A machine learning one-class logistic regression model to predict stemness for single cell transcriptomics and spatial omics.

ABSTRACT: Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single cell analysis but are often biased, manually curated and yet unproven in spatial omics. Here we apply a stemness model for assessing oncogenic states to single cell and spatial omic cancer datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic features from non-transformed stem cells to identify dedifferentiated cell states in tumors. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. This machine learning model identified stem-like cell populations not identified in single cell or spatial transcriptomic analysis

SUBMITTER: Dezem FS 

PROVIDER: S-EPMC10683105 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature