ReviewFrontiers in genetics2016
Single-Cell Transcriptomics Bioinformatics and Computational Challenges.
Review in Frontiers in genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 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
58 citing papers in PubMed.
- scZiva: imputation method for single-cell RNA-seq data with zero-inflated variational autoencoder.BMC bioinformatics · 2026Article
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- Adding highly variable genes to spatially variable genes can improve cell type clustering performance in spatial transcriptomics data.Bioinformatics advances · 2026Article
- Genomic Insights into Tumorigenesis in Newly Diagnosed Multiple Myeloma.Diagnostics (Basel, Switzerland) · 2025Article
- Advances and applications in single-cell and spatial genomics.Science China. Life sciences · 2025Review
- Exploring public cancer gene expression signatures across bulk, single-cell and spatial transcriptomics data with signifinder Bioconductor package.NAR genomics and bioinformatics · 2024Article
- Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective.Journal of pharmaceutical analysis · 2023Review
- IndepthPathway: an integrated tool for in-depth pathway enrichment analysis based on single-cell sequencing data.Bioinformatics (Oxford, England) · 2023Article
- Multi-omic analyses in immune cell development with lessons learned from T cell development.Frontiers in cell and developmental biology · 2023Review
- Unveiling the novel immune and molecular signatures of ovarian cancer: insights and innovations from single-cell sequencing.Frontiers in immunology · 2023Review
- Decoding brain memory formation by single-cell RNA sequencing.Briefings in bioinformatics · 2022Article
- Wound healing, fibroblast heterogeneity, and fibrosis.Cell stem cell · 2022Review
- ccImpute: an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data.BMC bioinformatics · 2022Article
- Review
- An Overview of Algorithms and Associated Applications for Single Cell RNA-Seq Data Imputation.Current genomics · 2021Review
- Polar Gini Curve: A Technique to Discover Gene Expression Spatial Patterns from Single-cell RNA-seq Data.Genomics, proteomics & bioinformatics · 2021Article
- GranatumX: A Community-engaging, Modularized, and Flexible Webtool for Single-cell Data Analysis.Genomics, proteomics & bioinformatics · 2021Article
- GECO: gene expression clustering optimization app for non-linear data visualization of patterns.BMC bioinformatics · 2021Article
- The current landscape of single-cell transcriptomics for cancer immunotherapy.The Journal of experimental medicine · 2021Review
- Understanding the Adult Mammalian Heart at Single-Cell RNA-Seq Resolution.Frontiers in cell and developmental biology · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The emerging single-cell RNA-Seq (scRNA-Seq) technology holds the promise to revolutionize our understanding of diseases and associated biological processes at an unprecedented resolution. It opens the door to reveal intercellular heterogeneity and has been employed to a variety of applications, ranging from characterizing cancer cells subpopulations to elucidating tumor resistance mechanisms. Parallel to improving experimental protocols to deal with technological issues, deriving new analytical methods to interpret the complexity in scRNA-Seq data is just as challenging. Here, we review current state-of-the-art bioinformatics tools and methods for scRNA-Seq analysis, as well as addressing some critical analytical challenges that the field faces.
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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.