CITATION.cff

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title: >-
  SpaceHack 2.0: an expert in the loop consensus driven
  framework for spatially aware clustering
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - name: SpaceHack 2.0. Participants
repository-code: 'https://github.com/SpatialHackathon/SpaceHack2023'
url: 'https://spatialhackathon.github.io/past.html'
abstract: >-
  Spatial omics have transformed tissue architecture and
  cellular heterogeneity analysis by integrating molecular
  data with spatial localization. In spatially resolved
  transcriptomics, identifying spatial domains is critical
  for analysis of anatomical regions within heterogeneous
  datasets and understanding tissue function. Since 2020,
  more than 50 spatially aware clustering methods have been
  developed for this task. However, the reliability of
  existing benchmarks is undermined by their narrow focus on
  Visium and brain tissue datasets, as well as the
  dependence on questionable ground truth annotations. Here,
  we implemented a consensus framework that surpasses
  traditional benchmarking practices.


  Our framework comprises a community-driven benchmark-like
  platform that streamlines data formatting, method
  integration, and metric evaluation while accommodating new
  methods and datasets. Currently, the platform includes 22
  spatially aware clustering methods across 15 datasets
  spanning 9 technologies and diverse tissue types. The
  benchmark approach uncovered significant limitations in
  generalizability and reproducibility where methods that
  perform well on healthy tissues often falter on cancer
  samples. We also found that anatomical labels commonly
  used as ground truths are often biased, potentially
  error-prone, and in some cases, unsuitable for
  benchmarking efforts.


  In light of these issues, we adopt a flexible
  expert-in-the-loop consensus-driven approach. This goes
  beyond traditional ensemble/consensus methods, and allows
  researchers to interact with intermediate results to
  determine which tools should be used to generate a
  consensus. We believe that the inclusion of an
  expert-in-the-loop is critical to ensure that the
  computational analysis matches the biological question at
  hand, and we believe that when the focus of the analysis
  is to un cover novel biological discoveries, tissue
  experts are accessible more often than not.
license: MIT-0