Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining

Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining

Single-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Thus, it would be of great interest being able to disclose biological information belonging to cell subpopulations, which can be defined by clustering analysis of scRNAseq data. In this manuscript, we r...

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Bibliographic Details
Main Authors: Alessandri, L. (Author), Arigoni, M. (Author), Beccuti, M. (Author), Calogero, R. (Author), Cordero, F. (Author), Di Renzo, M.F (Author), Licheri, N. (Author), Olivero, M. (Author), Sapino, A. (Author)
Format: Article
Language:English
Published: Nature Research 2021
Subjects:
algorithm
Algorithms
Base Sequence
cluster analysis
Cluster Analysis
data mining
Data Mining
genetics
human
Humans
Neural Networks, Computer
nucleotide sequence
procedures
sequence analysis
Sequence Analysis, RNA
single cell analysis
Single-Cell Analysis
software
Software
systems biology
Systems Biology
whole exome sequencing
Whole Exome Sequencing
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