相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。BBKNN: fast batch alignment of single cell transcriptomes
Krzysztof Polanski et al.
BIOINFORMATICS (2020)
schex avoids overplotting for large single-cell RNA-sequencing datasets
Saskia Freytag et al.
BIOINFORMATICS (2020)
The single-cell transcriptional landscape of mammalian organogenesis
Junyue Cao et al.
NATURE (2019)
Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
Samuel G. Rodriques et al.
SCIENCE (2019)
High-definition spatial transcriptomics for in situ tissue profiling
Sanja Vickovic et al.
NATURE METHODS (2019)
Clustering algorithms: A comparative approach
Mayra Z. Rodriguez et al.
PLOS ONE (2019)
From Louvain to Leiden: guaranteeing well-connected communities
V. A. Traag et al.
SCIENTIFIC REPORTS (2019)
Identifying cell populations with scRNASeq
Tallulah S. Andrews et al.
MOLECULAR ASPECTS OF MEDICINE (2018)
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
Alexander B. Rosenberg et al.
SCIENCE (2018)
beachmat: A Bioconductor C plus plus API for accessing high-throughput biological data from a variety of R matrix types
Aaron T. L. Lun et al.
PLOS COMPUTATIONAL BIOLOGY (2018)
Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain
Arpiar Saunders et al.
CELL (2018)
clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets
Davide Risso et al.
PLOS COMPUTATIONAL BIOLOGY (2018)
Identification of cell types in a mouse brain single-cell atlas using low sampling coverage
Aparna Bhaduri et al.
BMC BIOLOGY (2018)
Shared and distinct transcriptomic cell types across neocortical areas
Bosiljka Tasic et al.
NATURE (2018)
Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region
Jeffrey R. Moffitt et al.
SCIENCE (2018)
SCANPY: large-scale single-cell gene expression data analysis
F. Alexander Wolf et al.
GENOME BIOLOGY (2018)
SC3: consensus clustering of single-cell RNA-seq data
Vladimir Yu Kiselev et al.
NATURE METHODS (2017)
Machine Learning for Medical Imaging1
Bradley J. Erickson et al.
RADIOGRAPHICS (2017)
Massively parallel digital transcriptional profiling of single cells
Grace X. Y. Zheng et al.
NATURE COMMUNICATIONS (2017)
Developmental Emergence of Adult Neural Stem Cells as Revealed by Single-Cell Transcriptional Profiling
Scott A. Yuzwa et al.
CELL REPORTS (2017)
Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R
Davis J. McCarthy et al.
BIOINFORMATICS (2017)
Pooling across cells to normalize single-cell RNA sequencing data with many zero counts
Aaron T. L. Lun et al.
GENOME BIOLOGY (2016)
Clustering Algorithms: Their Application to Gene Expression Data
Jelili Oyelade et al.
BIOINFORMATICS AND BIOLOGY INSIGHTS (2016)
Orchestrating high-throughput genomic analysis with Bioconductor
Wolfgang Huber et al.
NATURE METHODS (2015)
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Amit Zeisel et al.
SCIENCE (2015)
Rcpp: SeamlessRandC++Integration
Dirk Eddelbuettel et al.
Journal of Statistical Software (2015)
Improved MapReduce k-Means Clustering Algorithm with Combiner
Prajesh P. Anchalia
2014 UKSIM-AMSS 16TH INTERNATIONAL CONFERENCE ON COMPUTER MODELLING AND SIMULATION (UKSIM) (2014)
Mapreduce: Simplified data processing on large clusters
Jeffrey Dean et al.
COMMUNICATIONS OF THE ACM (2008)
Fast unfolding of communities in large networks
Vincent D. Blondel et al.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT (2008)
Augmented implicitly restarted Lanczos bidiagonalization methods
J Baglama et al.
SIAM JOURNAL ON SCIENTIFIC COMPUTING (2005)
分享论文
