Create CITATION.cff

Author: naveedishaque

CITATION.cff

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+# This CITATION.cff file was generated with cffinit.
+# Visit https://bit.ly/cffinit to generate yours today!
+
+cff-version: 1.2.0
+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