CNS on Single-Cell Analysis
Published:
A track of CNS papers mainly related to single-cell analysis and machine learning.
Contents
2023
Feb
- Biologically informed deep learning to query gene programs in single-cell atlases. Nature Cell Biology
Jan
- Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA. Nature Communications
- Decision level integration of unimodal and multimodal single cell data with scTriangulate. Nature Communications
- scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection. Nature Communications
- Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST. Nature Communications
- Gene expression model inference from snapshot RNA data using Bayesian non-parametrics. Nature Computational Science
- Transformer for one stop interpretable cell type annotation. Nature Communications
- Robust single-cell matching and multimodal analysis using shared and distinct features. Nature Methods
2022
Dec
- Nonnegative spatial factorization applied to spatial genomics. Nature Methods
- Clustering of single-cell multi-omics data with a multimodal deep learning method. Nature Communications
- Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding. Nature Communications
- Evaluating deep learning for predicting epigenomic profiles. Nature Machine Intelligence
- Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease. Nature Communications
- A unified computational framework for single-cell data integration with optimal transport. Nature Communications
Nov
- Leveraging data-driven self-consistency for high-fidelity gene expression recovery. Nature Communications
- A flexible cross-platform single-cell data processing pipeline. Nature Communications
- Detection of m6A from direct RNA sequencing using a multiple instance learning framework. Nature Methods
- Learning the histone codes with large genomic windows and three-dimensional chromatin interactions using transformer. Nature Communications
Oct
- Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data. Nature Communications
- De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution. Nature Communications
- A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputation. Nature Machine Intelligence
- Annotation of spatially resolved single-cell data with STELLAR. Nature Methods
- Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space. Nature Communications
- Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction. Nature Biotechnology
- Deep learning of cross-species single-cell landscapes identifies conserved regulatory program. Nature Genetics
Sep
- scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. Nature Machine Intelligence
- Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity. Nature Communications
- ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells. Nature Methods
- Deep neural networks with controlled variable selection for the identification of putative causal genetic variants. Nature Machine Intelligence
- devCellPy is a machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data. Nature Communications
- MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells. Nature Methods
Aug
- Contrastive learning enables rapid mapping to multimodal single-cell atlas of multimillion scale. Nature Machine Intelligence
- Scarf enables a highly memory-efficient analysis of large-scale single-cell genomics data. Nature Communications
- scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks. Nature Methods
Jun
- Minimal gene set discovery in single-cell mRNA-seq datasets with ActiveSVM. Nature Computational Science
- Forest Fire Clustering for single-cell sequencing combines iterative label propagation with parallelized Monte Carlo simulations. Nature Communications
May
- Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets. Nature Computational Science
- Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nature Biotechnology
Apr
- DestVI identifies continuums of cell types in spatial transcriptomics data. Nature Biotechnology
- Membrane marker selection for segmenting single cell spatial proteomics data. Nature Communications
- Interactive single-cell data analysis using Cellar. Nature Communications
- Inferring transcription factor regulatory networks from single-cell ATAC-seq data based on graph neural networks. Nature Machine Intelligence
- A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data. Nature Communications
- Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nature Communications
Mar
- Integrative spatial analysis of cell morphologies and transcriptional states with MUSE. Nature Biotechnology
- Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro. Nature Biotechnology
- Spatial charting of single-cell transcriptomes in tissues. Nature Biotechnology
- Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nature Communications
Feb
- Simultaneous dimensionality reduction and integration for single-cell ATAC-seq data using deep learning. Nature Machine Intelligence
- Cell type annotation of single-cell chromatin accessibility data via supervised Bayesian embedding. Nature Machine Intelligence
- UINMF performs mosaic integration of single-cell multi-omic datasets using nonnegative matrix factorization. Nature Communications
- Inferring protein expression changes from mRNA in Alzheimer’s dementia using deep neural networks. Nature Communications
Jan
- A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data. Nature Computational Science
- Temporal modelling using single-cell transcriptomics. Nature Reviews Genetics
- Interpreting neural networks for biological sequences by learning stochastic masks. Nature Machine Intelligence
- scJoint: transfer learning for data integration of atlas-scale single-cell RNA-seq and ATAC-seq. Nature Biotechnology
- Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO. Nature Methods
- CellRank for directed single-cell fate mapping. Nature Methods
2021
Dec
- Benchmarking atlas-level data integration in single-cell genomics. Nature Methods
Nov
- scCODA is a Bayesian model for compositional single-cell data analysis. Nature Communications
- Navigating the pitfalls of applying machine learning in genomics. Nature Reviews Genetics
- Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen. Nature Communications
Oct
- Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nature Methods
- SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nature Methods
- ClusterMap for multi-scale clustering analysis of spatial gene expression. Nature Communications
- Efficient and precise single-cell reference atlas mapping with Symphony. Nature Communications
- 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
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
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