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

Luca Alessandri, Francesca Cordero, Marco Beccuti, Nicola Licheri, Maddalena Arigoni, Martina Olivero, Maria Flavia Di Renzo, Anna Sapino, Raffaele Calogero

Research output: Contribution to journalArticlepeer-review


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 report a tool that we developed for the functional mining of single cell clusters based on Sparsely-Connected Autoencoder (SCA). This tool allows uncovering hidden features associated with scRNAseq data. We implemented two new metrics, QCC (Quality Control of Cluster) and QCM (Quality Control of Model), which allow quantifying the ability of SCA to reconstruct valuable cell clusters and to evaluate the quality of the neural network achievements, respectively. Our data indicate that SCA encoded space, derived by different experimentally validated data (TF targets, miRNA targets, Kinase targets, and cancer-related immune signatures), can be used to grasp single cell cluster-specific functional features. In our implementation, SCA efficacy comes from its ability to reconstruct only specific clusters, thus indicating only those clusters where the SCA encoding space is a key element for cells aggregation. SCA analysis is implemented as module in rCASC framework and it is supported by a GUI to simplify it usage for biologists and medical personnel.

Original languageEnglish
Pages (from-to)1
Journalnpj Systems Biology and Applications
Issue number1
Publication statusPublished - Jan 5 2021


  • Algorithms
  • Base Sequence/genetics
  • Cluster Analysis
  • Data Mining/methods
  • Humans
  • Neural Networks, Computer
  • Sequence Analysis, RNA/methods
  • Single-Cell Analysis/methods
  • Software
  • Systems Biology/methods
  • Whole Exome Sequencing/methods


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