Expression-based species deconvolution and realignment removes misalignment error in multispecies single-cell data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-05-02

AUTHORS

Jaeyong Choi, Woochan Lee, Jung-Ki Yoon, Sun Mi Choi, Chang-Hoon Lee, Hyeong-Gon Moon, Sukki Cho, Jin-Haeng Chung, Han-Kwang Yang, Jong-Il Kim

ABSTRACT

BackgroundAlthough single-cell RNA sequencing of xenograft samples has been widely used, no comprehensive bioinformatics pipeline is available for human and mouse mixed single-cell analyses. Considering the numerous homologous genes across the human and mouse genomes, misalignment errors should be evaluated, and a new algorithm is required. We assessed the extents and effects of misalignment errors and exonic multi-mapping events when using human and mouse combined reference data and developed a new bioinformatics pipeline with expression-based species deconvolution to minimize errors. We also evaluated false-positive signals presumed to originate from ambient RNA of the other species and address the importance to computationally remove them.ResultError when using combined reference account for an average of 0.78% of total reads, but such reads were concentrated to few genes that were greatly affected. Human and mouse mixed single-cell data, analyzed using our pipeline, clustered well with unmixed data and showed higher k-nearest-neighbor batch effect test and Local Inverse Simpson’s Index scores than those derived from Cell Ranger (10 × Genomics). We also applied our pipeline to multispecies multisample single-cell library containing breast cancer xenograft tissue and successfully identified all samples using genomic array and expression. Moreover, diverse cell types in the tumor microenvironment were well captured.ConclusionWe present our bioinformatics pipeline for mixed human and mouse single-cell data, which can also be applied to pooled libraries to obtain cost-effective single-cell data. We also address misalignment, multi-mapping error, and ambient RNA as a major consideration points when analyzing multispecies single-cell data. More... »

PAGES

157

References to SciGraph publications

  • 2018-10-22. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations in NATURE COMMUNICATIONS
  • 2020-04-06. Organoid cultures from normal and cancer-prone human breast tissues preserve complex epithelial lineages in NATURE COMMUNICATIONS
  • 2018-12-19. Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics in GENOME BIOLOGY
  • 2019-06-17. MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices in NATURE METHODS
  • 2018-10-04. XenofilteR: computational deconvolution of mouse and human reads in tumor xenograft sequence data in BMC BIOINFORMATICS
  • 2017-01-16. Massively parallel digital transcriptional profiling of single cells in NATURE COMMUNICATIONS
  • 2018-12-20. A test metric for assessing single-cell RNA-seq batch correction in NATURE METHODS
  • 2019-02-04. The Korea Biobank Array: Design and Identification of Coding Variants Associated with Blood Biochemical Traits in SCIENTIFIC REPORTS
  • 2018-11-26. Tumour heterogeneity and metastasis at single-cell resolution in NATURE CELL BIOLOGY
  • 2021-01-29. XenoCell: classification of cellular barcodes in single cell experiments from xenograft samples in BMC MEDICAL GENOMICS
  • 2020-03-25. Construction of a human cell landscape at single-cell level in NATURE
  • 2009-03-29. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche in NATURE
  • 2020-05-27. Single-cell RNA sequencing reveals the tumor microenvironment and facilitates strategic choices to circumvent treatment failure in a chemorefractory bladder cancer patient in GENOME MEDICINE
  • 2018-11-14. Single-cell reconstruction of the early maternal–fetal interface in humans in NATURE
  • 2021-01-18. Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes in NATURE BIOTECHNOLOGY
  • 2020-03-06. Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing in NATURE CELL BIOLOGY
  • 2019-11-18. Fast, sensitive and accurate integration of single-cell data with Harmony in NATURE METHODS
  • 2019-03-22. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data in GENOME BIOLOGY
  • 2017-12-11. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation in NATURE BIOTECHNOLOGY
  • 2018-08-01. A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte in NATURE
  • 2020-05-04. Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes in NATURE METHODS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s12859-022-04676-0

    DOI

    http://dx.doi.org/10.1186/s12859-022-04676-0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1147539685

