Sincell: Bioconductor package for the statistical assessment of cell-state hierarchies from single-cell RNA-seq data
Abstract
SummaryCell differentiation processes are achieved through a continuum of hierarchical intermediate cell-states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general framework composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies. Sincell functionalities are illustrated in a real case study where its ability to discriminate noisy from stable cell-state hierarchies is demonstrated.\n\nAvailability and implementationSincell is an open-source R/Bioconductor package available at http://bioconductor.org/packages/3.1/bioc/html/sincell.html. A detailed manual and vignette describing functions and workflows is provided with the package.\n\nContact: antonio.rausell@isb-sib.ch
02 /Specification & Classification
Click any tag to browse matching papers- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- scClassify: hierarchical classification of cells
Yingxin Lin, Yue Cao, … Jean Yee Hwa Yang·Molecular Systems BiologyPeer-reviewed·Jun 1, 2020
- pcaReduce: Hierarchical Clustering of Single Cell Transcriptional Profiles
Justina Zurauskiene, Christopher Yau·bioRxiv·Sep 8, 2015
- scClassifR: Framework to accurately classify cell types in single-cell RNA-sequencing data
Vy Nguyen, Johannes Griss·bioRxiv·Dec 22, 2020
- Identifying hierarchical cell states and gene signatures with deep exponential families for single-cell transcriptomics
Pedro Ferreira, Jack Kuipers, Niko Beerenwinkel·bioRxiv·Oct 15, 2022
- Identifying cell states in single-cell RNA-seq data at statistically maximal resolution
Pascal Grobecker, Thomas Sakoparnig, Erik van Nimwegen·PLoS Computational BiologyPeer-reviewed·Jul 12, 2024
- Computational cell cycle analysis of single cell RNA-Seq data
Marmar R. Moussa, Ion Măndoiu·bioRxiv·Nov 21, 2020