ArticleBioinformatics (Oxford, England)2022
scFeatures: multi-view representations of single-cell and spatial data for disease outcome prediction.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Deep interpretable learning of sample representations for characterizing disease states in single-cell transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Tissueformer: extending single-cell foundation models to predict population-level phenotypes.BMC bioinformatics · 2026Article
- Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies.Briefings in bioinformatics · 2026Review
- Distinct immunologic patterns of response and resistance to anti-PD-1/PD-L1-based immunotherapy in patients with soft tissue sarcoma.Frontiers in immunology · 2026Article
- Harnessing the potential of spatial statistics for spatial omics data with pasta.Nucleic acids research · 2025Article
- Spatial gene expression at single-cell resolution from histology using deep learning with GHIST.Nature methods · 2025Article
- TissueFormer: a neural network for labeling tissue from grouped single-cell RNA profiles.bioRxiv : the preprint server for biology · 2025Article
- Integrating Spatially-Resolved Transcriptomics Data Across Tissues and Individuals: Challenges and Opportunities.Small methods · 2025Review
- Multi-task benchmarking of spatially resolved gene expression simulation models.Genome biology · 2025Article
- Scope+: an open source generalizable architecture for single-cell RNA-seq atlases at sample and cell levels.Bioinformatics (Oxford, England) · 2024Article
- Article
- Evaluation of deep learning-based feature selection for single-cell RNA sequencing data analysis.Genome biology · 2023Article
- Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data.Briefings in bioinformatics · 2023Article
- scAB detects multiresolution cell states with clinical significance by integrating single-cell genomics and bulk sequencing data.Nucleic acids research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationWith the recent surge of large-cohort scale single cell research, it is of critical importance that analytical methods can fully utilize the comprehensive characterization of cellular systems that single cell technologies produce to provide insights into samples from individuals. Currently, there is little consensus on the best ways to compress information from the complex data structures of these technologies to summary statistics that represent each sample (e.g. individuals).
resultsHere, we present scFeatures, an approach that creates interpretable cellular and molecular representations of single-cell and spatial data at the sample level. We demonstrate that summarizing a broad collection of features at the sample level is both important for understanding underlying disease mechanisms in different experimental studies and for accurately classifying disease status of individuals. AVAILABILITY AND IMPLEMENTATION: scFeatures is publicly available as an R package at https://github.com/SydneyBioX/scFeatures. All data used in this study are publicly available with accession ID reported in the Section 2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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.