ReviewNature reviews. Genetics2015
Computational and analytical challenges in single-cell transcriptomics.
Review in Nature reviews. Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 633 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.
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Who cites it
633 citing papers in PubMed.
- scGEN: Leveraging an Adaptive Weighting Strategy for Challenging Cell Clustering in Single-Cell RNA-seq.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multi‑omics insights into uveitis: From mechanisms to precision medicine (Review).International journal of molecular medicine · 2026Review
- Integrative evidence-knowledge marker selection enhances LLM-based cell type annotation in single-cell RNA-seq analysis.BioData mining · 2026Article
- Article
- A pre-trained large generative model for translating single-cell transcriptomes to proteomes.Nature biomedical engineering · 2026Article
- Article
- CoReGRN: a context-aware post-processing framework for gene regulatory network inference revealing regulatory signatures in HIV-Leishmaniasis.BMC genomics · 2026Article
- PLNFGL: joint estimation of multi-condition gene networks from single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2026Article
- Immune Landscape and Tumour Heterogeneity in Ovarian Cancer: Insights From Single-Cell RNA Sequencing.Journal of cellular and molecular medicine · 2026Review
- Pro-regenerative fingerprints identified in a sub-population of adult mouse cardiomyocytes by integrative single-cell proteomics.Genome biology · 2026Article
- Simultaneous modeling of chromatin conformation changes from multiple single-cell interaction maps with ChromMovie.Genome research · 2026Article
- Beyond the Cell Atlas: Functional Communities as the Essential Pathologic Units Driving Kidney Disease.Journal of the American Society of Nephrology : JASN · 2026Review
- Spatial Mapping of Single Cells via Correlation and Importance Between Cells and Spots.Interdisciplinary sciences, computational life sciences · 2026Article
- A Mouthful of Genomic Data: Single-Cell Insights into Salivary Gland Biology and Disease.Biology · 2026Review
- MitoChontrol: Adaptive mitochondrial filtering for robust single-cell RNA sequencing quality control.bioRxiv : the preprint server for biology · 2026Article
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.Cell reports methods · 2026Article
- Cell Type Populations for 3D Anatomical Structures of the Human Reference Atlas.Scientific data · 2026Article
- scCMA: A Contrastive Masked Autoencoder Framework for Robust Representation Learning of scRNA-seq Data.Interdisciplinary sciences, computational life sciences · 2026Article
- GDSim: accurate simulation for single-cell transcriptomes based on the guided diffusion model.Briefings in bioinformatics · 2026Article
- CellUntangler: Separating distinct biological signals in single-cell data with deep generative models.Cell genomics · 2026Article
573 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of high-throughput RNA sequencing (RNA-seq) at the single-cell level has already led to profound new discoveries in biology, ranging from the identification of novel cell types to the study of global patterns of stochastic gene expression. Alongside the technological breakthroughs that have facilitated the large-scale generation of single-cell transcriptomic data, it is important to consider the specific computational and analytical challenges that still have to be overcome. Although some tools for analysing RNA-seq data from bulk cell populations can be readily applied to single-cell RNA-seq data, many new computational strategies are required to fully exploit this data type and to enable a comprehensive yet detailed study of gene expression at the single-cell level.
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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.