PT  - JOURNAL ARTICLE
AU  - David L Gibbs
TI  - Robust classification of Immune Subtypes in Cancer
AID  - 10.1101/2020.01.17.910950
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.01.17.910950
4099  - http://biorxiv.org/content/early/2020/01/18/2020.01.17.910950.short
4100  - http://biorxiv.org/content/early/2020/01/18/2020.01.17.910950.full
AB  - As part of the ‘immune landscape of cancer’, six immune subtypes were defined which describe a categorization of tumor-immune states. A number of phenotypic variables were found to associate with immune subtypes, such as nonsilent mutation rates, regulation of immunomodulator genes, and cytokine network structures. An ensemble classifier based on XGBoost is introduced with the goal of classifying tumor samples into one of six immune subtypes. Robust performance was accomplished through feature engineering; quartile-levels, binary gene-pair features, and gene-set-pair features were computed for each sample independently. The classifier is robust to software pipeline and normalization scheme, making it applicable to any expression data format from raw count data to TPMs since the classification is essentially based on simple binary gene-gene level comparisons within a given sample. The classifier is available as an R package or part of the CRI iAtlas portal.Code / Tool availability Source Code https://github.com/Gibbsdavidl/ImmuneSubtypeClassifierWeb App Tool https://www.cri-iatlas.org/