CNS on Single-Cell Analysis

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A track of CNS papers mainly related to single-cell analysis and machine learning.

(Up to Apr 1, 2020)

2020

Mar

  • 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.

Feb

  • 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.

Jan

  • 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.

2019

Dec

  • Orchestrating single-cell analysis with Bioconductor. Nat. met.

Nov

  • 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.

Oct

  • Exploring single-cell data with deep multitasking neural networks. Nat. met.

Sept

  • 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.

Aug

  • Data denoising with transfer learning in single-cell transcriptomics. Nat. met.

Jul

  • scGen predicts single-cell perturbation responses. Nat. met.
  • Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat. met.

Jun

  • Pathway-level information extractor (PLIER) for gene expression data. Nat. met.

May

  • Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments. Nat. met.

Apr

  • cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nat. met.

Mar

  • 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.

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