English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Scanpro is a tool for robust proportion analysis of single-cell resolution data

MPS-Authors
/persons/resource/persons248851

Bentsen,  Mette
Bioinformatics, Max Planck Institute for Heart and Lung Research, Max Planck Society;

/persons/resource/persons224384

Looso,  Mario
Bioinformatics, Max Planck Institute for Heart and Lung Research, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Alayoubi, Y., Bentsen, M., & Looso, M. (2024). Scanpro is a tool for robust proportion analysis of single-cell resolution data. SCIENTIFIC REPORTS, 14(1): 15581. doi:10.1038/s41598-024-66381-7.


Cite as: https://hdl.handle.net/21.11116/0000-0010-08DD-D
Abstract
In higher organisms, individual cells respond to signals and perturbations by epigenetic regulation and transcriptional adaptation. However, in addition to shifting the expression level of individual genes, the adaptive response of cells can also lead to shifts in the proportions of different cell types. Recent methods such as scRNA-seq allow for the interrogation of expression on the single-cell level, and can quantify individual cell type clusters within complex tissue samples. In order to identify clusters showing differential composition between different biological conditions, differential proportion analysis has recently been introduced. However, bioinformatics tools for robust proportion analysis of both replicated and unreplicated single-cell datasets are critically missing. In this manuscript, we present Scanpro, a modular tool for proportion analysis, seamlessly integrating into widely accepted frameworks in the Python environment. Scanpro is fast, accurate, supports datasets without replicates, and is intended to be used by bioinformatics experts and beginners alike.