ArticleGenome research2021
Automated quality control and cell identification of droplet-based single-cell data using dropkick.
Article in Genome research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed.
- Tobemstomig, a Novel Bispecific Antibody, Preferentially Blocks PD-1 and LAG-3 on CD8 TILs to Expand Stem-like T Cells for Sustained Tumor Control.Cancer research communications · 2026Article
- Interrupting T-Cell Memory Ameliorates Exaggerated Metabolic Response to Weight Cycling.Diabetes · 2026Article
- A skeletal muscle atlas shows neuromuscular junction adaptations to growth and atrophy.Developmental cell · 2026Article
- Single-cell atlas of the transcriptome and chromatin accessibility in the human retina.Nature genetics · 2026Article
- Fetal reversion from diverse lineages sustains the intestinal stem cell pool and confers stress resilience.Communications biology · 2026Article
- ABCA4-mutant human retinal organoids sequencing reveals organoids application in inherited retinal diseases.Experimental eye research · 2025Article
- CelLink: integrating single-cell multi-omics data with weak feature linkage and imbalanced cell populations.Nucleic acids research · 2025Article
- AI-Driven Quality Monitoring and Control in Stem Cell Cultures: A Comprehensive Review.Biotechnology journal · 2025Review
- Pancreatic islet β-cell subtypes are derived from biochemically-distinct and nutritionally-regulated islet progenitors.Nature communications · 2025Article
- Multiomic spatial atlas shows deleted in malignant brain tumors 1 (DMBT1) glycoprotein is lost in colonic dysplasia.The Journal of pathology · 2025Article
- Comparison of Lean, Obese, and Weight-Loss Models Reveals TREM2 Deficiency Attenuates Breast Cancer Growth Uniquely in Lean Mice and Alters Clonal T-cell Populations.Cancer research · 2025Article
- Divergence in cellular markers observed in single-cell transcriptomics datasets between cultured primary trabecular meshwork cells and tissues.Scientific data · 2025Article
- Atopic skin inflammation promotes systemic anaphylactic responses via IL-13 signaling in conventional dendritic cells.Research square · 2025Article
- Multiplexing cortical brain organoids for the longitudinal dissection of developmental traits at single-cell resolution.Nature methods · 2025Article
- A Roadmap for Selecting and Utilizing Optimal Features in scRNA Sequencing Data Analysis for Stem Cell Research: A Comprehensive Review.International journal of stem cells · 2024Review
- Trem2 deficiency attenuates breast cancer tumor growth in lean, but not obese or weight loss, mice and is associated with alterations of clonal T cell populations.bioRxiv : the preprint server for biology · 2024Article
- MTGR1 is required to maintain small intestinal stem cell populations.Cell death and differentiation · 2024Article
- Comprehensive single-cell atlas of the mouse retina.iScience · 2024Article
- The Advancement and Application of the Single-Cell Transcriptome in Biological and Medical Research.Biology · 2024Review
- To be or not to be - Decoding the Trabecular Meshwork Cell Identity.bioRxiv : the preprint server for biology · 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
5 authors.
Funding
Abstract
A major challenge for droplet-based single-cell sequencing technologies is distinguishing true cells from uninformative barcodes in data sets with disparate library sizes confounded by high technical noise (i.e., batch-specific ambient RNA). We present dropkick, a fully automated software tool for quality control and filtering of single-cell RNA sequencing (scRNA-seq) data with a focus on excluding ambient barcodes and recovering real cells bordering the quality threshold. By automatically determining data set-specific training labels based on predictive global heuristics, dropkick learns a gene-based representation of real cells and ambient noise, calculating a cell probability score for each barcode. Using simulated and real-world scRNA-seq data, we benchmarked dropkick against conventional thresholding approaches and EmptyDrops, a popular computational method, showing greater recovery of rare cell types and exclusion of empty droplets and noisy, uninformative barcodes. We show for both low- and high-background data sets that dropkick's weakly supervised model reliably learns which genes are enriched in ambient barcodes and draws a multidimensional boundary that is more robust to data set-specific variation than existing filtering approaches. dropkick provides a fast, automated tool for reproducible cell identification from scRNA-seq data that is critical to downstream analysis and compatible with popular single-cell Python packages.
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.