ArticleScientific reports2017
Impact of sequencing depth and read length on single cell RNA sequencing data of T cells.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- A Mouthful of Genomic Data: Single-Cell Insights into Salivary Gland Biology and Disease.Biology · 2026Review
- Neurons differentially upregulate type 2 immune cytokines and interleukin-4 receptor subtypes during neuroinflammation.Acta neuropathologica communications · 2026Article
- Microglial CX3CR1 deficiency regulates the selective vulnerability of cone photoreceptors via STAT3/CCL-ACKR1 signaling in the mouse retina.Experimental & molecular medicine · 2026Article
- Development and validation of a prognostic model based on T cell signature genes in colon cancer using single-cell RNA sequencing.Clinical and experimental medicine · 2026Article
- DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads.Nature communications · 2025Article
- Single cell analysis identifies distinct CD4 + T cells associated with the pathobiology of pediatric obesity related asthma.Scientific reports · 2025Article
- Single-cell RNA sequencing reveals cellular diversity and gene expression dynamics in maize root development.Frontiers in plant science · 2025Article
- MetaCompare 2.0: differential ranking of ecological and human health resistome risks.FEMS microbiology ecology · 2024Article
- Advances in long-read single-cell transcriptomics.Human genetics · 2024Review
- Optimal sequencing budget allocation for trajectory reconstruction of single cells.Bioinformatics (Oxford, England) · 2024Article
- Article
- Review
- scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information.Cell & bioscience · 2022Article
- De Novo Transcriptome Sequencing and Analysis of Differential Gene Expression among Various Stages of Tail Regeneration inJournal of developmental biology · 2022Article
- Assessing reproducibility of high-throughput experiments in the case of missing data.Statistics in medicine · 2022Article
- Progress in multicellular human cardiac organoids for clinical applications.Cell stem cell · 2022Review
- A novel method for single-cell data imputation using subspace regression.Scientific reports · 2022Article
- Mapping the expression of an ANK3 isoform associated with bipolar disorder in the human brain.Translational psychiatry · 2022Article
- High-throughput and single-cell T cell receptor sequencing technologies.Nature methods · 2021Review
- Polar Gini Curve: A Technique to Discover Gene Expression Spatial Patterns from Single-cell RNA-seq Data.Genomics, proteomics & bioinformatics · 2021Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Single cell RNA sequencing (scRNA-seq) provides great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant T cell subsets, and the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, e.g. RNA library capture, cell quality, and sequencing output affect the quality of scRNA-seq data. We studied the effects of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 single cells from 8 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (<50 bp), but these featured higher technical variability compared to profiles from longer reads. Successful TCRαβ reconstruction was achieved for 6 datasets (81% - 100%) with at least 0.25 millions (PE) reads of length >50 bp, while it failed for datasets with <30 bp reads. Sufficient read length and sequencing depth can control technical noise to enable accurate identification of TCRαβ and gene expression profiles from scRNA-seq data of T cells.
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