Related references
Note: Only part of the references are listed.BBKNN: fast batch alignment of single cell transcriptomes
Krzysztof Polanski et al.
BIOINFORMATICS (2020)
Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets
Rossin Erbe et al.
NUCLEIC ACIDS RESEARCH (2020)
A benchmark of batch-effect correction methods for single-cell RNA sequencing data
Hoa Thi Nhu Tran et al.
GENOME BIOLOGY (2020)
scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles
Suoqin Jin et al.
GENOME BIOLOGY (2020)
Eleven grand challenges in single-cell data science
David Laehnemann et al.
GENOME BIOLOGY (2020)
Integrative single-cell analysis
Tim Stuart et al.
NATURE REVIEWS GENETICS (2019)
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
Brian Hie et al.
NATURE BIOTECHNOLOGY (2019)
scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets
Yingxin Lin et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species
Genevieve L. Stein-O'Brien et al.
CELL SYSTEMS (2019)
Comprehensive Integration of Single-Cell Data
Tim Stuart et al.
CELL (2019)
Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity
Joshua D. Welch et al.
CELL (2019)
Concepts and limitations for learning developmental trajectories from single cell genomics
Sophie Tritschler et al.
DEVELOPMENT (2019)
Current best practices in single-cell RNA-seq analysis: a tutorial
Malte D. Luecken et al.
MOLECULAR SYSTEMS BIOLOGY (2019)
Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility
Caleb A. Lareau et al.
NATURE BIOTECHNOLOGY (2019)
Joint analysis of heterogeneous single-cell RNA-seq dataset collections
Nikolas Barkas et al.
NATURE METHODS (2019)
scGen predicts single-cell perturbation responses
Mohammad Lotfollahi et al.
NATURE METHODS (2019)
Learning common and specific patterns from data of multiple interrelated biological scenarios with matrix factorization
Lihua Zhang et al.
NUCLEIC ACIDS RESEARCH (2019)
Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape
Brian Hie et al.
CELL SYSTEMS (2019)
Fast, sensitive and accurate integration of single-cell data with Harmony
Ilya Korsunsky et al.
NATURE METHODS (2019)
Single-Cell Analysis Reveals a Hair Follicle Dermal Niche Molecular Differentiation Trajectory that Begins Prior to Morphogenesis
Khusali Gupta et al.
DEVELOPMENTAL CELL (2019)
Dimensionality reduction for visualizing single-cell data using UMAP
Etienne Becht et al.
NATURE BIOTECHNOLOGY (2019)
Visualizing structure and transitions in high-dimensional biological data
Kevin R. Moon et al.
NATURE BIOTECHNOLOGY (2019)
Assessment of computational methods for the analysis of single-cell ATAC-seq data
Huidong Chen et al.
GENOME BIOLOGY (2019)
From Louvain to Leiden: guaranteeing well-connected communities
V. A. Traag et al.
SCIENTIFIC REPORTS (2019)
Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
Hyun Min Kang et al.
NATURE BIOTECHNOLOGY (2018)
Random Noise Suppression Algorithm for Seismic Signals Based on Principal Component Analysis
Yuan-Jia Ma et al.
WIRELESS PERSONAL COMMUNICATIONS (2018)
scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data
Suoqin Jin et al.
BIOINFORMATICS (2018)
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics
Kelly Street et al.
BMC GENOMICS (2018)
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Laleh Haghverdi et al.
NATURE BIOTECHNOLOGY (2018)
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Andrew Butler et al.
NATURE BIOTECHNOLOGY (2018)
scmap: projection of single-cell RNA -seq data across data sets
Vladimir Yu Kiselev et al.
NATURE METHODS (2018)
Alignment of single-cell trajectories to compare cellular expression dynamics
Ayelet Alpert et al.
NATURE METHODS (2018)
A novel autophagy enhancer as a therapeutic agent against metabolic syndrome and diabetes
Hyejin Lim et al.
NATURE COMMUNICATIONS (2018)
A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility
Darren A. Cusanovich et al.
CELL (2018)
Aligning Single-Cell Developmental and Reprogramming Trajectories Identifies Molecular Determinants of Myogenic Reprogramming Outcome
Davide Cacchiarelli et al.
CELL SYSTEMS (2018)
Deep generative modeling for single-cell transcriptomics
Romain Lopez et al.
NATURE METHODS (2018)
Hedgehog stimulates hair follicle neogenesis by creating inductive dermis during murine skin wound healing
Chae Ho Lim et al.
NATURE COMMUNICATIONS (2018)
chromVAR : inferring transcription-factor-associated accessibility from single-cell epigenomic data
Alicia N. Schep et al.
NATURE METHODS (2017)
Reversed graph embedding resolves complex single-cell trajectories
Xiaojie Qiu et al.
NATURE METHODS (2017)
SC3: consensus clustering of single-cell RNA-seq data
Vladimir Yu Kiselev et al.
NATURE METHODS (2017)
Splatter: simulation of single-cell RNA sequencing data
Luke Zappia et al.
GENOME BIOLOGY (2017)
Challenges and emerging directions in single-cell analysis
Guo-Cheng Yuan et al.
GENOME BIOLOGY (2017)
Simultaneous dimension reduction and adjustment for confounding variation
Zhixiang Lin et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2016)
Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments
Andrew McDavid et al.
BIOINFORMATICS (2013)
Distinct fibroblast lineages determine dermal architecture in skin development and repair
Ryan R. Driskell et al.
NATURE (2013)
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Guangchuang Yu et al.
OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY (2012)
GREAT improves functional interpretation of cis-regulatory regions
Cory Y. McLean et al.
NATURE BIOTECHNOLOGY (2010)
Share Paper
