ArticleGenome biology2024
SCIPAC: quantitative estimation of cell-phenotype associations.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- PhenoMapR: scalable mapping of sample phenotypes to single-cell, spatial, and bulk transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
- Tumor-derived ITGB3 shapes a primed but functionally constrained NK cell state in breast cancer through HLA-E-NKG2A signaling.Molecular biology reports · 2026Article
- Pan-cancer single-cell atlases of mouse and human tumor-associated dendritic cells.Nature communications · 2026Article
- Multi-omics mapping identifies a C/EBPβ-S100a4⁺ macrophage axis as a therapeutic target in acute spinal cord injury.Journal of translational medicine · 2026Article
- Multi-Scale Mapping of Gene Expression from Whole-slide Images for Identifying Phenotype-Associated Subpopulations.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Case Report: dynamic monocyte reprogramming during ALSS therapy in type B HBV-ACLF revealed by single-cell transcriptomics.Frontiers in immunology · 2026Article
- Multimodal integration of pathomics and single-cell transcriptomics reveals CDC20 as a link between prostate cancer recurrence and benzo[a]pyrene-responsive phenotypes.Frontiers in cell and developmental biology · 2026Article
- UDA-seq: universal droplet microfluidics-based combinatorial indexing for massive-scale multimodal single-cell sequencing.Nature methods · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Numerous algorithms have been proposed to identify cell types in single-cell RNA sequencing data, yet a fundamental problem remains: determining associations between cells and phenotypes such as cancer. We develop SCIPAC, the first algorithm that quantitatively estimates the association between each cell in single-cell data and a phenotype. SCIPAC also provides a p-value for each association and applies to data with virtually any type of phenotype. We demonstrate SCIPAC's accuracy in simulated data. On four real cancerous or noncancerous datasets, insights from SCIPAC help interpret the data and generate new hypotheses. SCIPAC requires minimum tuning and is computationally very fast.
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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.