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Differential gene expression in iPSC-derived macrophages after IFNg stimulation and Salmonella infection

Alasoo, Kaur


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  <identifier identifierType="DOI">10.5281/zenodo.839011</identifier>
  <creators>
    <creator>
      <creatorName>Alasoo, Kaur</creatorName>
      <givenName>Kaur</givenName>
      <familyName>Alasoo</familyName>
      <affiliation>Wellcome Trust Sanger Institute</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Differential gene expression in iPSC-derived macrophages after IFNg stimulation and Salmonella infection</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <subjects>
    <subject>RNA-seq</subject>
    <subject>differential expression</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2017-08-04</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/839011</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.839010</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/macrophage-gene-expression-genetics</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;We used likelihood ratio test implemented in DESeq2 v1.10.0 (test = “LRT”) to test if a model that allowed different mean expression in each condition explained the data better than a null model assuming the same mean expression across conditions. See the manuscript for more details: http://www.biorxiv.org/content/early/2017/05/18/102392 .&lt;/p&gt;

&lt;p&gt;We used the following commands in DESeq2:&lt;br&gt;
#Run DESeq2&lt;br&gt;
dds = DESeq2::DESeqDataSetFromMatrix(combined_expression_data_filtered$counts, design, ~condition_name) &lt;br&gt;
dds = DESeq2::DESeq(dds, test = "LRT", reduced = ~ 1)&lt;/p&gt;

&lt;p&gt;#Extract differentially expressed genes in each condition&lt;br&gt;
ifng_genes = results(dds, contrast=c("condition_name","IFNg","naive")) &lt;br&gt;
sl1344_genes = results(dds, contrast=c("condition_name","SL1344","naive")) &lt;br&gt;
ifng_sl1344_genes = results(dds, contrast=c("condition_name","IFNg_SL1344","naive"))&lt;/p&gt;</description>
  </descriptions>
</resource>
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