ArticleNature communications2024
Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 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
13 citing papers in PubMed.
- Human HSPC clones balance stochastic diversification with cytokine-induced differentiation.Blood · 2026Article
- Computational approaches for multimodal lineage tracing.Nature reviews. Genetics · 2026Review
- Article
- Clonal memory of cell division in humans diverges between healthy haematopoiesis and acute myeloid leukaemia.Nature communications · 2026Article
- Charting single-cell lineages with synthetic and natural barcodes.Nature reviews. Genetics · 2026Review
- Leveraging Lineage Barcodes as Natural Augmentations for Contrastive Learning of Cell Fate in scRNA-seq Data.bioRxiv : the preprint server for biology · 2026Article
- Perivascular adipose single-cell atlas identifies CD55Nature communications · 2026Article
- A sequence knowledge-guided deep learning method for single-cell multi-omics translation.Genome biology · 2026Article
- An information content principle explains regulatory patterns of gene expression across human tissues.Nature communications · 2026Article
- Single-cell spatial transcriptomics reveals clonal smooth muscle cell heterogeneity and programs associated with atherosclerotic plaque calcification.Clinical and experimental medicine · 2026Article
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets.Nature communications · 2024Article
- Machine learning based lineage tree reconstruction improved with knowledge of higher level relationships between cells and genomic barcodes.NAR genomics and bioinformatics · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
Assigning single cell transcriptomes to cellular lineage trees by lineage tracing has transformed our understanding of differentiation during development, regeneration, and disease. However, lineage tracing is technically demanding, often restricted in time-resolution, and most scRNA-seq datasets are devoid of lineage information. Here we introduce Gene Expression Memory-based Lineage Inference (GEMLI), a computational tool allowing to robustly identify small to medium-sized cell lineages solely from scRNA-seq datasets. GEMLI allows to study heritable gene expression, to discriminate symmetric and asymmetric cell fate decisions and to reconstruct individual multicellular structures from pooled scRNA-seq datasets. In human breast cancer biopsies, GEMLI reveals previously unknown gene expression changes at the onset of cancer invasiveness. The universal applicability of GEMLI allows studying the role of small cell lineages in a wide range of physiological and pathological contexts, notably in vivo. GEMLI is available as an R package on GitHub ( https://github.com/UPSUTER/GEMLI ).
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.