Quantified gene expression for all bulk RNA-seq samples used in this study are available as an HDF5 file (in log transcripts per million): bulk_log_tpm.h5
Each bulk RNA-seq experiment accession is mapped to a set of cell type labels from the Cell Ontology: bulk_labels.json
The single-cell data used in this study are also available as an HDF5 file (in log transcripts per million): single_cell_log_tpm.h5
Each single-cell experiment accession is mapped to a set of cell type labels from the Cell Ontology: single_cell_labels.json
We partitioned the bulk RNA-seq data into several subsets that were used for various purposes in the study:
After training the binary classifiers for each cell type, the model coefficients can be used to investigate up and downregulated genes in each cell type. Below, we post the model coefficients for the one-versus-rest trained binary classifiers (used in the Isotonic Regression and True Path Rule algorithms) as well as the coefficients for the classifiers in the Cascaded Logistic Regression algorithm. Each model was trained on the full set of bulk RNA-seq samples used in the study. Each algorithm's cell type model coefficients are available in a tab-separated-value file:
We packaged the classifiers into a software package we call CellO (Cell Ontology-based classification) available on GitHub here: https://github.com/deweylab/CellO
All code and data used in this work are licensed under CC BY 4.0.