ArticleNucleic acids research2018
BCseq: accurate single cell RNA-seq quantification with bias correction.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- PPIA-coExp: Discovering Context-Specific Biomarkers Based on Protein-Protein Interactions, Co-Expression Networks, and Expression Data.International journal of molecular sciences · 2024Article
- Deciphering single-cell gene expression variability and its role in drug response.Human molecular genetics · 2024Article
- Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets.Genome biology · 2023Review
- scRNA-seq data analysis method to improve analysis performance.IET nanobiotechnology · 2023Review
- Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications.Military Medical Research · 2022Review
- hECA: The cell-centric assembly of a cell atlas.iScience · 2022Article
- Advances in application of single-cell RNA sequencing in cardiovascular research.Frontiers in cardiovascular medicine · 2022Review
- Biological Sequence Classification: A Review on Data and General Methods.Research (Washington, D.C.) · 2022Article
- Single-Cell Genomics: Enabling the Functional Elucidation of Infectious Diseases in Multi-Cell Genomes.Pathogens (Basel, Switzerland) · 2021Review
- Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision.Briefings in bioinformatics · 2021Article
- Emerging Single-Cell Technological Approaches to Investigate Chromatin Dynamics and Centromere Regulation in Human Health and Disease.International journal of molecular sciences · 2021Review
- Alternative splicing: Human disease and quantitative analysis from high-throughput sequencing.Computational and structural biotechnology journal · 2021Review
- Quantile regression for challenging cases of eQTL mapping.Briefings in bioinformatics · 2020Article
- Single-cell sequencing and its applications in head and neck cancer.Oral oncology · 2019Review
- SCMarker: Ab initio marker selection for single cell transcriptome profiling.PLoS computational biology · 2019Article
- Developmental Xist induction is mediated by enhanced splicing.Nucleic acids research · 2019Article
- Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity.Frontiers in genetics · 2019Article
- Single-Cell RNA-Seq Technologies and Related Computational Data Analysis.Frontiers in genetics · 2019Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
With rapid technical advances, single cell RNA-seq (scRNA-seq) has been used to detect cell subtypes exhibiting distinct gene expression profiles and to trace cell transitions in development and disease. However, the potential of scRNA-seq for new discoveries is constrained by the robustness of subsequent data analysis. Here we propose a robust model, BCseq (bias-corrected sequencing analysis), to accurately quantify gene expression from scRNA-seq. BCseq corrects inherent bias of scRNA-seq in a data-adaptive manner and effectively removes technical noise. BCseq rescues dropouts through weighted consideration of similar cells. Cells with higher sequencing depths contribute more to the quantification nonlinearly. Furthermore, BCseq assigns a quality score for the expression of each gene in each cell, providing users an objective measure to select genes for downstream analysis. In comparison to existing scRNA-seq methods, BCseq demonstrates increased robustness in detection of differentially expressed (DE) genes and cell subtype classification.
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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.