Evidence map›Paper›PMID 37602214›Full record

ArticlePatterns (New York, N.Y.)2023

Interactive analysis of single-cell data using flexible workflows with SCTK2.

Yichen Wang, Irzam Sarfraz, Nida Pervaiz, Rui Hong, Yusuke Koga, Vidya Akavoor, Xinyun Cao, Salam Alabdullatif, Syed Ali Zaib, Zhe Wang and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Yichen WangSection of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
Irzam SarfrazBioinformatics Program, Boston University, Boston, MA, USA.
Nida PervaizSection of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
Rui HongBioinformatics Program, Boston University, Boston, MA, USA.
Yusuke KogaBioinformatics Program, Boston University, Boston, MA, USA.
Vidya AkavoorSoftware & Application Innovation Lab, Rafik B. Hariri Institute for Computing and Computational Science and Engineering, Boston, MA, USA.
Xinyun CaoSoftware & Application Innovation Lab, Rafik B. Hariri Institute for Computing and Computational Science and Engineering, Boston, MA, USA.
Salam AlabdullatifSection of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
Syed Ali ZaibSection of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
Zhe WangBioinformatics Program, Boston University, Boston, MA, USA.
Frederick JansenSoftware & Application Innovation Lab, Rafik B. Hariri Institute for Computing and Computational Science and Engineering, Boston, MA, USA.
Masanao YajimaDepartment of Mathematics and Statistics, Boston University, Boston, MA, USA.
W Evan JohnsonBioinformatics Program, Boston University, Boston, MA, USA.
Joshua D CampbellBioinformatics Program, Boston University, Boston, MA, USA.

Funding

Removing batch effects in high-throughput biomedical studiesR01GM127430 · NIGMS · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · PI William Evan Johnson · 2018 to 2026
$2.4M
An interactive analysis toolkit for single cell RNA-seq in cancer researchU01CA220413 · NCI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI JOHNSON, WILLIAM EVAN · 2017 to 2019
$1.2M
Integrative clustering of cells and samples using multi-modal single-cell dataR01LM013154 · NLM · BOSTON UNIVERSITY MEDICAL CAMPUS · PI CAMPBELL, JOSHUA D, YAJIMA, MASANAO · 2019 to 2021
$1.1M
NCI NIH HHS U01 CA220413NIGMS NIH HHS R01 GM127430NLM NIH HHS R01 LM013154
6 · The paper itself

Abstract

Analysis of single-cell RNA sequencing (scRNA-seq) data can reveal novel insights into the heterogeneity of complex biological systems. Many tools and workflows have been developed to perform different types of analyses. However, these tools are spread across different packages or programming environments, rely on different underlying data structures, and can only be utilized by people with knowledge of programming languages. In the Single-Cell Toolkit 2 (SCTK2), we have integrated a variety of popular tools and workflows to perform various aspects of scRNA-seq analysis. All tools and workflows can be run in the R console or using an intuitive graphical user interface built with R/Shiny. HTML reports generated with Rmarkdown can be used to document and recapitulate individual steps or entire analysis workflows. We show that the toolkit offers more features when compared with existing tools and allows for a seamless analysis of scRNA-seq data for non-computational users.

Indexed as

analysisbioinformaticsgenomicgraphical user interfaceinteractiveinteroperabilitysingle cellsoftwaretoolkittranscriptomic

Identifiers

PMID37602214
PMCPMC10436054

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.