ChromaFactor: Deconvolution of single-molecule chromatin organization with non-negative matrix factorization
Fig 3
NMF templates are significantly correlated with transcription.
a. Application of random forest models to predict cell transcription from the contribution matrix alone. b. A random forest can modestly predict transcription in abd-A, Abd-B, and Ubx, demonstrating that the components capture salient information for transcription. c. Random forest feature importance highlights templates 0, 1, and 5 as most important for predicting transcription. d. Several components, including 0, 1, 5, and 14, have significantly different component contribution weights in transcribing and non-transcribing cells (FDR < 0.001, Benjamini-Hochberg correction). e. UMAP visualization of component contribution matrix, colored by cells with a high contribution of components 0, 1, 5, and 14 (blue) and a low contribution of these components (red). f. Mean distance between abd-A and nearby enhancers at the same locus across the subset of cells with high and low component contributions. g. Median contact of cells with high and low component contributions, encompassing the subset of cells which may be responsible for changes in contact observed in bulk.