ArticleNature communications2020
CaSpER identifies and visualizes CNV events by integrative analysis of single-cell or bulk RNA-sequencing data.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 1 of them a synthesis that pooled it.
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
110 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- Animal-origin-free medium enhances G2/M progression while maintaining identity and genomic stability in human adipose-derived MSCs.Stem cell research & therapy · 2026Article
- Single-cell and spatial RNA sequencing in prostate cancer.Nature reviews. Urology · 2026Review
- FFPE-CUTAC: A Single Assay, Multiple Layers.bioRxiv : the preprint server for biology · 2026Article
- Benchmarking copy number alteration inference methods for spatial transcriptomics.Nature communications · 2026Article
- Robust and generalizable CNV detection for single-cell sequencing assays.Nucleic acids research · 2026Article
- Computational strategies for copy number variation detection, disease association, and beyond.Genome biology · 2026Review
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- Beyond Histology: A Unified Transcriptomic Atlas Defines Lung Cancer Biologic States and Subtypes.bioRxiv : the preprint server for biology · 2026Article
- Integrated transcriptomic landscape of medulloblastoma and ependymoma reveals novel tumor subtype-specific biology.Neuro-oncology · 2026Article
- Self-organizing maps for allele specific expression data reconstruction and identification of anomalous genomic regions.Frontiers in bioinformatics · 2026Article
- Integrative Modeling of Read Depth and B-Allele Frequency Improves Single-Cell Copy Number Calling from Targeted DNA Sequencing Panels.Computational and structural biotechnology journal · 2026Article
- Subgroup of meningiomas involving FOS and FOSB gene fusions.Nature communications · 2025Article
- Benchmarking scRNA-seq copy number variation callers.Nature communications · 2025Article
- RCANE: a deep learning algorithm for whole-genome pan-cancer somatic copy number aberration prediction using RNA-seq data.Communications biology · 2025Article
- CCNV: a user-friendly R package enabling large-scale cumulative copy number variation analyses of DNA methylation data.BMC bioinformatics · 2025Article
- Cancer subclone detection based on DNA copy number in single-cell and spatial omic sequencing data.Nature methods · 2025Article
- Single-cell RNA sequencing reveals different cellular states in malignant cells and the tumor microenvironment in primary and metastatic ER-positive breast cancer.NPJ breast cancer · 2025Article
- Identification of malignant cells in single-cell transcriptomics data.Communications biology · 2025Review
50 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
Abstract
RNA sequencing experiments generate large amounts of information about expression levels of genes. Although they are mainly used for quantifying expression levels, they contain much more biologically important information such as copy number variants (CNVs). Here, we present CaSpER, a signal processing approach for identification, visualization, and integrative analysis of focal and large-scale CNV events in multiscale resolution using either bulk or single-cell RNA sequencing data. CaSpER integrates the multiscale smoothing of expression signal and allelic shift signals for CNV calling. The allelic shift signal measures the loss-of-heterozygosity (LOH) which is valuable for CNV identification. CaSpER employs an efficient methodology for the generation of a genome-wide B-allele frequency (BAF) signal profile from the reads and utilizes it for correction of CNVs calls. CaSpER increases the utility of RNA-sequencing datasets and complements other tools for complete characterization and visualization of the genomic and transcriptomic landscape of single cell and bulk RNA sequencing 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.