Evidence map›Paper›PMID 37961742›Full record

ArticleArXiv2024

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

Tomás Vega Waichman, M Luz Vercesi, Ariel A Berardino, Maximiliano S Beckel, Damiana Giacomini, Natalí B Rasetto, Magalí Herrero, Daniela J Di Bella, Paola Arlotta, Alejandro F Schinder and 1 more

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Tomás Vega WaichmanIntegrative Systems Biology Lab, Leloir Institute, Buenos Aires, C1405 BWE, Argentina.
M Luz VercesiIntegrative Systems Biology Lab, Leloir Institute, Buenos Aires, C1405 BWE, Argentina.
Ariel A BerardinoIntegrative Systems Biology Lab, Leloir Institute, Buenos Aires, C1405 BWE, Argentina.
Maximiliano S BeckelIntegrative Systems Biology Lab, Leloir Institute, Buenos Aires, C1405 BWE, Argentina.
Damiana GiacominiInstituto de Investigaciones Bioquímicas de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, C1425 FQB, Argentina.
Natalí B RasettoInstituto de Investigaciones Bioquímicas de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, C1425 FQB, Argentina.
Magalí HerreroInstituto de Investigaciones Bioquímicas de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, C1425 FQB, Argentina.
Daniela J Di BellaDept. of Stem Cells and Regenerative Biology, Harvard University & Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Paola ArlottaDept. of Stem Cells and Regenerative Biology, Harvard University & Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Alejandro F SchinderInstituto de Investigaciones Bioquímicas de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, C1425 FQB, Argentina.
Ariel ChernomoretzIntegrative Systems Biology Lab, Leloir Institute, Buenos Aires, C1405 BWE, Argentina.

Funding

Molecular principles of neuronal maturation and integration in the adult and aging brainR01NS103758 · NINDS · HARVARD UNIVERSITY · PI ARLOTTA, PAOLA, SCHINDER, ALEJANDRO FABIAN · 2018 to 2022
$2.7M
NINDS NIH HHS R01 NS103758
6 · The paper itself

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 Single-CellExperiment 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.

Identifiers

PMID37961742
PMCPMC10635291

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.