MulCNN: An efficient and accurate deep learning method based on gene embedding for cell type identification in single-cell RNA-seq data

Molecular Property Prediction by Combining LSTM and GAT

(2023) Lei Xu et al.   Biomolecules

scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks

(2022) Han Yuan et al.   NATURE METHODS

Automated methods for cell type annotation on scRNA-seq data

(2021) Giovanni Pasquini et al.   Computational and Structural Biotechnology Journal

Identification of resistance pathways and therapeutic targets in relapsed multiple myeloma patients through single-cell sequencing

(2021) Yael C. Cohen et al.   NATURE MEDICINE

Critical downstream analysis steps for single-cell RNA sequencing data

(2021) Zilong Zhang et al.   BRIEFINGS IN BIOINFORMATICS

Nanopore sequencing of single-cell transcriptomes with scCOLOR-seq

(2021) Martin Philpott et al.   NATURE BIOTECHNOLOGY

Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

(2021) Sophia K. Longo et al.   NATURE REVIEWS GENETICS

A single–cell type transcriptomics map of human tissues

(2021) Max Karlsson et al.   Science Advances

Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis

(2020) Xiangjie Li et al.   Nature Communications

Goals and approaches for each processing step for single-cell RNA sequencing data

(2020) Zilong Zhang et al.   BRIEFINGS IN BIOINFORMATICS

Challenges in unsupervised clustering of single-cell RNA-seq data

(2019) Vladimir Yu Kiselev et al.   NATURE REVIEWS GENETICS

Single-cell RNA-seq denoising using a deep count autoencoder

(2019) Gökcen Eraslan et al.   Nature Communications

Integrative single-cell analysis

(2019) Tim Stuart et al.   NATURE REVIEWS GENETICS

ACTINN: Automated Identification of Cell Types in Single Cell RNA Sequencing

(2019) Feiyang Ma et al.   BIOINFORMATICS

Current best practices in single‐cell RNA‐seq analysis: a tutorial

(2019) Malte D Luecken et al.   Molecular Systems Biology

Data denoising with transfer learning in single-cell transcriptomics

(2019) Jingshu Wang et al.   NATURE METHODS

Exploring single-cell data with deep multitasking neural networks

(2019) Matthew Amodio et al.   NATURE METHODS

Identifying cell populations with scRNASeq

(2018) Tallulah S. Andrews et al.   MOLECULAR ASPECTS OF MEDICINE

Deep learning in biomedicine

(2018) Michael Wainberg et al.   NATURE BIOTECHNOLOGY

Dimensionality reduction for visualizing single-cell data using UMAP

(2018) Etienne Becht et al.   NATURE BIOTECHNOLOGY

Deep generative modeling for single-cell transcriptomics

(2018) Romain Lopez et al.   NATURE METHODS

Using neural networks for reducing the dimensions of single-cell RNA-Seq data

(2017) Chieh Lin et al.   NUCLEIC ACIDS RESEARCH

Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes

(2016) Åsa Segerstolpe et al.   Cell Metabolism

Single-Cell RNAseq Reveals That Pancreatic β-Cells From Very Old Male Mice Have a Young Gene Signature

(2016) Yurong Xin et al.   ENDOCRINOLOGY

Single-cell transcriptomes identify human islet cell signatures and reveal cell-type–specific expression changes in type 2 diabetes

(2016) Nathan Lawlor et al.   GENOME RESEARCH

A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure

(2016) Maayan Baron et al.   Cell Systems

mRNA-Seq whole-transcriptome analysis of a single cell

(2009) Fuchou Tang et al.   NATURE METHODS

Fast unfolding of communities in large networks

(2008) Vincent D Blondel et al.   JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT