Vizgen response to German Court permanent injunction against Nanostring CosMx instruments & reagents for RNA Detection


A Machine Learning One-Class Logistic Regression Model to Predict Stemness in Single Cell Transcriptomics and Spatial Omics Datasets

Felipe Segato Dezem, Maycon Marção, Bassem Ben-Cheikh, Nadya Nikulina, Ayodele Omotoso, Destiny Burnett, Priscila Coelho, Simone Badal, Judith Hurley, Carmen Gomez, Tien Phan-Everson, Giang Ong, Luciano Martelotto, Zachary R. Lewis, Sophia George, Oliver Braubach, Tathiane Malta, Jasmine Plummer
bioRxiv (2023)


Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches are often biased and manually curated. Here we harness an existing stemness model for assessing oncogenic states to transform its application to single cell and spatial omic datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic or proteomic features from non-transformed stem cells to identify dedifferentiated cell states. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. Finally these stemness indices are applicable across a variety of spatial transcriptomic and proteomic technologies for the identification of oncogenic cell types in the tumor microenvironment.