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/35501695


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Biological Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0604", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Genetics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Algorithms", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Animals", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Computational Biology", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Genome", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Genomics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Mice", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "RNA", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Biomedical Sciences, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea", 
                "Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Choi", 
            "givenName": "Jaeyong", 
            "id": "sg:person.014035340527.70", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014035340527.70"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Biomedical Sciences, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea", 
                "Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lee", 
            "givenName": "Woochan", 
            "id": "sg:person.010377100323.17", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010377100323.17"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.412484.f", 
              "name": [
                "Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Yoon", 
            "givenName": "Jung-Ki", 
            "id": "sg:person.010576731031.26", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010576731031.26"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea", 
                "Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Choi", 
            "givenName": "Sun Mi", 
            "id": "sg:person.01067024644.46", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01067024644.46"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.412484.f", 
              "name": [
                "Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lee", 
            "givenName": "Chang-Hoon", 
            "id": "sg:person.0743464132.81", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0743464132.81"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Moon", 
            "givenName": "Hyeong-Gon", 
            "id": "sg:person.0734631064.34", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0734631064.34"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.412480.b", 
              "name": [
                "Department of Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Cho", 
            "givenName": "Sukki", 
            "id": "sg:person.01023401101.34", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023401101.34"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.412480.b", 
              "name": [
                "Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Chung", 
            "givenName": "Jin-Haeng", 
            "id": "sg:person.013014114662.53", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013014114662.53"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Yang", 
            "givenName": "Han-Kwang", 
            "id": "sg:person.015270312155.27", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015270312155.27"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Biomedical Sciences, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea", 
                "Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kim", 
            "givenName": "Jong-Il", 
            "id": "sg:person.01216736563.00", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01216736563.00"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1186/s13073-020-00741-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1127948668", 
              "https://doi.org/10.1186/s13073-020-00741-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12859-018-2353-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107404312", 
              "https://doi.org/10.1186/s12859-018-2353-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41592-019-0433-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1117288825", 
              "https://doi.org/10.1038/s41592-019-0433-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13059-019-1662-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1112948105", 
              "https://doi.org/10.1186/s13059-019-1662-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41556-020-0477-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1125429591", 
              "https://doi.org/10.1038/s41556-020-0477-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41556-018-0236-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1110042719", 
              "https://doi.org/10.1038/s41556-018-0236-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41592-019-0619-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1122668169", 
              "https://doi.org/10.1038/s41592-019-0619-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41467-018-06318-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107670523", 
              "https://doi.org/10.1038/s41467-018-06318-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13059-018-1603-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1110764498", 
              "https://doi.org/10.1186/s13059-018-1603-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-018-37832-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1111914009", 
              "https://doi.org/10.1038/s41598-018-37832-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41586-018-0394-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105928903", 
              "https://doi.org/10.1038/s41586-018-0394-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41586-020-2157-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1125895308", 
              "https://doi.org/10.1038/s41586-020-2157-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41587-020-00795-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1134665169", 
              "https://doi.org/10.1038/s41587-020-00795-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41467-020-15548-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1126154523", 
