CNS on Single-Cell Analysis

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Published:

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

Contents

2021

Oct

  • DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data. Nature Communications

Sep

  • VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics. Nature Communications
  • Generalized and scalable trajectory inference in single-cell omics data with VIA. Nature Communications
  • Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data. Nature Communications
  • EpiScanpy: integrated single-cell epigenomic analysis. Nature Communications

Aug

  • Mapping single-cell data to reference atlases by transfer learning. Nature Biotechnology
  • Sc-compReg enables the comparison of gene regulatory networks between conditions using single-cell data. Nature Communications

Jul

  • Modeling gene regulatory networks using neural network architectures. Nature Computational Science
  • Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications. Nature Communications

Jun

  • scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics. Nature Communications
  • The triumphs and limitations of computational methods for scRNA-seq. Nature Methods
  • Integration of millions of transcriptomes using batch-aware triplet neural networks. Nature Machine Intelligence
  • Model-based prediction of spatial gene expression via generative linear mapping. Nature Communications
  • Spatial transcriptomics at subspot resolution with BayesSpace. Nature Biotechnology

May

  • Integrated analysis of multimodal single-cell data. Cell
  • Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions. Nature Communications
  • Hierarchical progressive learning of cell identities in single-cell data. Nature Communications
  • Simultaneous deep generative modelling and clustering of single-cell genomic data. Nature Machine Intelligence
  • Computational principles and challenges in single-cell data integration. Nature Biotechnology

Apr

  • Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID. Nature Biotechnology
  • Bayesian inference of gene expression states from single-cell RNA-seq data. Nature Biotechnology
  • Iterative single-cell multi-omic integration using online learning. Nature Biotechnology
  • Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms. Nature Machine Intelligence

Mar

  • Robust integration of multiple single-cell RNA sequencing datasets using a single reference space. Nature Biotechnology
  • Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data. Nature Communications
  • scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses. Nature Communications
  • Deep generative neural network for accurate drug response imputation. Nature Communications
  • Deep learning-based enhancement of epigenomics data with AtacWorks. Nature Communications
  • An automated framework for efficiently designing deep convolutional neural networks in genomics. Nature Machine Intelligence

Feb

  • ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nature Genetics
  • Joint probabilistic modeling of single-cell multi-omic data with totalVI. Nature Methods
  • Fast and precise single-cell data analysis using a hierarchical autoencoder. Nature Communications
  • Improving representations of genomic sequence motifs in convolutional networks with exponential activations. Nature Machine Intelligence

Jan

  • Deep neural networks identify sequence context features predictive of transcription factor binding. Nature Machine Intelligence
  • Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nature Communications
  • Multi-domain translation between single-cell imaging and sequencing data using autoencoders. Nature Communications
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2020

Dec

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

Nov

  • Ensemble dimensionality reduction and feature gene extraction for single-cell RNA-seq data. Nature Communications
  • An interpretable deep-learning architecture of capsule networks for identifying cell-type gene expression programs from single-cell RNA-sequencing data. Nature Machine Intelligence

Oct

  • Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis. Nature Machine Intelligence
  • A multiresolution framework to characterize single-cell state landscapes. Nature Communications
  • MARS: discovering novel cell types across heterogeneous single-cell experiments. Nature Methods

Sept

  • Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks. Nature Communications

Aug

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

Jul

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

Jun

  • Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data. Nature Communications

May

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

Apr

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

Mar

  • Cardelino: computational integration of somatic clonal substructure and single-cell transcriptomes. Nature Methods
  • TooManyCells identifies and visualizes relationships of single-cell clades. Nature Methods
  • Integrative differential expression and gene set enrichment analysis using summary statistics for scRNA-seq studies. Nature Communications
  • Latent periodic process inference from single-cell RNA-seq data. Nature Communications

Feb

  • Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nature Methods
  • Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder. Nature Communications

Jan

  • Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nature Methods
  • Surface protein imputation from single cell transcriptomes by deep neural networks. Nature Communications
  • Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nature Communications
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2019

Dec

  • Orchestrating single-cell analysis with Bioconductor. Nature Methods

Nov

  • Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nature Methods
  • Fast, sensitive and accurate integration of single-cell data with Harmony. Nature Methods

Oct

  • Exploring single-cell data with deep multitasking neural networks. Nature Methods

Sept

  • Supervised classification enables rapid annotation of cell atlases. Nature Methods
  • Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nature Methods

Aug

  • Data denoising with transfer learning in single-cell transcriptomics. Nature Methods

Jul

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

Jun

  • Pathway-level information extractor (PLIER) for gene expression data. Nature Methods

May

  • Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments. Nature Methods

Apr

  • cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nature Methods

Mar

  • Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning. Nature Methods
  • Selene: a PyTorch-based deep learning library for sequence data. Nature Methods
  • Deep-learning augmented RNA-seq analysis of transcript splicing. Nature Methods
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