ReviewInternational journal of stem cells2024
A Roadmap for Selecting and Utilizing Optimal Features in scRNA Sequencing Data Analysis for Stem Cell Research: A Comprehensive Review.
Review in International journal of stem cells, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- Single-cell and bulk transcriptomic analyses identify B-cell senescence-associated biomarkers in papillary thyroid carcinoma.Translational cancer research · 2026Article
- Feature selection methods affect the performance of scRNA-seq data integration and querying.Nature methods · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stem cells and the cells they produce are unique because they vary from one cell to another. Traditional methods of studying cells often overlook these differences. However, the development of new technologies for studying individual cells has greatly changed biological research in recent years. Among these innovations, single-cell RNA sequencing (scRNA-seq) stands out. This technique allows scientists to examine the activity of genes in each cell, across thousands or even millions of cells. This makes it possible to understand the diversity of cells, identify new types of cells, and see how cells differ across different tissues, individuals, species, times, and conditions. This paper discusses the importance of scRNA-seq and the computational tools and software that are essential for analyzing the vast amounts of data generated by scRNA-seq studies. Our goal is to provide practical advice for bioinformaticians and biologists who are using scRNA-seq to study stem cells. We offer an overview of the scRNA-seq field, including the tools available, how they can be used, and how to present the results of these studies effectively. Our findings include a detailed overview and classification of tools used in scRNA-seq analysis, based on a review of 2,733 scientific publications. This review is complemented by information from the scRNA-tools database, which lists over 1,400 tools for analyzing scRNA-seq data. This database is an invaluable resource for researchers, offering a wide range of options for analyzing their scRNA-seq data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.