ArticleNucleic acids research2018
PaSD-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation.
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 11 papers.
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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
11 citing papers in PubMed.
- A Tailing Method of Quality Control on MALBAC-Based Whole-Genome Amplification Products.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Analyzing somatic mutations by single-cell whole-genome sequencing.Nature protocols · 2024Review
- Somatic CNV Detection by Single-Cell Whole-Genome Sequencing in Postmortem Human Brain.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Comprehensive identification of somatic nucleotide variants in human brain tissue.Genome biology · 2021Article
- Large mosaic copy number variations confer autism risk.Nature neuroscience · 2021Article
- SCELLECTOR: ranking amplification bias in single cells using shallow sequencing.BMC bioinformatics · 2020Article
- Genome aging: somatic mutation in the brain links age-related decline with disease and nominates pathogenic mechanisms.Human molecular genetics · 2019Article
- A low cost and input tailing method of quality control on multiple annealing, and looping-based amplification cycles-based whole-genome amplification products.Journal of clinical laboratory analysis · 2019Article
- Haplotype phasing in single-cell DNA-sequencing data.Bioinformatics (Oxford, England) · 2018Article
- Sensitivity to sequencing depth in single-cell cancer genomics.Genome medicine · 2018Article
- Aging and neurodegeneration are associated with increased mutations in single human neurons.Science (New York, N.Y.) · 2018Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Single cell whole-genome sequencing (scWGS) is providing novel insights into the nature of genetic heterogeneity in normal and diseased cells. However, the whole-genome amplification process required for scWGS introduces biases into the resulting sequencing that can confound downstream analysis. Here, we present a statistical method, with an accompanying package PaSD-qc (Power Spectral Density-qc), that evaluates the properties and quality of single cell libraries. It uses a modified power spectral density to assess amplification uniformity, amplicon size distribution, autocovariance and inter-sample consistency as well as to identify chromosomes with aberrant read-density profiles due either to copy alterations or poor amplification. These metrics provide a standard way to compare the quality of single cell samples as well as yield information necessary to improve variant calling strategies. We demonstrate the usefulness of this tool in comparing the properties of scWGS protocols, identifying potential chromosomal copy number variation, determining chromosomal and subchromosomal regions of poor amplification, and selecting high-quality libraries from low-coverage data for deep sequencing. The software is available free and open-source at https://github.com/parklab/PaSDqc.
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Registered trials
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