CAncer bioMarker Prediction Pipeline (CAMPP) - A standardised and user-friendly framework for the analysis of quantitative biological data.

Abstract

MotivationRecent improvements in -omics and next-generation sequencing (NGS) technologies, and the lowered costs associated with generating these types of data, have made the analysis of high-throughput datasets standard, both for forming and testing biomedical hypotheses. Alongside new wet-lab methodologies, our knowledge of how to normalise bio-data has grown extensively. By removing latent undesirable variances, we obtain standardised datasets, which can be more easily compared between studies. These advancements mean that non-experts in bioinformatics are now faced with the challenge of performing computational data analysis, pre-processing and visualisation. One example could be the analysis of biological data to pinpoint disease-related biomarkers for experimental validation. In this case, bio-researchers will desire an easy and standardised way of analysing high-throughput datasets.\n\nResultsHere we present the CAncer bioMarker Prediction Pipeline (CAMPP), an open-source R-based wrapper intended to aid non-experts in bioinformatics 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. CAMPP performs missing value imputation and normalisation followed by (I) k-means clustering, (II) differential expression/abundance analysis, (III) elastic-net regression, (IV) correlation and co-expression network analyses, (V) survival analysis and (IV) protein-protein/miRNA-gene interaction networks. The pipeline returns tabular files and graphical representations of the results. We hope that CAMPP will assist biomedical researchers in the analysis of quantitative biological data, whilst ensuring an appropriate biostatistical framework.\n\nAvailability and ImplementationCAMPP is available at https://github.com/ELELAB/CAMPP