CNS on Single-Cell Analysis

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

2021

Jan

  • Deep neural networks identify sequence context features predictive of transcription factor binding. Nat. mach. intell.
  • Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nat. comm.
  • Multi-domain translation between single-cell imaging and sequencing data using autoencoders. Nat. comm.

2020

Dec

  • Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders. Nat. mach. intell.
  • Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure. Nat. comm.

Nov

  • Ensemble dimensionality reduction and feature gene extraction for single-cell RNA-seq data. Nat. comm.
  • An interpretable deep-learning architecture of capsule networks for identifying cell-type gene expression programs from single-cell RNA-sequencing data. Nat. mach. intell.

Oct

  • A multiresolution framework to characterize single-cell state landscapes. Nat. comm.
  • MARS: discovering novel cell types across heterogeneous single-cell experiments. Nat. met.

Sept

  • Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks. Nat. comm.

Aug

  • A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nat. comm.

Jul

  • VoPo leverages cellular heterogeneity for predictive modeling of single-cell data. Nat. comm.
  • A unified framework for integrative study of heterogeneous gene regulatory mechanisms. Nat. mach. intell.
  • Deep learning decodes the principles of differential gene expression. Nat. mach. intell.
  • Deep learning for genomics using Janggu. Nat. comm.
  • Searching Large-Scale scRNA-seq Databases via Unbiased Cell Embedding With Cell BLAST. Nat. comm.

Jun

  • Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data. Nat. comm.

May

  • Putative cell type discovery from single-cell gene expression data. Nat. met.
  • Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes. Nat. met.
  • Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis. Nat. comm.

Apr

  • Accurate estimation of cell composition in bulk expression through robust integration of single-cell information. Nat. comm.
  • Large scale active-learning-guided exploration for in vitro protein production optimization. Nat. comm.

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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