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Papers: 10.1093/bioinformatics/btaa201

https://doi.org/10.1093/bioinformatics/btaa201

Resolving single-cell heterogeneity from hundreds of thousands of cells through sequential hybrid clustering and NMF

Cited by: 37
Author(s): Meenakshi Venkatasubramanian, Kashish Chetal, Daniel Schnell, Gowtham Atluri, Nathan Salomonis
Published: over 5 years ago

Software Mentions 10

bioconductor: SC3
Single-Cell Consensus Clustering
Papers that mentioned: 71
Very Likely Science (100)
bioconductor: scMerge
scMerge: Merging multiple batches of scRNA-seq data
Papers that mentioned: 8
Very Likely Science (100)
cran: CCA
Canonical Correlation Analysis
Papers that mentioned: 449
Very Likely Science (93)
cran: conos
Clustering on Network of Samples
Papers that mentioned: 1
Very Likely Science (100)
cran: Seurat
Tools for Single Cell Genomics
Papers that mentioned: 1,512
Very Likely Science (100)
pypi: AltAnalyze
User Friendly Application for Comprehensive Transcriptome Analysis
Papers that mentioned: 102
Very Likely Science (90)
pypi: Harmony
Harmony Programming Language
Papers that mentioned: 504
Very Likely Science (65)
pypi: networkx
Python package for creating and manipulating graphs and networks
Papers that mentioned: 110
Very Likely Science (100)
pypi: nimfa
A Python module for nonnegative matrix factorization
Papers that mentioned: 3
Very Likely Science (80)
pypi: scikit-learn
A set of python modules for machine learning and data mining
Papers that mentioned: 2,431
Very Likely Science (100)