A track of CNS papers mainly related to single-cell analysis and machine learning.
(Up to Apr 1, 2020)
- Cardelino: computational integration of somatic clonal substructure and single-cell transcriptomes. Nat. met.
- TooManyCells identifies and visualizes relationships of single-cell clades. Nat. met.
- Integrative differential expression and gene set enrichment analysis using summary statistics for scRNA-seq studies. Nat. comm.
- Latent periodic process inference from single-cell RNA-seq data. Nat. comm.
- Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat. met.
- Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder. Nat. comm.
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat. met.
- Surface protein imputation from single cell transcriptomes by deep neural networks. Nat. comm.
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nat. comm.
- Orchestrating single-cell analysis with Bioconductor. Nat. met.
- Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nat. met.
- Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. met.
- Exploring single-cell data with deep multitasking neural networks. Nat. met.
- Supervised classification enables rapid annotation of cell atlases. Nat. met.
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nat. met.
- Data denoising with transfer learning in single-cell transcriptomics. Nat. met.
- scGen predicts single-cell perturbation responses. Nat. met.
- Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat. met.
- Pathway-level information extractor (PLIER) for gene expression data. Nat. met.
- Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments. Nat. met.
- cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nat. met.
- Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning. Nat. met.
- Selene: a PyTorch-based deep learning library for sequence data. Nat. met.
- Deep-learning augmented RNA-seq analysis of transcript splicing. Nat. met.