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scX: A user-friendly tool for scRNA-seq exploration

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Tomás Vega Waichman, M. Luz Vercesi, Ariel A. Berardino, Maximiliano S. Beckel, Damiana Giacomini, Natalí B. Rasetto, Magalí Herrero, Daniela J. Di Bella
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Paola Arlotta, Alejandro F. Schinder, Ariel Chernomoretz

Genomics · Submitted 31 Oct 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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.

how it is stated · asserted 1.00 read the record →

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arXiv ID2311.00012 (version 2)
Pith IDGUG75UID, Pith's permanent record of this paper and its review
CitationAlejandro F. Schinder, Ariel A. Berardino, Ariel Chernomoretz, Damiana Giacomini, Daniela J. Di Bella, Magal\'i Herrero, Maximiliano S. Beckel, M. Luz Vercesi, Natal\'i B. Rasetto, Paola Arlotta, Tom\'as Vega Waichman. "scX: A user-friendly tool for scRNA-seq exploration." arXiv:2311.00012 (2023). https://pith.science/paper/GUG75UID
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,894 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.10 · human participants 0.08 · controlled comparison 0.03 · randomized assignment 0.02 · states quantitative result 0.03 · compares with prior results 0.06 · mentions code or data availability 0.48.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

Single-cell RNA sequencing (scRNA-seq) has transformed our ability to explore biological systems. Nevertheless, proficient expertise is essential for handling and interpreting the data. In this paper, 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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Single-cell RNA sequencing (scRNA-seq) has transformed our ability to explore biological systems. background 1.00 0.04 0.02 asserted 1.00
s1 Nevertheless, proficient expertise is essential for handling and interpreting the data. background 0.51 0.06 0.03 asserted 1.00
s2 In this paper, we present scX, an R package built on the Shiny framework that streamlines the analysis, exploration, and visualization of single-cell experiments. contribution 1.00 0.91 0.18 asserted 1.00
s3 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. method 0.64 0.85 0.08 asserted 1.00
s4 Additionally, scX seamlessly integrates with commonly used single-cell Seurat and SingleCellExperiment R objects, resulting in efficient processing and visualization of varied datasets. method 0.71 0.79 0.06 asserted 1.00
s5 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. interpretation 0.85 0.39 0.83 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.53.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Single-cell RNA sequencing (scRNA-seq) has transformed our ability to explore biological systems. Nevertheless, proficient expertise is essential for handling and interpreting the data. In this paper, 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.

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