A ratiometric-based measure of gene co-expression View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-12

AUTHORS

Anna CT Abelin, Georgi K Marinov, Brian A Williams, Kenneth McCue, Barbara J Wold

ABSTRACT

BACKGROUND: Gene co-expression analysis has previously been based on measures that include correlation coefficients and mutual information, as well as newcomers such as MIC. These measures depend primarily on the degree of association between the RNA levels of two genes and to a lesser extent on their variability. They focus on the similarity of expression value trajectories that change in like manner across samples. However there are relationships of biological interest for which these classical measures are expected to be insensitive. These include genes whose expression levels are ratiometrically stable and genes whose variance is tightly constrained. Large-scale studies of relatively homogeneous samples, including single cell RNA-seq, are experimental settings in which such relationships might be especially pertinent. RESULTS: We develop and implement a ratiometric approach for detecting gene associations (abbreviated RA). It is based on the coefficient of variation of the measured expression ratio of each pair of genes. We apply it to a collection of lymphoblastoid RNA-seq data from the 1000 Genomes Project Consortium, a typical sample set with high overall homogeneity. RA is a selective method, reporting in this case ~1/4 of all possible gene pairs, yet these relationships include a distilled picture of biological relationships previously found by other methods. In addition, RA reveals expression relationships that are not detected by traditional correlation and mutual information methods. We also analyze data from individual lymphoblastoid cells and show that desirable properties of the RA method extend to single-cell RNA-seq. CONCLUSION: We show that our ratiometric method identifies biologically significant relationships that are often missed or low-ranked by conventional association-based methods when applied to a relatively homogenous dataset. The results open new questions about the regulatory mechanisms that produce strong RA relationships. RA is scalable and potentially well suited for the analysis of thousands of bulk-RNA or single-cell transcriptomes. More... »

