An open API service providing mapping between scientific papers and software projects that are mentioned in them.

Papers: 10.1186/s13059-020-02083-3

https://doi.org/10.1186/s13059-020-02083-3

DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning

Cited by: 23
Author(s): Yong He, Hao Yuan, Changbao Wu, Zhi Xie
Published: about 5 years ago

Software Mentions 9

bioconductor: MAST
Model-based Analysis of Single Cell Transcriptomics
Papers that mentioned: 668
Very Likely Science (100)
cran: DCA
Dynamic Correlation Analysis for High Dimensional Data
Papers that mentioned: 128
Very Likely Science (93)
cran: DDRTree
Learning Principal Graphs with DDRTree
Papers that mentioned: 42
Very Likely Science (83)
cran: reldist
Relative Distribution Methods
Papers that mentioned: 15
Very Likely Science (83)
cran: Seurat
Tools for Single Cell Genomics
Papers that mentioned: 1,512
Very Likely Science (100)
pypi: DCA
Count autoencoder for scRNA-seq denoising
Papers that mentioned: 128
Very Likely Science (100)
pypi: magic-impute
MAGIC
Papers that mentioned: 1
Very Likely Science (100)
pypi: MAST
MAterials Simulation Toolkit
Papers that mentioned: 668
Very Likely Science (75)
pypi: scScope
scScope is a deep-learning based approach for single cell RNA-seq analysis.
Papers that mentioned: 7
Very Likely Science (100)