Computational methods for the integrative analysis of single-cell data.
Cited by: 20
Recent advances in single-cell technologies are providing exciting opportunities for dissecting tissue heterogeneity and investigating cell identity, fate and function. This is a pristine, exploding field that is flooding biologists with a new wave of data, each with its own specificities in terms of complexity and information content. The integrative analysis of genomic data, collected at different molecular layers from diverse cell populations, holds promise to address the full-scale complexity of biological systems. However, the combination of different single-cell genomic signals is computationally challenging, as these data are intrinsically heterogeneous for experimental, technical and biological reasons. Here, we describe the computational methods for the integrative analysis of single-cell genomic data, with a focus on the integration of single-cell RNA sequencing datasets and on the joint analysis of multimodal signals from individual cells.
1. Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces.
2. Single Cell Explorer, collaboration-driven tools to leverage large-scale single cell RNA-seq data.
3. scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses.
4. Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine.
5. Single-cell genomics to understand disease pathogenesis.
1. Silencing of odorant receptor gene expression by G protein βγ signaling ensures the expression of one odorant receptor per olfactory sensory neuron
2. Single-cell RNA-sequencing of Platynereis dumerilii larval brain cells
3. Joint profiling of chromatin accessibility, DNA methylation and transcription in single cells
4. Cicero Predicts cis-Regulatory DNA Interactions from Single-Cell Chromatin Accessibility Data
5. Integrative analysis of single cell genomics data by coupled nonnegative matrix factorizations (ATAC-Seq)