PAGES

331

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-15-331

DOI

http://dx.doi.org/10.1186/1471-2105-15-331

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1022123469

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25411051


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/2008/0604", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Genetics", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "B-Lymphocytes", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Cell Line, Transformed", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gene Expression Profiling", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genetic Association Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Human Genome Project", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sequence Analysis, RNA", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Single-Cell Analysis", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "California Institute of Technology", 
          "id": "https://www.grid.ac/institutes/grid.20861.3d", 
          "name": [
            "Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Blvd, 91125, Pasadena, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Abelin", 
        "givenName": "Anna CT", 
        "id": "sg:person.01372733062.92", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01372733062.92"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "California Institute of Technology", 
          "id": "https://www.grid.ac/institutes/grid.20861.3d", 
          "name": [
            "Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Blvd, 91125, Pasadena, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Marinov", 
        "givenName": "Georgi K", 
        "id": "sg:person.01073746603.46", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01073746603.46"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "California Institute of Technology", 
          "id": "https://www.grid.ac/institutes/grid.20861.3d", 
          "name": [
            "Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Blvd, 91125, Pasadena, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Williams", 
        "givenName": "Brian A", 
        "id": "sg:person.0741140331.06", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0741140331.06"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "California Institute of Technology", 
          "id": "https://www.grid.ac/institutes/grid.20861.3d", 
          "name": [
            "Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Blvd, 91125, Pasadena, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "McCue", 
        "givenName": "Kenneth", 
        "id": "sg:person.07460726307.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07460726307.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "California Institute of Technology", 
          "id": "https://www.grid.ac/institutes/grid.20861.3d", 
          "name": [
            "Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Blvd, 91125, Pasadena, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wold", 
        "givenName": "Barbara J", 
        "id": "sg:person.01026162263.73", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01026162263.73"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1186/1471-2105-13-328", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000406998", 
          "https://doi.org/10.1186/1471-2105-13-328"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature11632", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000661742", 
          "https://doi.org/10.1038/nature11632"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1128/cdli.9.6.1235-1239.2002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006042864"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/sj.onc.1202671", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008580170", 
          "https://doi.org/10.1038/sj.onc.1202671"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/sj.onc.1202671", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008580170", 
          "https://doi.org/10.1038/sj.onc.1202671"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.celrep.2011.12.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009103722"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-8-111", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011921254", 
          "https://doi.org/10.1186/1471-2105-8-111"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jcb.20725", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012184342"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jcb.20725", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012184342"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkn721", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013742279"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth.2251", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016190409", 
          "https://doi.org/10.1038/nmeth.2251"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0968-0004(00)01604-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016226772"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1205438", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017056822"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkh063", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017271040"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.161034.113", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018538260"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/hmg/ddi233", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019078079"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/hmg/ddi233", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019078079"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gku244", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020062282"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.95.25.14863", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020882317"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/hmg/ddg136", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025254284"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1309933111", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025347782"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1033", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026723563", 
          "https://doi.org/10.1038/ng1033"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1033", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026723563", 
          "https://doi.org/10.1038/ng1033"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1033", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026723563", 
          "https://doi.org/10.1038/ng1033"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1042/bj20081501", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029061781"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1042/bj20081501", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029061781"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0046539", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035217373"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nprot.2008.211", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039987283", 
          "https://doi.org/10.1038/nprot.2008.211"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1752-0509-6-s2-s2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044103681", 
          "https://doi.org/10.1186/1752-0509-6-s2-s2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkn923", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047610281"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1074/jbc.m201083200", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047868622"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1467-842x.1971.tb01245.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048900503"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/gb-2009-10-3-r25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049583368", 
          "https://doi.org/10.1186/gb-2009-10-3-r25"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/emboj.2012.293", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052553150"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1984.10477053", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058302898"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1086/169390", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058500680"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1086/171766", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058503056"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/bmj.d556", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062809288"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/bmj.d556", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062809288"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.18637/jss.v016.i04", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068672248"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014-12", 
    "datePublishedReg": "2014-12-01", 
    "description": "BACKGROUND: Gene co-expression analysis has previously been based on measures that include correlation coefficients and mutual information, as well as newcomers such as MIC. These measures depend primarily on the degree of association between the RNA levels of two genes and to a lesser extent on their variability. They focus on the similarity of expression value trajectories that change in like manner across samples. However there are relationships of biological interest for which these classical measures are expected to be insensitive. These include genes whose expression levels are ratiometrically stable and genes whose variance is tightly constrained. Large-scale studies of relatively homogeneous samples, including single cell RNA-seq, are experimental settings in which such relationships might be especially pertinent.\nRESULTS: We develop and implement a ratiometric approach for detecting gene associations (abbreviated RA). It is based on the coefficient of variation of the measured expression ratio of each pair of genes. We apply it to a collection of lymphoblastoid RNA-seq data from the 1000 Genomes Project Consortium, a typical sample set with high overall homogeneity. RA is a selective method, reporting in this case ~1/4 of all possible gene pairs, yet these relationships include a distilled picture of biological relationships previously found by other methods. In addition, RA reveals expression relationships that are not detected by traditional correlation and mutual information methods. We also analyze data from individual lymphoblastoid cells and show that desirable properties of the RA method extend to single-cell RNA-seq.\nCONCLUSION: We show that our ratiometric method identifies biologically significant relationships that are often missed or low-ranked by conventional association-based methods when applied to a relatively homogenous dataset. The results open new questions about the regulatory mechanisms that produce strong RA relationships. RA is scalable and potentially well suited for the analysis of thousands of bulk-RNA or single-cell transcriptomes.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/1471-2105-15-331", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2699370", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1023786", 
        "issn": [
          "1471-2105"
        ], 
        "name": "BMC Bioinformatics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "15"
      }
    ], 
    "name": "A ratiometric-based measure of gene co-expression", 
    "pagination": "331", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "6966d8fd9886d9cff3c105621efc46376447aaa0ba7b20f426234a7ad14bb0b1"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "25411051"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "100965194"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1471-2105-15-331"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1022123469"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1471-2105-15-331", 
      "https://app.dimensions.ai/details/publication/pub.1022123469"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T09:59", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000347_0000000347/records_89814_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2F1471-2105-15-331"
  }
]
 

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.10.1186/1471-2105-15-331'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-15-331'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-15-331'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-15-331'


 

