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@ARTICLE{Aybey:278729,
      author       = {B. Aybey and S. Zhao and B. Brors$^*$ and E. Staub},
      title        = {{I}mmune cell type signature discovery and random forest
                      classification for analysis of single cell gene expression
                      datasets.},
      journal      = {Frontiers in immunology},
      volume       = {14},
      issn         = {1664-3224},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {DKFZ-2023-01700},
      pages        = {1194745},
      year         = {2023},
      abstract     = {Robust immune cell gene expression signatures are central
                      to the analysis of single cell studies. Nearly all known
                      sets of immune cell signatures have been derived by making
                      use of only single gene expression datasets. Utilizing the
                      power of multiple integrated datasets could lead to
                      high-quality immune cell signatures which could be used as
                      superior inputs to machine learning-based cell type
                      classification approaches.We established a novel workflow
                      for the discovery of immune cell type signatures based
                      primarily on gene-versus-gene expression similarity. It
                      leverages multiple datasets, here seven single cell
                      expression datasets from six different cancer types and
                      resulted in eleven immune cell type-specific gene expression
                      signatures. We used these to train random forest classifiers
                      for immune cell type assignment for single-cell RNA-seq
                      datasets. We obtained similar or better prediction results
                      compared to commonly used methods for cell type assignment
                      in independent benchmarking datasets. Our gene signature set
                      yields higher prediction scores than other published immune
                      cell type gene sets in random forest-based cell type
                      classification. We further demonstrate how our approach
                      helps to avoid bias in downstream statistical analyses by
                      re-analysis of a published IFN stimulation experiment.We
                      demonstrated the quality of our immune cell signatures and
                      their strong performance in a random forest-based cell
                      typing approach. We argue that classifying cells based on
                      our comparably slim sets of genes accompanied by a random
                      forest-based approach not only matches or outperforms widely
                      used published approaches. It also facilitates unbiased
                      downstream statistical analyses of differential gene
                      expression between cell types for significantly more genes
                      compared to previous cell classification algorithms.},
      keywords     = {cell clustering (Other) / cell type classification (Other)
                      / gene signature discovery (Other) / machine learning
                      (Other) / single-cell RNA sequencing (Other) / tumor
                      microenvironment (Other)},
      cin          = {B330 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)B330-20160331 / I:(DE-He78)HD01-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37609075},
      pmc          = {pmc:PMC10441575},
      doi          = {10.3389/fimmu.2023.1194745},
      url          = {https://inrepo02.dkfz.de/record/278729},
}