Ontology type: schema:ScholarlyArticle Open Access: True
2017-03-03
AUTHORSSophie Weiss, Zhenjiang Zech Xu, Shyamal Peddada, Amnon Amir, Kyle Bittinger, Antonio Gonzalez, Catherine Lozupone, Jesse R. Zaneveld, Yoshiki Vázquez-Baeza, Amanda Birmingham, Embriette R. Hyde, Rob Knight
ABSTRACTBackgroundData from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses.ResultsEffects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size.Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10×) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate.ConclusionsThese findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study. More... »
PAGES27
http://scigraph.springernature.com/pub.10.1186/s40168-017-0237-y
DOIhttp://dx.doi.org/10.1186/s40168-017-0237-y
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1084252802
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/28253908
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/2008/11",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Medical and Health Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0602",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Ecology",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0605",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Microbiology",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1108",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Medical Microbiology",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Bacteria",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Bacterial Load",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Base Sequence",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "DNA, Bacterial",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Ecosystem",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Gene Library",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Humans",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Microbial Consortia",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "RNA, Ribosomal, 16S",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Sequence Analysis, DNA",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Department of Chemical and Biological Engineering, University of Colorado at Boulder, 80309, Boulder, CO, USA",
"id": "http://www.grid.ac/institutes/grid.266190.a",
"name": [
"Department of Chemical and Biological Engineering, University of Colorado at Boulder, 80309, Boulder, CO, USA"
],
"type": "Organization"
},
"familyName": "Weiss",
"givenName": "Sophie",
"id": "sg:person.01166324530.84",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01166324530.84"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Departments of Pediatrics, University of California San Diego, 9500 Gilman Drive, MC 0763, 92093, La Jolla, CA, USA",
"id": "http://www.grid.ac/institutes/grid.266100.3",
"name": [
"Departments of Pediatrics, University of California San Diego, 9500 Gilman Drive, MC 0763, 92093, La Jolla, CA, USA"
],
"type": "Organization"
},
"familyName": "Xu",
"givenName": "Zhenjiang Zech",
"id": "sg:person.01161573701.69",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01161573701.69"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Biostatistics and Computational Biology Branch, NIEHS, NIH, Research Triangle Park Durham, NC, USA",
"id": "http://www.grid.ac/institutes/grid.280664.e",
"name": [
"Biostatistics and Computational Biology Branch, NIEHS, NIH, Research Triangle Park Durham, NC, USA"
],
"type": "Organization"
},
"familyName": "Peddada",
"givenName": "Shyamal",
"id": "sg:person.015365450637.65",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015365450637.65"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Departments of Pediatrics, University of California San Diego, 9500 Gilman Drive, MC 0763, 92093, La Jolla, CA, USA",
"id": "http://www.grid.ac/institutes/grid.266100.3",
"name": [
"Departments of Pediatrics, University of California San Diego, 9500 Gilman Drive, MC 0763, 92093, La Jolla, CA, USA"
],
"type": "Organization"
},
"familyName": "Amir",
"givenName": "Amnon",
"id": "sg:person.01163102774.24",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01163102774.24"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Microbiology, University of Pennsylvania, 18014, Philadelphia, PA, USA",
"id": "http://www.grid.ac/institutes/grid.25879.31",
"name": [
"Department of Microbiology, University of Pennsylvania, 18014, Philadelphia, PA, USA"
],
"type": "Organization"
},
"familyName": "Bittinger",
"givenName": "Kyle",
"id": "sg:person.01244225361.13",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01244225361.13"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Departments of Pediatrics, University of California San Diego, 9500 Gilman Drive, MC 0763, 92093, La Jolla, CA, USA",
"id": "http://www.grid.ac/institutes/grid.266100.3",
"name": [
"Departments of Pediatrics, University of California San Diego, 9500 Gilman Drive, MC 0763, 92093, La Jolla, CA, USA"
],
"type": "Organization"
},
"familyName": "Gonzalez",
"givenName": "Antonio",
"id": "sg:person.01301120245.46",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01301120245.46"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Medicine, University of Colorado, 80204, Denver, CO, USA",
"id": "http://www.grid.ac/institutes/grid.241116.1",
"name": [
"Department of Medicine, University of Colorado, 80204, Denver, CO, USA"
],
"type": "Organization"
},
"familyName": "Lozupone",
"givenName": "Catherine",
"id": "sg:person.0672337357.81",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0672337357.81"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Microbiology, Oregon State University, 226 Nash Hall, 97331, Corvallis, OR, USA",
"id": "http://www.grid.ac/institutes/grid.4391.f",