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

238 TRIPLES      21 PREDICATES      70 URIs      29 LITERALS      17 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1471-2105-15-331 schema:about N0b30617898484260b7fce6bb37d95576
2 N1629786a185f40ffa53d4ed7b9b4fbef
3 N197554ad3a5045efa0ae3e89acbff8d1
4 N4ea5662fda354bc9a3037a4c7372d265
5 N741ec603f3b649618ebd878c0afcb656
6 Nb17b4603935a43b19de1bb7f83ff93fd
7 Nb6f91102a841486d89ff0b954276b287
8 Nf5fa57b74da849b3a098adec77d575db
9 anzsrc-for:06
10 anzsrc-for:0604
11 schema:author N96b02ca34b984882b75dbb82f3163523
12 schema:citation sg:pub.10.1038/nature11632
13 sg:pub.10.1038/ng1033
14 sg:pub.10.1038/nmeth.2251
15 sg:pub.10.1038/nprot.2008.211
16 sg:pub.10.1038/sj.onc.1202671
17 sg:pub.10.1186/1471-2105-13-328
18 sg:pub.10.1186/1471-2105-8-111
19 sg:pub.10.1186/1752-0509-6-s2-s2
20 sg:pub.10.1186/gb-2009-10-3-r25
21 https://doi.org/10.1002/jcb.20725
22 https://doi.org/10.1016/j.celrep.2011.12.001
23 https://doi.org/10.1016/s0968-0004(00)01604-2
24 https://doi.org/10.1038/emboj.2012.293
25 https://doi.org/10.1042/bj20081501
26 https://doi.org/10.1073/pnas.1309933111
27 https://doi.org/10.1073/pnas.95.25.14863
28 https://doi.org/10.1074/jbc.m201083200
29 https://doi.org/10.1080/01621459.1984.10477053
30 https://doi.org/10.1086/169390
31 https://doi.org/10.1086/171766
32 https://doi.org/10.1093/hmg/ddg136
33 https://doi.org/10.1093/hmg/ddi233
34 https://doi.org/10.1093/nar/gkh063
35 https://doi.org/10.1093/nar/gkn721
36 https://doi.org/10.1093/nar/gkn923
37 https://doi.org/10.1093/nar/gku244
38 https://doi.org/10.1101/gr.161034.113
39 https://doi.org/10.1111/j.1467-842x.1971.tb01245.x
40 https://doi.org/10.1126/science.1205438
41 https://doi.org/10.1128/cdli.9.6.1235-1239.2002
42 https://doi.org/10.1136/bmj.d556
43 https://doi.org/10.1371/journal.pone.0046539
44 https://doi.org/10.18637/jss.v016.i04
45 schema:datePublished 2014-12
46 schema:datePublishedReg 2014-12-01
47 schema:description BACKGROUND: Gene co-expression analysis has previously been based on measures that include correlation coefficients and mutual information, as well as newcomers such as MIC. These measures depend primarily on the degree of association between the RNA levels of two genes and to a lesser extent on their variability. They focus on the similarity of expression value trajectories that change in like manner across samples. However there are relationships of biological interest for which these classical measures are expected to be insensitive. These include genes whose expression levels are ratiometrically stable and genes whose variance is tightly constrained. Large-scale studies of relatively homogeneous samples, including single cell RNA-seq, are experimental settings in which such relationships might be especially pertinent. RESULTS: We develop and implement a ratiometric approach for detecting gene associations (abbreviated RA). It is based on the coefficient of variation of the measured expression ratio of each pair of genes. We apply it to a collection of lymphoblastoid RNA-seq data from the 1000 Genomes Project Consortium, a typical sample set with high overall homogeneity. RA is a selective method, reporting in this case ~1/4 of all possible gene pairs, yet these relationships include a distilled picture of biological relationships previously found by other methods. In addition, RA reveals expression relationships that are not detected by traditional correlation and mutual information methods. We also analyze data from individual lymphoblastoid cells and show that desirable properties of the RA method extend to single-cell RNA-seq. CONCLUSION: We show that our ratiometric method identifies biologically significant relationships that are often missed or low-ranked by conventional association-based methods when applied to a relatively homogenous dataset. The results open new questions about the regulatory mechanisms that produce strong RA relationships. RA is scalable and potentially well suited for the analysis of thousands of bulk-RNA or single-cell transcriptomes.
48 schema:genre research_article
49 schema:inLanguage en
50 schema:isAccessibleForFree true
51 schema:isPartOf N9ebe33a8fdb54856a7663d93ef1cbcc8
52 Nca7341e10746452aa568fffc30ebaf03
53 sg:journal.1023786
54 schema:name A ratiometric-based measure of gene co-expression
55 schema:pagination 331
56 schema:productId N13599bfc7e1941b38788a3bc2cde9a26
57 N1f7572fce3c840fa8764bd5b47098d1b
58 N2c11f34ce4e54c588d87242d2d10350c
59 Na0c084208d6d40ccbc9aa5cb2830a4b3
60 Nc48fc39561f04528ab969481af5fa969
61 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022123469
62 https://doi.org/10.1186/1471-2105-15-331
63 schema:sdDatePublished 2019-04-11T09:59
64 schema:sdLicense https://scigraph.springernature.com/explorer/license/
65 schema:sdPublisher N015958da425b43ea93e095701c692a7a