              "https://doi.org/10.1038/s41467-020-15548-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature07935", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024082069", 
              "https://doi.org/10.1038/nature07935"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41592-018-0254-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1110510327", 
              "https://doi.org/10.1038/s41592-018-0254-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ncomms14049", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019155899", 
              "https://doi.org/10.1038/ncomms14049"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41586-018-0698-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1109832851", 
              "https://doi.org/10.1038/s41586-018-0698-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41592-020-0820-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1127346179", 
              "https://doi.org/10.1038/s41592-020-0820-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt.4042", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1099639136", 
              "https://doi.org/10.1038/nbt.4042"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12920-021-00872-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1134980012", 
              "https://doi.org/10.1186/s12920-021-00872-8"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2022-05-02", 
        "datePublishedReg": "2022-05-02", 
        "description": "BackgroundAlthough single-cell RNA sequencing of xenograft samples has been widely used, no comprehensive bioinformatics pipeline is available for human and mouse mixed single-cell analyses. Considering the numerous homologous genes across the human and mouse genomes, misalignment errors should be evaluated, and a new algorithm is required. We assessed the extents and effects of misalignment errors and exonic multi-mapping events when using human and mouse combined reference data and developed a new bioinformatics pipeline with expression-based species deconvolution to minimize errors. We also evaluated false-positive signals presumed to originate from ambient RNA of the other species and address the importance to computationally remove them.ResultError when using combined reference account for an average of 0.78% of total reads, but such reads were concentrated to few genes that were greatly affected. Human and mouse mixed single-cell data, analyzed using our pipeline, clustered well with unmixed data and showed higher k-nearest-neighbor batch effect test and Local Inverse Simpson\u2019s Index scores than those derived from Cell Ranger (10\u2009\u00d7\u2009Genomics). We also applied our pipeline to multispecies multisample single-cell library containing breast cancer xenograft tissue and successfully identified all samples using genomic array and expression. Moreover, diverse cell types in the tumor microenvironment were well captured.ConclusionWe present our bioinformatics pipeline for mixed human and mouse single-cell data, which can also be applied to pooled libraries to obtain cost-effective single-cell data. We also address misalignment, multi-mapping error, and ambient RNA as a major consideration points when analyzing multispecies single-cell data.", 
        "genre": "article", 
        "id": "sg:pub.10.1186/s12859-022-04676-0", 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1023786", 
            "issn": [
              "1471-2105"
            ], 
            "name": "BMC Bioinformatics", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "23"
          }
        ], 
        "keywords": [
          "single-cell data", 
          "bioinformatics pipeline", 
          "ambient RNA", 
          "breast cancer xenograft tissues", 
          "single-cell RNA sequencing", 
          "comprehensive bioinformatics pipeline", 
          "single-cell libraries", 
          "new bioinformatic pipeline", 
          "diverse cell types", 
          "single-cell analysis", 
          "Cell Ranger", 
          "homologous genes", 
          "mouse genome", 
          "RNA sequencing", 
          "total reads", 
          "genomic arrays", 
          "cell types", 
          "such reads", 
          "xenograft samples", 
          "genes", 
          "RNA", 
          "false-positive signals", 
          "reads", 
          "xenograft tissues", 
          "tumor microenvironment", 
          "genome", 
          "humans", 
          "species", 
          "sequencing", 
          "mixed human", 
          "library", 
          "expression", 
          "microenvironment", 
          "pipeline", 
          "tissue", 
          "unmixed data", 
          "mice", 
          "signals", 
          "data", 
          "events", 
          "importance", 
          "analysis", 
          "array", 
          "types", 
          "extent", 
          "samples", 
          "effect", 
          "rangers", 
          "deconvolution", 
          "reference data", 
          "average", 
          "ConclusionWe", 
          "point", 
          "test", 
          "consideration points", 
          "account", 
          "effect test", 
          "misalignment", 
          "error", 
          "new algorithm", 
          "index score", 
          "scores", 
          "algorithm", 
          "misalignment errors"
        ], 
        "name": "Expression-based species deconvolution and realignment removes misalignment error in multispecies single-cell data", 
        "pagination": "157", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1147539685"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/s12859-022-04676-0"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "35501695"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/s12859-022-04676-0", 
          "https://app.dimensions.ai/details/publication/pub.1147539685"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-09-02T16:07", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/article/article_953.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1186/s12859-022-04676-0"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s12859-022-04676-0'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12859-022-04676-0'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12859-022-04676-0'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12859-022-04676-0'


