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CAncer bioMarker Prediction Pipeline (CAMPP)—A standardized framework for the analysis of quantitative biological data

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  • Thilde Terkelsen
  • Anders Krogh
  • Elena Papaleo

Abstract

With the improvement of -omics and next-generation sequencing (NGS) methodologies, along with the lowered cost of generating these types of data, the analysis of high-throughput biological data has become standard both for forming and testing biomedical hypotheses. Our knowledge of how to normalize datasets to remove latent undesirable variances has grown extensively, making for standardized data that are easily compared between studies. Here we present the CAncer bioMarker Prediction Pipeline (CAMPP), an open-source R-based wrapper (https://github.com/ELELAB/CAncer-bioMarker-Prediction-Pipeline -CAMPP) intended to aid bioinformatic software-users with data analyses. CAMPP is called from a terminal command line and is supported by a user-friendly manual. The pipeline may be run on a local computer and requires little or no knowledge of programming. To avoid issues relating to R-package updates, a renv .lock file is provided to ensure R-package stability. Data-management includes missing value imputation, data normalization, and distributional checks. CAMPP performs (I) k-means clustering, (II) differential expression/abundance analysis, (III) elastic-net regression, (IV) correlation and co-expression network analyses, (V) survival analysis, and (VI) protein-protein/miRNA-gene interaction networks. The pipeline returns tabular files and graphical representations of the results. We hope that CAMPP will assist in streamlining bioinformatic analysis of quantitative biological data, whilst ensuring an appropriate bio-statistical framework.

Suggested Citation

  • Thilde Terkelsen & Anders Krogh & Elena Papaleo, 2020. "CAncer bioMarker Prediction Pipeline (CAMPP)—A standardized framework for the analysis of quantitative biological data," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-20, March.
    • Handle: RePEc:plo:pcbi00:1007665
    DOI: 10.1371/journal.pcbi.1007665
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    1. Hai-Hui Huang & Xiao-Ying Liu & Yong Liang, 2016. "Feature Selection and Cancer Classification via Sparse Logistic Regression with the Hybrid L1/2 +2 Regularization," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-15, May.
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    3. Jeffrey T Leek & John D Storey, 2007. "Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis," PLOS Genetics, Public Library of Science, vol. 3(9), pages 1-12, September.
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