66 schema:url https://link.springer.com/10.1186%2F1471-2105-15-331
67 sgo:license sg:explorer/license/
68 sgo:sdDataset articles
69 rdf:type schema:ScholarlyArticle
70 N015958da425b43ea93e095701c692a7a schema:name Springer Nature - SN SciGraph project
71 rdf:type schema:Organization
72 N0b30617898484260b7fce6bb37d95576 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
73 schema:name Cell Line, Transformed
74 rdf:type schema:DefinedTerm
75 N13599bfc7e1941b38788a3bc2cde9a26 schema:name doi
76 schema:value 10.1186/1471-2105-15-331
77 rdf:type schema:PropertyValue
78 N1629786a185f40ffa53d4ed7b9b4fbef schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
79 schema:name Human Genome Project
80 rdf:type schema:DefinedTerm
81 N197554ad3a5045efa0ae3e89acbff8d1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
82 schema:name Gene Expression Profiling
83 rdf:type schema:DefinedTerm
84 N1f7572fce3c840fa8764bd5b47098d1b schema:name readcube_id
85 schema:value 6966d8fd9886d9cff3c105621efc46376447aaa0ba7b20f426234a7ad14bb0b1
86 rdf:type schema:PropertyValue
87 N2014fa2889724b3585e01666d1723bc5 rdf:first sg:person.01026162263.73
88 rdf:rest rdf:nil
89 N244c344c67ec4eaca0ac6af69ec8bea8 rdf:first sg:person.01073746603.46
90 rdf:rest N3c8b00aaffbe48c28c9c99944952e48b
91 N2c11f34ce4e54c588d87242d2d10350c schema:name pubmed_id
92 schema:value 25411051
93 rdf:type schema:PropertyValue
94 N3c8b00aaffbe48c28c9c99944952e48b rdf:first sg:person.0741140331.06
95 rdf:rest N72ed7347ea814f7190873cd2840519c0
96 N4ea5662fda354bc9a3037a4c7372d265 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
97 schema:name Sequence Analysis, RNA
98 rdf:type schema:DefinedTerm
99 N72ed7347ea814f7190873cd2840519c0 rdf:first sg:person.07460726307.17
100 rdf:rest N2014fa2889724b3585e01666d1723bc5
101 N741ec603f3b649618ebd878c0afcb656 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
102 schema:name Humans
103 rdf:type schema:DefinedTerm
104 N96b02ca34b984882b75dbb82f3163523 rdf:first sg:person.01372733062.92
105 rdf:rest N244c344c67ec4eaca0ac6af69ec8bea8
106 N9ebe33a8fdb54856a7663d93ef1cbcc8 schema:volumeNumber 15
107 rdf:type schema:PublicationVolume
108 Na0c084208d6d40ccbc9aa5cb2830a4b3 schema:name nlm_unique_id
109 schema:value 100965194
110 rdf:type schema:PropertyValue
111 Nb17b4603935a43b19de1bb7f83ff93fd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
112 schema:name Genetic Association Studies
113 rdf:type schema:DefinedTerm
114 Nb6f91102a841486d89ff0b954276b287 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
115 schema:name B-Lymphocytes
116 rdf:type schema:DefinedTerm
117 Nc48fc39561f04528ab969481af5fa969 schema:name dimensions_id
118 schema:value pub.1022123469
119 rdf:type schema:PropertyValue
120 Nca7341e10746452aa568fffc30ebaf03 schema:issueNumber 1
121 rdf:type schema:PublicationIssue
122 Nf5fa57b74da849b3a098adec77d575db schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
123 schema:name Single-Cell Analysis
124 rdf:type schema:DefinedTerm
125 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
126 schema:name Biological Sciences
127 rdf:type schema:DefinedTerm
128 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
129 schema:name Genetics
130 rdf:type schema:DefinedTerm
131 sg:grant.2699370 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-15-331
132 rdf:type schema:MonetaryGrant
133 sg:journal.1023786 schema:issn 1471-2105
134 schema:name BMC Bioinformatics
135 rdf:type schema:Periodical
136 sg:person.01026162263.73 schema:affiliation https://www.grid.ac/institutes/grid.20861.3d
137 schema:familyName Wold
138 schema:givenName Barbara J
139 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01026162263.73
140 rdf:type schema:Person
141 sg:person.01073746603.46 schema:affiliation https://www.grid.ac/institutes/grid.20861.3d
142 schema:familyName Marinov
143 schema:givenName Georgi K
144 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01073746603.46
145 rdf:type schema:Person
146 sg:person.01372733062.92 schema:affiliation https://www.grid.ac/institutes/grid.20861.3d
147 schema:familyName Abelin
148 schema:givenName Anna CT
149 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01372733062.92
150 rdf:type schema:Person
151 sg:person.0741140331.06 schema:affiliation https://www.grid.ac/institutes/grid.20861.3d
152 schema:familyName Williams
153 schema:givenName Brian A
154 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0741140331.06
155 rdf:type schema:Person
156 sg:person.07460726307.17 schema:affiliation https://www.grid.ac/institutes/grid.20861.3d
157 schema:familyName McCue
158 schema:givenName Kenneth
159 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07460726307.17
160 rdf:type schema:Person
161 sg:pub.10.1038/nature11632 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000661742
162 https://doi.org/10.1038/nature11632
163 rdf:type schema:CreativeWork
164 sg:pub.10.1038/ng1033 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026723563
