SureTypeSC - A Random Forest and Gaussian Mixture predictor of high confidence genotypes in single cell data

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Standard

SureTypeSC - A Random Forest and Gaussian Mixture predictor of high confidence genotypes in single cell data. / Vogel, Ivan; Blanshard, Robert C; Hoffmann, Eva R.

I: Bioinformatics, Bind 35, Nr. 23, 2019, s. 5055-5062.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Vogel, I, Blanshard, RC & Hoffmann, ER 2019, 'SureTypeSC - A Random Forest and Gaussian Mixture predictor of high confidence genotypes in single cell data', Bioinformatics, bind 35, nr. 23, s. 5055-5062. https://doi.org/10.1093/bioinformatics/btz412

APA

Vogel, I., Blanshard, R. C., & Hoffmann, E. R. (2019). SureTypeSC - A Random Forest and Gaussian Mixture predictor of high confidence genotypes in single cell data. Bioinformatics, 35(23), 5055-5062. https://doi.org/10.1093/bioinformatics/btz412

Vancouver

Vogel I, Blanshard RC, Hoffmann ER. SureTypeSC - A Random Forest and Gaussian Mixture predictor of high confidence genotypes in single cell data. Bioinformatics. 2019;35(23):5055-5062. https://doi.org/10.1093/bioinformatics/btz412

Author

Vogel, Ivan ; Blanshard, Robert C ; Hoffmann, Eva R. / SureTypeSC - A Random Forest and Gaussian Mixture predictor of high confidence genotypes in single cell data. I: Bioinformatics. 2019 ; Bind 35, Nr. 23. s. 5055-5062.

Bibtex

@article{b8aaec1de0134c61b4caf666abc45ce3,

title = "SureTypeSC - A Random Forest and Gaussian Mixture predictor of high confidence genotypes in single cell data",

abstract = "MOTIVATION: Accurate genotyping of DNA from a single cell is required for applications such as de novo mutation detection, linkage analysis and lineage tracing. However, achieving high precision genotyping in the single cell environment is challenging due to the errors caused by whole genome amplification. Two factors make genotyping from single cells using single nucleotide polymorphism (SNP) arrays challenging. The lack of a comprehensive single cell dataset with a reference genotype and the absence of genotyping tools specifically designed to detect noise from the whole genome amplification step. Algorithms designed for bulk DNA genotyping cause significant data loss when used for single cell applications.RESULTS: In this study, we have created a resource of 28.7 million SNPs, typed at high confidence from whole genome amplified DNA from single cells using the Illumina SNP bead array technology. The resource is generated from 104 single cells from two cell lines that are available from the Coriell repository. We used mother-father-proband (trio) information from multiple technical replicates of bulk DNA to establish a high quality reference genotype for the two cell lines on the SNP array. This enabled us to develop SureTypeSC - a two-stage machine learning algorithm that filters a substantial part of the noise, thereby retaining the majority of the high quality SNPs. SureTypeSC also provides a simple statistical output to show the confidence of a particular single cell genotype using Bayesian statistics.AVAILABILITY: The implementation of SureTypeSC in Python and sample data are available in the GitHub repository: https://github.com/puko818/SureTypeSC.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",

author = "Ivan Vogel and Blanshard, {Robert C} and Hoffmann, {Eva R}",

year = "2019",

doi = "10.1093/bioinformatics/btz412",

language = "English",

volume = "35",

pages = "5055--5062",

journal = "Computer Applications in the Biosciences",

issn = "1471-2105",

publisher = "Oxford University Press",

number = "23",

}

RIS

TY - JOUR

T1 - SureTypeSC - A Random Forest and Gaussian Mixture predictor of high confidence genotypes in single cell data

AU - Vogel, Ivan

AU - Blanshard, Robert C

AU - Hoffmann, Eva R

PY - 2019

Y1 - 2019

N2 - MOTIVATION: Accurate genotyping of DNA from a single cell is required for applications such as de novo mutation detection, linkage analysis and lineage tracing. However, achieving high precision genotyping in the single cell environment is challenging due to the errors caused by whole genome amplification. Two factors make genotyping from single cells using single nucleotide polymorphism (SNP) arrays challenging. The lack of a comprehensive single cell dataset with a reference genotype and the absence of genotyping tools specifically designed to detect noise from the whole genome amplification step. Algorithms designed for bulk DNA genotyping cause significant data loss when used for single cell applications.RESULTS: In this study, we have created a resource of 28.7 million SNPs, typed at high confidence from whole genome amplified DNA from single cells using the Illumina SNP bead array technology. The resource is generated from 104 single cells from two cell lines that are available from the Coriell repository. We used mother-father-proband (trio) information from multiple technical replicates of bulk DNA to establish a high quality reference genotype for the two cell lines on the SNP array. This enabled us to develop SureTypeSC - a two-stage machine learning algorithm that filters a substantial part of the noise, thereby retaining the majority of the high quality SNPs. SureTypeSC also provides a simple statistical output to show the confidence of a particular single cell genotype using Bayesian statistics.AVAILABILITY: The implementation of SureTypeSC in Python and sample data are available in the GitHub repository: https://github.com/puko818/SureTypeSC.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AB - MOTIVATION: Accurate genotyping of DNA from a single cell is required for applications such as de novo mutation detection, linkage analysis and lineage tracing. However, achieving high precision genotyping in the single cell environment is challenging due to the errors caused by whole genome amplification. Two factors make genotyping from single cells using single nucleotide polymorphism (SNP) arrays challenging. The lack of a comprehensive single cell dataset with a reference genotype and the absence of genotyping tools specifically designed to detect noise from the whole genome amplification step. Algorithms designed for bulk DNA genotyping cause significant data loss when used for single cell applications.RESULTS: In this study, we have created a resource of 28.7 million SNPs, typed at high confidence from whole genome amplified DNA from single cells using the Illumina SNP bead array technology. The resource is generated from 104 single cells from two cell lines that are available from the Coriell repository. We used mother-father-proband (trio) information from multiple technical replicates of bulk DNA to establish a high quality reference genotype for the two cell lines on the SNP array. This enabled us to develop SureTypeSC - a two-stage machine learning algorithm that filters a substantial part of the noise, thereby retaining the majority of the high quality SNPs. SureTypeSC also provides a simple statistical output to show the confidence of a particular single cell genotype using Bayesian statistics.AVAILABILITY: The implementation of SureTypeSC in Python and sample data are available in the GitHub repository: https://github.com/puko818/SureTypeSC.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

U2 - 10.1093/bioinformatics/btz412

DO - 10.1093/bioinformatics/btz412

M3 - Journal article

C2 - 31116387

VL - 35

SP - 5055

EP - 5062

JO - Computer Applications in the Biosciences

JF - Computer Applications in the Biosciences

SN - 1471-2105

IS - 23

ER -

ID: 228200534