deepMNN: Deep Learning-Based Single-Cell RNA Sequencing Data Batch Correction Using Mutual Nearest Neighbors

Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench

(2021) Ruben Chazarra-Gil et al.   NUCLEIC ACIDS RESEARCH

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

(2020) Xiangjie Li et al.   Nature Communications

Deep feature extraction of single-cell transcriptomes by generative adversarial network

(2020) Mojtaba Bahrami et al.   BIOINFORMATICS

Efficient integration of heterogeneous single-cell transcriptomes using Scanorama

(2019) Brian Hie et al.   NATURE BIOTECHNOLOGY

scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets

(2019) Yingxin Lin et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Comprehensive Integration of Single-Cell Data

(2019) Tim Stuart et al.   CELL

Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity

(2019) Joshua D. Welch et al.   CELL

scGen predicts single-cell perturbation responses

(2019) Mohammad Lotfollahi et al.   NATURE METHODS

Fast, sensitive and accurate integration of single-cell data with Harmony

(2019) Ilya Korsunsky et al.   NATURE METHODS

Mapping the Mouse Cell Atlas by Microwell-Seq

(2018) Xiaoping Han et al.   CELL

Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors

(2018) Laleh Haghverdi et al.   NATURE BIOTECHNOLOGY

Integrating single-cell transcriptomic data across different conditions, technologies, and species

(2018) Andrew Butler et al.   NATURE BIOTECHNOLOGY

Exponential scaling of single-cell RNA-seq in the past decade

(2018) Valentine Svensson et al.   Nature Protocols

Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris

(2018) et al.   NATURE

Dimensionality reduction for visualizing single-cell data using UMAP

(2018) Etienne Becht et al.   NATURE BIOTECHNOLOGY

Why Batch Effects Matter in Omics Data, and How to Avoid Them

(2017) Wilson Wen Bin Goh et al.   TRENDS IN BIOTECHNOLOGY

Massively parallel digital transcriptional profiling of single cells

(2017) Grace X. Y. Zheng et al.   Nature Communications

RNA Sequencing of Single Human Islet Cells Reveals Type 2 Diabetes Genes

(2016) Yurong Xin et al.   Cell Metabolism

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

(2016) Åsa Segerstolpe et al.   Cell Metabolism

Single-Cell Transcriptomics of the Human Endocrine Pancreas

(2016) Yue J. Wang et al.   DIABETES

A Single-Cell Transcriptome Atlas of the Human Pancreas

(2016) Mauro J. Muraro et al.   Cell Systems

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

Computational and analytical challenges in single-cell transcriptomics

(2015) Oliver Stegle et al.   NATURE REVIEWS GENETICS