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  • br R Development Core Team R A language

    2020-03-17


    R Development Core Team (2018). R: A language and environment for statis-tical computing (R Foundation for Statistical Computing).
    Robinson, M.D., and Oshlack, A. (2010). A scaling normalization method for differential N-octanoyl-L-Homoserine lactone analysis of RNA-seq data. Genome Biol. 11, R25.
    Robinson, M.D., McCarthy, D.J., and Smyth, G.K. (2010). edgeR: a Bio-conductor package for differential expression analysis of digital gene expres-sion data. Bioinformatics 26, 139–140.
    Roy, R., Louis, G., Loughlin, K.R., Wiederschain, D., Kilroy, S.M., Lamb, C.C., Zurakowski, D., and Moses, M.A. (2008). Tumor-specific urinary matrix metal-loproteinase fingerprinting: identification of high molecular weight urinary ma-trix metalloproteinase species. Clin. Cancer Res. 14, 6610–6617.
    Va¨remo, L., Nielsen, J., and Nookaew, I. (2013). Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Res. 41, 4378–4391.
    Vathipadiekal, V., Wang, V., Wei, W., Waldron, L., Drapkin, R., Gillette, M., Skates, S., and Birrer, M. (2015). Creation of a Human Secretome: A Novel Composite Library of Human Secreted Proteins: Validation Using Ovarian Cancer Gene Expression N-octanoyl-L-Homoserine lactone Data and a Virtual Secretome Array. Clin. Cancer Res. 21, 4960–4969.
    Vlassov, A.V., Magdaleno, S., Setterquist, R., and Conrad, R. (2012). Exo-somes: current knowledge of their composition, biological functions, and diag-nostic and therapeutic potentials. Biochim. Biophys. Acta 1820, 940–948.
    World Health Organization (2017). Guide to Cancer. Early Diagnosis (World Health Organization).
    cancer identified from the secretomes of 23 cancer cell lines and the human protein atlas. Mol. Cell. Proteomics 9, 1100–1117.
    STAR+METHODS
    KEY RESOURCES TABLE
    REAGENT or RESOURCE SOURCE
    IDENTIFIER
    Software and Algorithms
    The R Project for Statistical Computing
    https://www.R-project.org/
    Bioconductor
    https://www.bioconductor.org/
    EdgeR
    R Bioconductor
    TCGAbiolinks
    R Bioconductor
    The MathWorks, Inc.
    https://ch.mathworks.com/products/matlab.html
    MSigDB database
    http://software.broadinstitute.org/gsea/msigdb
    CONTACT FOR REAGENT AND RESOURCE SHARING
    Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Jens Nielsen ([email protected] chalmers.se).
    METHOD DETAILS
    Definition of the secretome and SP genes
    The list of proteins comprising the classically secreted secretome was obtained via UniProt (uniprot.org) (Bateman et al., 2017). Beginning with the entire human proteome (UP000005640), proteins were filtered to include those labeled as ‘‘UniProtKB/Swiss-Prot (reviewed),’’ with a subcellular location of ‘‘Secreted,’’ and PTM/Processing of ‘‘Signal peptide,’’ yielding 1,838 unique UniProt entries. The associated Entrez gene IDs and gene names were mapped to Ensembl IDs (GRCh38.p12), where those that did not map were excluded, and duplicated entries were removed, resulting in a secretome of 1,816 unique genes when analyzing TCGA data. For analyses also involving GTEx samples, genes absent from the that dataset were excluded, yielding a secretome comprised of 1,810 genes.
    SP (signal peptide) genes were defined and generated in the same way as the secretome, except without the requirement for a subcellular location of ‘‘Secreted.’’ This resulted in a set of 3,491 SP genes, of which 3,111 had associated differential expression data (TCGA primary tumor versus paired normal).
    The experimentally-derived secretome