"name": [
"Department of Microbiology, Oregon State University, 226 Nash Hall, 97331, Corvallis, OR, USA"
],
"type": "Organization"
},
"familyName": "Zaneveld",
"givenName": "Jesse R.",
"id": "sg:person.0753705541.65",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0753705541.65"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Computer Science & Engineering, University of California San Diego, 92093, La Jolla, CA, USA",
"id": "http://www.grid.ac/institutes/grid.266100.3",
"name": [
"Department of Computer Science & Engineering, University of California San Diego, 92093, La Jolla, CA, USA"
],
"type": "Organization"
},
"familyName": "V\u00e1zquez-Baeza",
"givenName": "Yoshiki",
"id": "sg:person.01113460501.49",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01113460501.49"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Center for Computational Biology and Bioinformatics, Dept. of Medicine, University of California San Diego, 92093, La Jolla, CA, USA",
"id": "http://www.grid.ac/institutes/grid.266100.3",
"name": [
"Center for Computational Biology and Bioinformatics, Dept. of Medicine, University of California San Diego, 92093, La Jolla, CA, USA"
],
"type": "Organization"
},
"familyName": "Birmingham",
"givenName": "Amanda",
"id": "sg:person.01231155371.50",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01231155371.50"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Departments of Pediatrics, University of California San Diego, 9500 Gilman Drive, MC 0763, 92093, La Jolla, CA, USA",
"id": "http://www.grid.ac/institutes/grid.266100.3",
"name": [
"Departments of Pediatrics, University of California San Diego, 9500 Gilman Drive, MC 0763, 92093, La Jolla, CA, USA"
],
"type": "Organization"
},
"familyName": "Hyde",
"givenName": "Embriette R.",
"id": "sg:person.0756335676.60",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0756335676.60"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Center for Microbiome Innovation, University of California San Diego, 92093, La Jolla, CA, USA",
"id": "http://www.grid.ac/institutes/grid.266100.3",
"name": [
"Departments of Pediatrics, University of California San Diego, 9500 Gilman Drive, MC 0763, 92093, La Jolla, CA, USA",
"Department of Computer Science & Engineering, University of California San Diego, 92093, La Jolla, CA, USA",
"Center for Microbiome Innovation, University of California San Diego, 92093, La Jolla, CA, USA"
],
"type": "Organization"
},
"familyName": "Knight",
"givenName": "Rob",
"id": "sg:person.016311745377.96",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016311745377.96"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1186/s12915-014-0087-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027737035",
"https://doi.org/10.1186/s12915-014-0087-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nmeth.2897",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1030185276",
"https://doi.org/10.1038/nmeth.2897"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/gb-2011-12-5-r50",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050643751",
"https://doi.org/10.1186/gb-2011-12-5-r50"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nprot.2013.099",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033430059",
"https://doi.org/10.1038/nprot.2013.099"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/gb-2014-15-2-r29",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1045312009",
"https://doi.org/10.1186/gb-2014-15-2-r29"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/gb-2010-11-10-r106",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1031289083",
"https://doi.org/10.1186/gb-2010-11-10-r106"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00056509",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007450841",
"https://doi.org/10.1007/bf00056509"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11004-011-9338-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048467842",
"https://doi.org/10.1007/s11004-011-9338-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nmeth.2658",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002139060",
"https://doi.org/10.1038/nmeth.2658"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/gb-2004-5-10-r80",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018457673",
"https://doi.org/10.1186/gb-2004-5-10-r80"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ismej.2010.133",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1042916034",
"https://doi.org/10.1038/ismej.2010.133"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nmeth.2898",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007964999",
"https://doi.org/10.1038/nmeth.2898"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/gb-2013-14-9-r95",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036803445",
"https://doi.org/10.1186/gb-2013-14-9-r95"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10260-005-0121-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007650443",
"https://doi.org/10.1007/s10260-005-0121-y"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10852-005-9022-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011366574",
"https://doi.org/10.1007/s10852-005-9022-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature03073",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024447086",
"https://doi.org/10.1038/nature03073"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ismej.2014.76",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1006057368",
"https://doi.org/10.1038/ismej.2014.76"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature08821",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050498034",
