMeo
    roles: [author]
    info:
      github: bendemeo
  - name: Robrecht Cannoodt
    roles: [author]
    info:
      github: rcannood
      orcid: 0000-0003-3641-729X
  - name: Kai Waldrant
    roles: [contributor]
    info:
      github: KaiWaldrant
      orcid: 0009-0003-8555-1361
  - name: Sai Nirmayi Yasa
    roles: [contributor]
    info:
      github: sainirmayi
      orcid: 0009-0003-6319-9803
  - name: Juan A. Cordero Varela
    roles: [contributor]
    info:
      github: jacorvar
      orcid: 0000-0002-7373-5433

# Step 7: Remove all of the comments of the steps you completed
# Step 8: High five yourself!

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: core
    type: github
    repo: openproblems-bio/core
    tag: build/main
    path: viash/core