inst/markdown/methods/ExpSig.markdown
In CRI-iAtlas/iatlas-app: What the Package Does (One Line, Title Case)
Title: Tumor Immune Expression Signatures
Description: We undertook an extensive literature search and assembled a collection of 160 immune expression signatures utilizing diverse resources which were considered to be reliable and comprehensive, based on the expert opinions of immune-oncologists in the group. Of these signatures, 83 were derived in the context of studies of the immune response in cancer and the remaining 77 are of general validity for immunity. The 83 signatures that are known to be associated with immune activity in tumor tissue consisted of 68 gene sets collected from earlier studies (Wolf et al., 2014), 9 co-expression signatures derived from computational analysis of all TCGA gene expression data sets (immune metagene attractors), (Cheng et al., 2013a, b), 3 signatures representing the functional orientation of the immune contexture (or Immunologic Constant of Rejection, ICR) (Bedognetti et al., 2016; Galon et al., 2013; Hendrickx et al., 2017), and 3 signatures from a recent study characterizing the immune microenvironment of clear cell renal cell carcinoma (Senbabaoglu et al., 2016). The 77 more general signatures comprised scores of 45 signatures representing individual cell types from two sources (20 from (Gentles et al., 2015) and 25 from (Bindea et al., 2013)) and 32 scores encompassing the dominant modes of scores derived from the ImmuneSigDB (Godec et al., 2016)(Collection C7 of MSigDB, Broad Institute)(Subramanian et al., 2005). The modes were determined as the first 32 principal components of 1888 Immune C7 human gene sets, and were used as the full set was overwhelmingly large and complex. Gene sets were scored using single-sample gene set enrichment (ssGSEA) analysis (Barbie et al., 2009), as implemented in the GSVA R package(Hanzelmann et al., 2013).
Contributors: David L. Gibbs, Denise Wolf, Vesteinn Thorsson, Benjamin Vincent, Ilya Shmulevich
CRI-iAtlas/iatlas-app documentation built on Feb. 7, 2025, 9:02 p.m.
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Title: Tumor Immune Expression Signatures
Description: We undertook an extensive literature search and assembled a collection of 160 immune expression signatures utilizing diverse resources which were considered to be reliable and comprehensive, based on the expert opinions of immune-oncologists in the group. Of these signatures, 83 were derived in the context of studies of the immune response in cancer and the remaining 77 are of general validity for immunity. The 83 signatures that are known to be associated with immune activity in tumor tissue consisted of 68 gene sets collected from earlier studies (Wolf et al., 2014), 9 co-expression signatures derived from computational analysis of all TCGA gene expression data sets (immune metagene attractors), (Cheng et al., 2013a, b), 3 signatures representing the functional orientation of the immune contexture (or Immunologic Constant of Rejection, ICR) (Bedognetti et al., 2016; Galon et al., 2013; Hendrickx et al., 2017), and 3 signatures from a recent study characterizing the immune microenvironment of clear cell renal cell carcinoma (Senbabaoglu et al., 2016). The 77 more general signatures comprised scores of 45 signatures representing individual cell types from two sources (20 from (Gentles et al., 2015) and 25 from (Bindea et al., 2013)) and 32 scores encompassing the dominant modes of scores derived from the ImmuneSigDB (Godec et al., 2016)(Collection C7 of MSigDB, Broad Institute)(Subramanian et al., 2005). The modes were determined as the first 32 principal components of 1888 Immune C7 human gene sets, and were used as the full set was overwhelmingly large and complex. Gene sets were scored using single-sample gene set enrichment (ssGSEA) analysis (Barbie et al., 2009), as implemented in the GSVA R package(Hanzelmann et al., 2013).
Contributors: David L. Gibbs, Denise Wolf, Vesteinn Thorsson, Benjamin Vincent, Ilya Shmulevich
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