     

    This table displays all metadata directly associated to this object as RDF triples.

    316 TRIPLES      21 PREDICATES      118 URIs      89 LITERALS      15 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/s12859-022-04676-0 schema:about N15d5051e42d84aaf8559f4f9e1471a31
    2 N37068de2ec0e4b6fb7a2696f47fe6645
    3 N5c83cb4bd27642bb95301156264ccad8
    4 N82b68cc76ace494ca58dd0b18739e169
    5 Nb248fafc6c404eff998b46f85b45a142
    6 Nc3bd048c718e49f494a2a880e9620f51
    7 Ncc7cf00017cf40f0984100c35e9ae0b7
    8 Nfbec2da4408c4992a344302ddae1240d
    9 anzsrc-for:06
    10 anzsrc-for:0604
    11 schema:author N1a3b26ea7d12457caee3a6527cc5bda8
    12 schema:citation sg:pub.10.1038/nature07935
    13 sg:pub.10.1038/nbt.4042
    14 sg:pub.10.1038/ncomms14049
    15 sg:pub.10.1038/s41467-018-06318-7
    16 sg:pub.10.1038/s41467-020-15548-7
    17 sg:pub.10.1038/s41556-018-0236-7
    18 sg:pub.10.1038/s41556-020-0477-0
    19 sg:pub.10.1038/s41586-018-0394-6
    20 sg:pub.10.1038/s41586-018-0698-6
    21 sg:pub.10.1038/s41586-020-2157-4
    22 sg:pub.10.1038/s41587-020-00795-2
    23 sg:pub.10.1038/s41592-018-0254-1
    24 sg:pub.10.1038/s41592-019-0433-8
    25 sg:pub.10.1038/s41592-019-0619-0
    26 sg:pub.10.1038/s41592-020-0820-1
    27 sg:pub.10.1038/s41598-018-37832-9
    28 sg:pub.10.1186/s12859-018-2353-5
    29 sg:pub.10.1186/s12920-021-00872-8
    30 sg:pub.10.1186/s13059-018-1603-1
    31 sg:pub.10.1186/s13059-019-1662-y
    32 sg:pub.10.1186/s13073-020-00741-6
    33 schema:datePublished 2022-05-02
    34 schema:datePublishedReg 2022-05-02
    35 schema:description BackgroundAlthough single-cell RNA sequencing of xenograft samples has been widely used, no comprehensive bioinformatics pipeline is available for human and mouse mixed single-cell analyses. Considering the numerous homologous genes across the human and mouse genomes, misalignment errors should be evaluated, and a new algorithm is required. We assessed the extents and effects of misalignment errors and exonic multi-mapping events when using human and mouse combined reference data and developed a new bioinformatics pipeline with expression-based species deconvolution to minimize errors. We also evaluated false-positive signals presumed to originate from ambient RNA of the other species and address the importance to computationally remove them.ResultError when using combined reference account for an average of 0.78% of total reads, but such reads were concentrated to few genes that were greatly affected. Human and mouse mixed single-cell data, analyzed using our pipeline, clustered well with unmixed data and showed higher k-nearest-neighbor batch effect test and Local Inverse Simpson’s Index scores than those derived from Cell Ranger (10 × Genomics). We also applied our pipeline to multispecies multisample single-cell library containing breast cancer xenograft tissue and successfully identified all samples using genomic array and expression. Moreover, diverse cell types in the tumor microenvironment were well captured.ConclusionWe present our bioinformatics pipeline for mixed human and mouse single-cell data, which can also be applied to pooled libraries to obtain cost-effective single-cell data. We also address misalignment, multi-mapping error, and ambient RNA as a major consideration points when analyzing multispecies single-cell data.
    36 schema:genre article
    37 schema:isAccessibleForFree true
    38 schema:isPartOf N5216b106e4cd40b390fb11a25fe8ebc9
    39 Nc9528955fed2449499062f923e554b0d
    40 sg:journal.1023786
    41 schema:keywords Cell Ranger
    42 ConclusionWe
    43 RNA
    44 RNA sequencing
    45 account
    46 algorithm
    47 ambient RNA
    48 analysis
    49 array
    50 average
    51 bioinformatics pipeline
    52 breast cancer xenograft tissues
    53 cell types
    54 comprehensive bioinformatics pipeline
    55 consideration points
    56 data
    57 deconvolution
    58 diverse cell types
    59 effect
    60 effect test
    61 error
    62 events
    63 expression
    64 extent
    65 false-positive signals
    66 genes
    67 genome
    68 genomic arrays
    69 homologous genes
    70 humans
    71 importance
    72 index score
    73 library
    74 mice
    75 microenvironment