"https://doi.org/10.1038/nature08821"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/gb-2014-15-6-r76",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024439294",
"https://doi.org/10.1186/gb-2014-15-6-r76"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature12820",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013830289",
"https://doi.org/10.1038/nature12820"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-11-94",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053091615",
"https://doi.org/10.1186/1471-2105-11-94"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature07540",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1030170002",
"https://doi.org/10.1038/nature07540"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-14-91",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016675314",
"https://doi.org/10.1186/1471-2105-14-91"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/s13059-014-0550-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015222646",
"https://doi.org/10.1186/s13059-014-0550-8"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/2047-217x-2-16",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022546943",
"https://doi.org/10.1186/2047-217x-2-16"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/a:1023818214614",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024177181",
"https://doi.org/10.1023/a:1023818214614"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ismej.2012.8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038992953",
"https://doi.org/10.1038/ismej.2012.8"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/2049-2618-2-15",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046874717",
"https://doi.org/10.1186/2049-2618-2-15"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nmeth.f.303",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009032055",
"https://doi.org/10.1038/nmeth.f.303"
],
"type": "CreativeWork"
}
],
"datePublished": "2017-03-03",
"datePublishedReg": "2017-03-03",
"description": "BackgroundData from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses.ResultsEffects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size.Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10\u00d7) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10\u00d7) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate.ConclusionsThese findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study.",
"genre": "article",
"id": "sg:pub.10.1186/s40168-017-0237-y",
"isAccessibleForFree": true,
"isFundedItemOf": [
{
"id": "sg:grant.4242003",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.2691272",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.2717719",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.2529347",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.2725281",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1048878",
"issn": [
"2049-2618"
],
"name": "Microbiome",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "5"
}
],
"keywords": [
"relative abundance",
"taxa relative abundance",
"false discovery rate",
"library size",
"discovery rate",
"ribosomal RNA amplicons",
"differential abundance analysis",
"differential abundance testing",
"high false discovery rate",
"overall microbial composition",
"most normalization methods",
"microbial composition",
"biological origin",
"ecosystems",
"abundance",
"abundance technique",
"RNA amplicons",
"taxa",
"abundance analysis",
"DESeq2",
"microbiome",
"amplicons",
"ResultsEffects",
"normalization measures",
"origin",
"more samples",
"analysis of composition",
"composition",
"loss of sensitivity",
"large differences",
"successful clustering",
"analysis",
"size",
"compositional data",
"absence",
"normalization method",
"more groups",
"present challenges",
"simplex",
"available data",
"previous work",
"portion",
"data",
"loss",
"presence",
"inference",
"effect",
"study",
"clustering",
"rate",
"statistical methods",
"sensitivity",
"addition",
"only method",
"specimens",
"ConclusionsThese findings",
"comparison",
"samples",
"range of magnitudes",
"group",
"control",
"strategies",
"findings",
"dataset",
"differences",
"challenges",
"standard methods",
"data characteristics",
"range",
"special challenges",
"characteristics",
"raw data",
"results",
"small datasets",
"elimination",
"statistical interpretation",
"better control",
"metrics",
"normalization",
"method",
"compositional effects",
"magnitude",
"work",
"technique",
"measures",
"interpretation",
"specimen",
"simulation study",
"normalization technique",
"space",
"artifacts",
"testing",
"sum",
"Euclidean space",
"performance",
"cluster sample",
"zeros",
"BackgroundData"
],
"name": "Normalization and microbial differential abundance strategies depend upon data characteristics",
"pagination": "27",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1084252802"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1186/s40168-017-0237-y"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"28253908"
]
}
],
"sameAs": [
"https://doi.org/10.1186/s40168-017-0237-y",
"https://app.dimensions.ai/details/publication/pub.1084252802"
],
"sdDataset": "articles",
"sdDatePublished": "2022-08-04T17:05",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/article/article_745.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1186/s40168-017-0237-y"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
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/s40168-017-0237-y'
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/s40168-017-0237-y'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s40168-017-0237-y'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s40168-017-0237-y'
This table displays all metadata directly associated to this object as RDF triples.
434 TRIPLES
21 PREDICATES
165 URIs
125 LITERALS
17 BLANK NODES