ArticleFrontiers in genetics2019
Benchmark and Parameter Sensitivity Analysis of Single-Cell RNA Sequencing Clustering Methods.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed.
- Simultaneous modeling of chromatin conformation changes from multiple single-cell interaction maps with ChromMovie.Genome research · 2026Article
- Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.Genome biology · 2025Article
- scEVE: a single-cell RNA-seq ensemble clustering algorithm capitalizing on the differences of predictions between multiple clustering methods.NAR genomics and bioinformatics · 2025Article
- ChromMovie: A Molecular Dynamics Approach for Simultaneous Modeling of Chromatin Conformation Changes from Multiple Single-Cell Hi-C Maps.bioRxiv : the preprint server for biology · 2025Article
- A survey of biclustering and clustering methods in clustering different types of single-cell RNA sequencing data.Briefings in functional genomics · 2025Article
- The impact of dropouts in scRNAseq dense neighborhood analysis.Computational and structural biotechnology journal · 2025Article
- Two-Stage Multi-View Graph Spectral Clustering for Single-Cell RNA-Seq Data.Current genomics · 2025Article
- gSELECT: A novel pre-analysis machine-learning library enabling early hypothesis testing and predictive gene selection in single-cell data.Computational and structural biotechnology journal · 2025Article
- Decoding the mosaic of inflammatory bowel disease: Illuminating insights with single-cell RNA technology.Computational and structural biotechnology journal · 2024Review
- ESCHR: a hyperparameter-randomized ensemble approach for robust clustering across diverse datasets.Genome biology · 2024Article
- Binomial models uncover biological variation during feature selection of droplet-based single-cell RNA sequencing.PLoS computational biology · 2024Article
- CASCC: a co-expression-assisted single-cell RNA-seq data clustering method.Bioinformatics (Oxford, England) · 2024Article
- scRNA-seq Reveals Novel Genetic Pathways and Sex Chromosome Regulation in Tribolium Spermatogenesis.Genome biology and evolution · 2024Article
- Quantifying the clusterness and trajectoriness of single-cell RNA-seq data.PLoS computational biology · 2024Article
- Radiotranscriptomics of non-small cell lung carcinoma for assessing high-level clinical outcomes using a machine learning-derived multi-modal signature.Biomedical engineering online · 2023Article
- Robust, scalable, and informative clustering for diverse biological networks.Genome biology · 2023Article
- Statistical and machine learning methods for immunoprofiling based on single-cell data.Human vaccines & immunotherapeutics · 2023Review
- The shaky foundations of simulating single-cell RNA sequencing data.Genome biology · 2023Article
- Single-cell RNA-seq methods to interrogate virus-host interactions.Seminars in immunopathology · 2023Review
- How does the structure of data impact cell-cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data.Briefings in bioinformatics · 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
Single-cell RNA-seq (scRNAseq) is a powerful tool to study heterogeneity of cells. Recently, several clustering based methods have been proposed to identify distinct cell populations. These methods are based on different statistical models and usually require to perform several additional steps, such as preprocessing or dimension reduction, before applying the clustering algorithm. Individual steps are often controlled by method-specific parameters, permitting the method to be used in different modes on the same datasets, depending on the user choices. The large number of possibilities that these methods provide can intimidate non-expert users, since the available choices are not always clearly documented. In addition, to date, no large studies have invistigated the role and the impact that these choices can have in different experimental contexts. This work aims to provide new insights into the advantages and drawbacks of scRNAseq clustering methods and describe the ranges of possibilities that are offered to users. In particular, we provide an extensive evaluation of several methods with respect to different modes of usage and parameter settings by applying them to real and simulated datasets that vary in terms of dimensionality, number of cell populations or levels of noise. Remarkably, the results presented here show that great variability in the performance of the models is strongly attributed to the choice of the user-specific parameter settings. We describe several tendencies in the performance attributed to their modes of usage and different types of datasets, and identify which methods are strongly affected by data dimensionality in terms of computational time. Finally, we highlight some open challenges in scRNAseq data clustering, such as those related to the identification of the number of clusters.
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.