    76 misalignment
    77 misalignment errors
    78 mixed human
    79 mouse genome
    80 new algorithm
    81 new bioinformatic pipeline
    82 pipeline
    83 point
    84 rangers
    85 reads
    86 reference data
    87 samples
    88 scores
    89 sequencing
    90 signals
    91 single-cell RNA sequencing
    92 single-cell analysis
    93 single-cell data
    94 single-cell libraries
    95 species
    96 such reads
    97 test
    98 tissue
    99 total reads
    100 tumor microenvironment
    101 types
    102 unmixed data
    103 xenograft samples
    104 xenograft tissues
    105 schema:name Expression-based species deconvolution and realignment removes misalignment error in multispecies single-cell data
    106 schema:pagination 157
    107 schema:productId N190fbc2c673a4ca79a8526a0ef71b9ee
    108 N60fafdbeb80f4560940d6a90a93b9dc8
    109 N83f1c2becce74704a229482753672962
    110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1147539685
    111 https://doi.org/10.1186/s12859-022-04676-0
    112 schema:sdDatePublished 2022-09-02T16:07
    113 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    114 schema:sdPublisher N02667905e0ad45e59405e182d2865735
    115 schema:url https://doi.org/10.1186/s12859-022-04676-0
    116 sgo:license sg:explorer/license/
    117 sgo:sdDataset articles
    118 rdf:type schema:ScholarlyArticle
    119 N02667905e0ad45e59405e182d2865735 schema:name Springer Nature - SN SciGraph project
    120 rdf:type schema:Organization
    121 N15d5051e42d84aaf8559f4f9e1471a31 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    122 schema:name Genomics
    123 rdf:type schema:DefinedTerm
    124 N190fbc2c673a4ca79a8526a0ef71b9ee schema:name dimensions_id
    125 schema:value pub.1147539685
    126 rdf:type schema:PropertyValue
    127 N1a3b26ea7d12457caee3a6527cc5bda8 rdf:first sg:person.014035340527.70
    128 rdf:rest N3ab786eece5b40b3ac03c2d943ba3167
    129 N1e805db7265742068a6e199746e6a891 rdf:first sg:person.010576731031.26
    130 rdf:rest N55b61abe1bf842c195a449fb990e10a6
    131 N37068de2ec0e4b6fb7a2696f47fe6645 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    132 schema:name Computational Biology
    133 rdf:type schema:DefinedTerm
    134 N3ab786eece5b40b3ac03c2d943ba3167 rdf:first sg:person.010377100323.17
    135 rdf:rest N1e805db7265742068a6e199746e6a891
    136 N4faf717cfd1e485aa7eb20c9930e3847 rdf:first sg:person.01216736563.00
    137 rdf:rest rdf:nil
    138 N5216b106e4cd40b390fb11a25fe8ebc9 schema:issueNumber 1
    139 rdf:type schema:PublicationIssue
    140 N55b61abe1bf842c195a449fb990e10a6 rdf:first sg:person.01067024644.46
    141 rdf:rest N60a78d0b0b8c4fe19fedfc8efe78b8e7
    142 N5c83cb4bd27642bb95301156264ccad8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    143 schema:name RNA
    144 rdf:type schema:DefinedTerm
    145 N60a78d0b0b8c4fe19fedfc8efe78b8e7 rdf:first sg:person.0743464132.81
    146 rdf:rest Na854c3c6e5f548a49567162c3b199c49
    147 N60fafdbeb80f4560940d6a90a93b9dc8 schema:name pubmed_id
    148 schema:value 35501695
    149 rdf:type schema:PropertyValue
    150 N82b68cc76ace494ca58dd0b18739e169 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    151 schema:name Humans
    152 rdf:type schema:DefinedTerm
    153 N83f1c2becce74704a229482753672962 schema:name doi
    154 schema:value 10.1186/s12859-022-04676-0
    155 rdf:type schema:PropertyValue
    156 N978fc95fe4bc46a2a523f008cd05a00f rdf:first sg:person.01023401101.34
    157 rdf:rest Nb3105ea63f22476bb2df5b94aa29c5d1
    158 Na854c3c6e5f548a49567162c3b199c49 rdf:first sg:person.0734631064.34
    159 rdf:rest N978fc95fe4bc46a2a523f008cd05a00f
    160 Nb248fafc6c404eff998b46f85b45a142 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    161 schema:name Mice
    162 rdf:type schema:DefinedTerm
    163 Nb3105ea63f22476bb2df5b94aa29c5d1 rdf:first sg:person.013014114662.53
    164 rdf:rest Nd9dafbc219cf49b3a05f55e193d63155
    165 Nc3bd048c718e49f494a2a880e9620f51 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    166 schema:name Animals
    167 rdf:type schema:DefinedTerm
    168 Nc9528955fed2449499062f923e554b0d schema:volumeNumber 23
    169 rdf:type schema:PublicationVolume