165 https://doi.org/10.1038/ng1033
166 rdf:type schema:CreativeWork
167 sg:pub.10.1038/nmeth.2251 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016190409
168 https://doi.org/10.1038/nmeth.2251
169 rdf:type schema:CreativeWork
170 sg:pub.10.1038/nprot.2008.211 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039987283
171 https://doi.org/10.1038/nprot.2008.211
172 rdf:type schema:CreativeWork
173 sg:pub.10.1038/sj.onc.1202671 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008580170
174 https://doi.org/10.1038/sj.onc.1202671
175 rdf:type schema:CreativeWork
176 sg:pub.10.1186/1471-2105-13-328 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000406998
177 https://doi.org/10.1186/1471-2105-13-328
178 rdf:type schema:CreativeWork
179 sg:pub.10.1186/1471-2105-8-111 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011921254
180 https://doi.org/10.1186/1471-2105-8-111
181 rdf:type schema:CreativeWork
182 sg:pub.10.1186/1752-0509-6-s2-s2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044103681
183 https://doi.org/10.1186/1752-0509-6-s2-s2
184 rdf:type schema:CreativeWork
185 sg:pub.10.1186/gb-2009-10-3-r25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049583368
186 https://doi.org/10.1186/gb-2009-10-3-r25
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1002/jcb.20725 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012184342
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1016/j.celrep.2011.12.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009103722
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1016/s0968-0004(00)01604-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016226772
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1038/emboj.2012.293 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052553150
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1042/bj20081501 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029061781
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1073/pnas.1309933111 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025347782
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1073/pnas.95.25.14863 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020882317
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1074/jbc.m201083200 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047868622
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1080/01621459.1984.10477053 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058302898
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1086/169390 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058500680
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1086/171766 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058503056
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1093/hmg/ddg136 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025254284
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1093/hmg/ddi233 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019078079
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1093/nar/gkh063 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017271040
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1093/nar/gkn721 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013742279
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1093/nar/gkn923 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047610281
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1093/nar/gku244 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020062282
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1101/gr.161034.113 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018538260
223 rdf:type schema:CreativeWork
224 https://doi.org/10.1111/j.1467-842x.1971.tb01245.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1048900503
225 rdf:type schema:CreativeWork
226 https://doi.org/10.1126/science.1205438 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017056822
227 rdf:type schema:CreativeWork
228 https://doi.org/10.1128/cdli.9.6.1235-1239.2002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006042864
229 rdf:type schema:CreativeWork
230 https://doi.org/10.1136/bmj.d556 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062809288
231 rdf:type schema:CreativeWork
232 https://doi.org/10.1371/journal.pone.0046539 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035217373
233 rdf:type schema:CreativeWork
234 https://doi.org/10.18637/jss.v016.i04 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068672248
235 rdf:type schema:CreativeWork
236 https://www.grid.ac/institutes/grid.20861.3d schema:alternateName California Institute of Technology
237 schema:name Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Blvd, 91125, Pasadena, CA, USA
238 rdf:type schema:Organization
 




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


...