ReviewNeuro-oncology advances2026
Pitfalls in analysis and interpretation of single-cell RNA-seq data in cancer.
Review in Neuro-oncology advances, 2026. 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.
- Special Issue: The Evolving Landscape of Single-Cell Genomics in CNS and PNS Oncology.Neuro-oncology advances · 2026Review
- NLRP12 in cancer: a context-dependent regulator of tumor progression, immunity, and metabolism.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell and single-nuclei RNA-seq (sc/snRNA-seq) have become a central approach in cancer research, and their widespread impact has been enabled by various computational tools developed specifically for sc/snRNA-seq analysis. Nevertheless, proper analysis and interpretation of sc/snRNA-seq data requires significant expertise, and the inadequate utility of certain computational methods may lead to dubious results. To mitigate these issues, it is important to recognize the limitations of sc/snRNA-seq data, the assumptions of common methods, and to perform robust analysis. Here, I describe common pitfalls in cancer sc/snRNA-seq analysis and discuss ways to overcome them. Among others, this includes a discussion of potential errors in statistical analysis, in inference of chromosomal aberrations, in trajectory analysis, and in signature-based analysis of bulk RNA-seq data. This review may help readers to avoid common pitfalls and to perform informative analysis and careful interpretation of sc/snRNA-seq datasets in cancer.
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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.