    170 Ncc7cf00017cf40f0984100c35e9ae0b7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    171 schema:name Genome
    172 rdf:type schema:DefinedTerm
    173 Nd9dafbc219cf49b3a05f55e193d63155 rdf:first sg:person.015270312155.27
    174 rdf:rest N4faf717cfd1e485aa7eb20c9930e3847
    175 Nfbec2da4408c4992a344302ddae1240d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    176 schema:name Algorithms
    177 rdf:type schema:DefinedTerm
    178 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    179 schema:name Biological Sciences
    180 rdf:type schema:DefinedTerm
    181 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    182 schema:name Genetics
    183 rdf:type schema:DefinedTerm
    184 sg:journal.1023786 schema:issn 1471-2105
    185 schema:name BMC Bioinformatics
    186 rdf:type schema:Periodical
    187 sg:person.01023401101.34 schema:affiliation grid-institutes:grid.412480.b
    188 schema:familyName Cho
    189 schema:givenName Sukki
    190 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023401101.34
    191 rdf:type schema:Person
    192 sg:person.010377100323.17 schema:affiliation grid-institutes:grid.31501.36
    193 schema:familyName Lee
    194 schema:givenName Woochan
    195 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010377100323.17
    196 rdf:type schema:Person
    197 sg:person.010576731031.26 schema:affiliation grid-institutes:grid.412484.f
    198 schema:familyName Yoon
    199 schema:givenName Jung-Ki
    200 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010576731031.26
    201 rdf:type schema:Person
    202 sg:person.01067024644.46 schema:affiliation grid-institutes:grid.31501.36
    203 schema:familyName Choi
    204 schema:givenName Sun Mi
    205 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01067024644.46
    206 rdf:type schema:Person
    207 sg:person.01216736563.00 schema:affiliation grid-institutes:grid.31501.36
    208 schema:familyName Kim
    209 schema:givenName Jong-Il
    210 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01216736563.00
    211 rdf:type schema:Person
    212 sg:person.013014114662.53 schema:affiliation grid-institutes:grid.412480.b
    213 schema:familyName Chung
    214 schema:givenName Jin-Haeng
    215 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013014114662.53
    216 rdf:type schema:Person
    217 sg:person.014035340527.70 schema:affiliation grid-institutes:grid.31501.36
    218 schema:familyName Choi
    219 schema:givenName Jaeyong
    220 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014035340527.70
    221 rdf:type schema:Person
    222 sg:person.015270312155.27 schema:affiliation grid-institutes:grid.31501.36
    223 schema:familyName Yang
    224 schema:givenName Han-Kwang
    225 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015270312155.27
    226 rdf:type schema:Person
    227 sg:person.0734631064.34 schema:affiliation grid-institutes:grid.31501.36
    228 schema:familyName Moon
    229 schema:givenName Hyeong-Gon
    230 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0734631064.34
    231 rdf:type schema:Person
    232 sg:person.0743464132.81 schema:affiliation grid-institutes:grid.412484.f
    233 schema:familyName Lee
    234 schema:givenName Chang-Hoon
    235 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0743464132.81
    236 rdf:type schema:Person
    237 sg:pub.10.1038/nature07935 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024082069
    238 https://doi.org/10.1038/nature07935
    239 rdf:type schema:CreativeWork
    240 sg:pub.10.1038/nbt.4042 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099639136
    241 https://doi.org/10.1038/nbt.4042
    242 rdf:type schema:CreativeWork
    243 sg:pub.10.1038/ncomms14049 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019155899
    244 https://doi.org/10.1038/ncomms14049
    245 rdf:type schema:CreativeWork
    246 sg:pub.10.1038/s41467-018-06318-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107670523
    247 https://doi.org/10.1038/s41467-018-06318-7
    248 rdf:type schema:CreativeWork
    249 sg:pub.10.1038/s41467-020-15548-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1126154523
    250 https://doi.org/10.1038/s41467-020-15548-7
    251 rdf:type schema:CreativeWork
