ReviewBiophysical reviews2024
Studying temporal dynamics of single cells: expression, lineage and regulatory networks.
Review in Biophysical reviews, 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.
- Trajectory-informed gene feature selection in single-cell analysis with SEEK-VFI.Cell reports methods · 2026Article
- Integrated Single-Cell and Bulk Transcriptomics Unveils Immune Profiles in Chick Erythroid Cells upon Avian PathogenicAnimals : an open access journal from MDPI · 2026Article
- BCTI: a Bayesian network-based method for revealing critical transitions in complex biological systems.PeerJ · 2026Article
- Trajectory-informed gene feature selection in single-cell analysis with SEEK-VFI.bioRxiv : the preprint server for biology · 2025Article
- InterVelo: a mutually enhancing model for estimating pseudotime and RNA velocity in multi-omic single-cell data.Bioinformatics (Oxford, England) · 2025Article
- Profiling the heterogeneity of microbial populations and communities at the single-cell level.mLife · 2025Review
- Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos.Nature communications · 2025Article
- Article
- Biophysical Reviews: peering into 2024.Biophysical reviews · 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
Abstract
Learning how multicellular organs are developed from single cells to different cell types is a fundamental problem in biology. With the high-throughput scRNA-seq technology, computational methods have been developed to reveal the temporal dynamics of single cells from transcriptomic data, from phenomena on cell trajectories to the underlying mechanism that formed the trajectory. There are several distinct families of computational methods including Trajectory Inference (TI), Lineage Tracing (LT), and Gene Regulatory Network (GRN) Inference which are involved in such studies. This review summarizes these computational approaches which use scRNA-seq data to study cell differentiation and cell fate specification as well as the advantages and limitations of different methods. We further discuss how GRNs can potentially affect cell fate decisions and trajectory structures. Supplementary Information: The online version contains supplementary material available at 10.1007/s12551-023-01090-5.
Indexed as
Identifiers
What Socratic 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 Socratic graph.