Classification of low quality cells from single-cell RNA-seq data
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Summary
This work presents a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features, which improves classification accuracy by over 30 % compared to traditional methods.
- Type
- article
- Published
- 2016-02-17
- Cited by
- 621
- References
- 55
- Access
- Open access
- OpenAlex
- https://openalex.org/W2286142055
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:15559440
Keywords
Biology, Embryonic stem cell, Computational biology, RNA, RNA-Seq
References
- Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
- RNA-Seq: a revolutionary tool for transcriptomics
- Pseudo-temporal ordering of individual cells reveals dynamics and regulators of cell fate decisions
- Deterministic and Stochastic Allele Specific Gene Expression in Single Mouse Blastomeres
- Single Cell Genomics: Advances and Future Perspectives
- Single-Cell RNA Sequencing Reveals T Helper Cells Synthesizing Steroids De Novo to Contribute to Immune Homeostasis
- Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells
- Smart-seq2 for sensitive full-length transcriptome profiling in single cells
- Count-based differential expression analysis of RNA sequencing data using R and Bioconductor
- Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells
- Accounting for technical noise in single-cell RNA-seq experiments
- Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing
- Development and applications of single cell transcriptome analysis
- RNA sequencing: advances, challenges and opportunities
- Reconstructing lineage hierarchies of the distal lung epithelium using single cell RNA-seq
- Massively parallel single cell RNA-Seq for marker-free decomposition of tissues into cell types
- Progressive increase in mtDNA 3243A>G heteroplasmy causes abrupt transcriptional reprogramming
- Quantitative single-cell RNA-seq with unique molecular identifiers
- Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing
- Multivariate outlier detection in exploration geochemistry
Cited by
- Benchmarking full-length transcript single cell mRNA sequencing protocols
- Design and computational analysis of single-cell RNA-sequencing experiments
- Exploiting single-cell expression to characterize co-expression replicability
- Single-Cell Transcriptome Analysis of Developing and Regenerating Spiral Ganglion Neurons
- Transcriptome analysis reveals rod/cone photoreceptor specific signatures across mammalian retinas.
- A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor
- Single-Cell Transcriptomics Bioinformatics and Computational Challenges
- Single-Cell Isolation by Modular Single-Cell Pipette for RNA-Sequencing
- Exploring viral infection using single-cell sequencing.
- Single-cell transcriptomes identify human islet cell signatures and reveal cell-type–specific expression changes in type 2 diabetes
- PRODUCTION OF A PRELIMINARY QUALITY CONTROL PIPELINE FOR SINGLE NUCLEI RNA-SEQ AND ITS APPLICATION IN THE ANALYSIS OF CELL TYPE DIVERSITY OF POST-MORTEM HUMAN BRAIN NEOCORTEX
- Revealing the vectors of cellular identity with single-cell genomics
- Human dendritic cells (DCs) are derived from distinct circulating precursors that are precommitted to become CD1c+ or CD141+ DCs
- How Single-Cell Genomics Is Changing Evolutionary and Developmental Biology.
- Single cell transcriptomics reveals unanticipated features of early hematopoietic precursors
- Transcriptome-wide analysis in cells and tissues
- A cost effective 5΄ selective single cell transcriptome profiling approach with improved UMI design
- Seq-Well: A Portable, Low-Cost Platform for High-Throughput Single-Cell RNA-Seq of Low-Input Samples
- Single-Cell Transcriptome Analysis of Neural Stem Cells
- Cell fixation and preservation for droplet-based single-cell transcriptomics
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