ArticleGenomics2021
SwarnSeq: An improved statistical approach for differential expression analysis of single-cell RNA-seq data.
Article in Genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed.
- Article
- PbImpute: Precise Zero Discrimination and Balanced Imputation in Single-Cell RNA Sequencing Data.Journal of chemical information and modeling · 2025Article
- Unveiling patterns in spatial transcriptomics data: a novel approach utilizing graph attention autoencoder and multiscale deep subspace clustering network.GigaScience · 2025Article
- Identify Diabetes-related Targets based on ForgeNet_GPC.Current computer-aided drug design · 2024Article
- Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges.Entropy (Basel, Switzerland) · 2022Review
- Benchmarking of a Bayesian single cell RNAseq differential gene expression test for dose-response study designs.Nucleic acids research · 2022Article
- Article
- Article
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
Single-cell RNA sequencing (scRNA-seq) is a powerful technology that is capable of generating gene expression data at the resolution of individual cell. The scRNA-seq data is characterized by the presence of dropout events, which severely bias the results if they remain unaddressed. There are limited Differential Expression (DE) approaches which consider the biological processes, which lead to dropout events, in the modeling process. So, we develop, SwarnSeq, an improved method for DE, and other downstream analysis that considers the molecular capture process in scRNA-seq data modeling. The performance of the proposed method is benchmarked with 11 existing methods on 10 different real scRNA-seq datasets under three comparison settings. We demonstrate that SwarnSeq method has improved performance over the 11 existing methods. This improvement is consistently observed across several public scRNA-seq datasets generated using different scRNA-seq protocols. The external spike-ins data can be used in the SwarnSeq method to enhance its performance. AVAILABILITY AND IMPLEMENTATION: The method is implemented as a publicly available R package available at https://github.com/sam-uofl/SwarnSeq.
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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.