Interpreting single-cell and spatial omics data using deep neural network training dynamics.

ABSTRACT: Single-cell and spatial omics datasets can be organized and interpreted by annotating single cells to distinct types, states, locations or phenotypes. However, cell annotations are inherently ambiguous, as discrete labels with subjective interpretations are assigned to heterogeneous cell populations on the basis of noisy, sparse and high-dimensional data. Here we developed Annotatability, a framework for identifying annotation mismatches and characterizing biological data structure by monitoring the dynamics and difficulty of training a deep neural network over such annotated data. Following this, we developed a signal-aware graph embedding method that enables downstream analysis of biological signals. This embedding captures cellular communities associated with target signals. Using Annot

SUBMITTER: Karin J 

PROVIDER: S-EPMC11659171 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature