Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities.
A complete understanding of biological processes requires synthesizing information across heterogeneous modalities, such as age, disease status, or gene expression. Technological advances in single-cell profiling have enabled researchers to assay multiple modalities simultaneously. We present Schema, which uses a principled metric learning strategy that identifies informative features in a modality to synthesize disparate modalities into a single coherent interpretation. We use Schema to infer cell types by integrating gene expression and chromatin accessibility data; demonstrate informative data visualizations that synthesize multiple modalities; perform differential gene expression analysis in the context of spatial variability; and estimate evolutionary pressure on peptide sequences.
1. Cell type prioritization in single-cell data.
2. Identification of genomic enhancers through spatial integration of single-cell transcriptomics and epigenomics.
3. SCMarker: Ab initio marker selection for single cell transcriptome profiling.
4. Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning.
5. Iterative single-cell multi-omic integration using online learning.
1. Joint profiling of chromatin accessibility and gene expression in thousands of single cells
2. Parallel Bimodal Single-cell Sequencing of Transcriptome and Chromatin Accessibility (Mouse single cell ATAC-seq)
3. Identification of genomic enhancers through spatial integration of single-cell transcriptomics and epigenomics [FluidigmC1_scATACseq]
4. Transcriptome analysis of single cells from the developing mouse dentate gyrus
5. Parallel Bimodal Single-cell Sequencing of Transcriptome and Chromatin Accessibility (Human single cell ATAC-seq)