ArticleNature communications2024
SiFT: uncovering hidden biological processes by probabilistic filtering of single-cell data.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Integrative multi-omics analysis identifies SELL and PRF1 as key immune biomarkers and therapeutic targets in tuberculosis.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Integrated transcriptomic and in silico structural analysis identifies NFS1 and HSPA9 as potential regulators associated with mitochondrial ferroptosis and cell death resistance in colorectal cancer.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Transcriptomic profiling and structural characterization reveal UQCRC1 and COX4I1 as key mitochondrial regulators associated with oxidative stress in multiple sclerosis.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Machine learning-based multi-omic analysis identifies CEP55, DLGAP5, and EZH2 as regulated cell death biomarkers linked to immunotherapy resistance in hepatocellular carcinoma.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Transcriptomic and multi-layer variant analysis identifies STAT3 and HIF1A as central regulators of regulated cell death pathways in lung squamous cell carcinoma.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Integrative transcriptomic and structural modeling reveal CASP1, TLR3, PYCARD, and CD274 as immune-modulatory drivers in breast cancer.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- CellUntangler: Separating distinct biological signals in single-cell data with deep generative models.Cell genomics · 2026Article
- Comprehensive In Silico Analysis of the CXCL12, C55Y Mutation Reveals Structural and Functional Disruption in CXCR4/CXCR7 Signaling.Evolutionary bioinformatics online · 2026Article
- SiFT: uncovering hidden biological processes by probabilistic filtering of single-cell data.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cellular populations simultaneously encode multiple biological attributes, including spatial configuration, temporal trajectories, and cell-cell interactions. Some of these signals may be overshadowed by others and harder to recover, despite the great progress made to computationally reconstruct biological processes from single-cell data. To address this, we present SiFT, a kernel-based projection method for filtering biological signals in single-cell data, thus uncovering underlying biological processes. SiFT applies to a wide range of tasks, from the removal of unwanted variation in the data to revealing hidden biological structures. We demonstrate how SiFT enhances the liver circadian signal by filtering spatial zonation, recovers regenerative cell subpopulations in spatially-resolved liver data, and exposes COVID-19 disease-related cells, pathways, and dynamics by filtering healthy reference signals. SiFT performs the correction at the gene expression level, can scale to large datasets, and compares favorably to state-of-the-art methods.
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.