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scX: a user-friendly tool for scRNAseq exploration.
Waichman, Tomás V; Vercesi, M L; Berardino, Ariel A; Beckel, Maximiliano S; Giacomini, Damiana; Rasetto, Natalí B; Herrero, Magalí; Di Bella, Daniela J; Arlotta, Paola; Schinder, Alejandro F; Chernomoretz, Ariel.
Afiliação
  • Waichman TV; Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, CP1405, Argentina.
  • Vercesi ML; Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, CP1405, Argentina.
  • Berardino AA; Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, CP1405, Argentina.
  • Beckel MS; Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina.
  • Giacomini D; Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, CP1405, Argentina.
  • Rasetto NB; Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina.
  • Herrero M; Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina.
  • Di Bella DJ; Laboratory of Neuronal Plasticity, Leloir Institute, Buenos Aires, CP1405, Argentina.
  • Arlotta P; Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina.
  • Schinder AF; Laboratory of Neuronal Plasticity, Leloir Institute, Buenos Aires, CP1405, Argentina.
  • Chernomoretz A; Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina.
Bioinform Adv ; 4(1): vbae062, 2024.
Article em En | MEDLINE | ID: mdl-38779177
ABSTRACT
Motivation Single-cell RNA sequencing (scRNAseq) has transformed our ability to explore biological systems. Nevertheless, proficient expertise is essential for handling and interpreting the data.

Results:

In this article, we present scX, an R package built on the Shiny framework that streamlines the analysis, exploration, and visualization of single-cell experiments. With an interactive graphic interface, implemented as a web application, scX provides easy access to key scRNAseq analyses, including marker identification, gene expression profiling, and differential gene expression analysis. Additionally, scX seamlessly integrates with commonly used single-cell Seurat and SingleCellExperiment R objects, resulting in efficient processing and visualization of varied datasets. Overall, scX serves as a valuable and user-friendly tool for effortless exploration and sharing of single-cell data, simplifying some of the complexities inherent in scRNAseq analysis. Availability and implementation Source code can be downloaded from https//github.com/chernolabs/scX. A docker image is available from dockerhub as chernolabs/scx.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article