    252 sg:pub.10.1038/s41556-018-0236-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110042719
    253 https://doi.org/10.1038/s41556-018-0236-7
    254 rdf:type schema:CreativeWork
    255 sg:pub.10.1038/s41556-020-0477-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1125429591
    256 https://doi.org/10.1038/s41556-020-0477-0
    257 rdf:type schema:CreativeWork
    258 sg:pub.10.1038/s41586-018-0394-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105928903
    259 https://doi.org/10.1038/s41586-018-0394-6
    260 rdf:type schema:CreativeWork
    261 sg:pub.10.1038/s41586-018-0698-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109832851
    262 https://doi.org/10.1038/s41586-018-0698-6
    263 rdf:type schema:CreativeWork
    264 sg:pub.10.1038/s41586-020-2157-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1125895308
    265 https://doi.org/10.1038/s41586-020-2157-4
    266 rdf:type schema:CreativeWork
    267 sg:pub.10.1038/s41587-020-00795-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1134665169
    268 https://doi.org/10.1038/s41587-020-00795-2
    269 rdf:type schema:CreativeWork
    270 sg:pub.10.1038/s41592-018-0254-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110510327
    271 https://doi.org/10.1038/s41592-018-0254-1
    272 rdf:type schema:CreativeWork
    273 sg:pub.10.1038/s41592-019-0433-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1117288825
    274 https://doi.org/10.1038/s41592-019-0433-8
    275 rdf:type schema:CreativeWork
    276 sg:pub.10.1038/s41592-019-0619-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1122668169
    277 https://doi.org/10.1038/s41592-019-0619-0
    278 rdf:type schema:CreativeWork
    279 sg:pub.10.1038/s41592-020-0820-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1127346179
    280 https://doi.org/10.1038/s41592-020-0820-1
    281 rdf:type schema:CreativeWork
    282 sg:pub.10.1038/s41598-018-37832-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111914009
    283 https://doi.org/10.1038/s41598-018-37832-9
    284 rdf:type schema:CreativeWork
    285 sg:pub.10.1186/s12859-018-2353-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107404312
    286 https://doi.org/10.1186/s12859-018-2353-5
    287 rdf:type schema:CreativeWork
    288 sg:pub.10.1186/s12920-021-00872-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1134980012
    289 https://doi.org/10.1186/s12920-021-00872-8
    290 rdf:type schema:CreativeWork
    291 sg:pub.10.1186/s13059-018-1603-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110764498
    292 https://doi.org/10.1186/s13059-018-1603-1
    293 rdf:type schema:CreativeWork
    294 sg:pub.10.1186/s13059-019-1662-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1112948105
    295 https://doi.org/10.1186/s13059-019-1662-y
    296 rdf:type schema:CreativeWork
    297 sg:pub.10.1186/s13073-020-00741-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1127948668
    298 https://doi.org/10.1186/s13073-020-00741-6
    299 rdf:type schema:CreativeWork
    300 grid-institutes:grid.31501.36 schema:alternateName Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea
    301 Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
    302 Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
    303 schema:name Department of Biomedical Sciences, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea
    304 Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea
    305 Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
    306 Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
    307 Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
    308 rdf:type schema:Organization
    309 grid-institutes:grid.412480.b schema:alternateName Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    310 Department of Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    311 schema:name Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    312 Department of Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    313 rdf:type schema:Organization
    314 grid-institutes:grid.412484.f schema:alternateName Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
    315 schema:name Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
    316 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...