ReviewFEBS letters2017
Computational approaches for interpreting scRNA-seq data.
Review in FEBS letters, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
What it found
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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.
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Who cites it
54 citing papers in PubMed.
- Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications.Genomics & informatics · 2025Review
- Advances in Microfluidic Single-Cell RNA Sequencing and Spatial Transcriptomics.Micromachines · 2025Review
- Article
- scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.PLoS computational biology · 2024Article
- K-nearest-neighbors induced topological PCA for single cell RNA-sequence data analysis.Computers in biology and medicine · 2024Article
- Article
- Generative modeling of single-cell gene expression for dose-dependent chemical perturbations.Patterns (New York, N.Y.) · 2023Article
- Characterization of cell-fate decision landscapes by estimating transcription factor dynamics.Cell reports methods · 2023Article
- Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer's disease.Communications biology · 2023Article
- Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments.Nature communications · 2022Article
- Single-cell RNA-sequencing data analysis reveals a highly correlated triphasic transcriptional response to SARS-CoV-2 infection.Communications biology · 2022Article
- Analysis of Tumor-Infiltrating T-Cell Transcriptomes Reveal a Unique Genetic Signature across Different Types of Cancer.International journal of molecular sciences · 2022Article
- RNA velocity unraveled.PLoS computational biology · 2022Article
- ccImpute: an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data.BMC bioinformatics · 2022Article
- Review
- Analysis of Single-Cell Transcriptome Data in Drosophila.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Single-Cell Analysis of the Transcriptome and Epigenome.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Single-Cell Transcriptomics Uncovers Cellular Heterogeneity, Mechanisms, and Therapeutic Targets for Parkinson's Disease.Frontiers in genetics · 2022Article
- Characterization of hormone-producing cell types in the teleost pituitary gland using single-cell RNA-seq.Scientific data · 2021Article
- Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent developments in high-throughput single-cell RNA sequencing technology (scRNA-seq) have enabled the generation of vast amounts of transcriptomic data at cellular resolution. With these advances come new modes of data analysis, building on high-dimensional data mining techniques. Here, we consider biological questions for which scRNA-seq data is used, both at a cell and gene level, and describe tools available for these types of analyses. This is an exciting and rapidly evolving field, where clustering, pseudotime inference, branching inference and gene-level analyses are particularly informative areas of computational analysis.
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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.