Natacha Turck

BACKGROUND: Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. RESULTS: With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. CONCLUSIONS: pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

A new 6-plex isobaric mass tagging technology is presented, and proof of principle studies are carried out using standard protein mixtures and human cerebrospinal fluid (CSF) samples. The Tandem Mass Tags (TMT) comprise a set of structurally identical tags which label peptides on free amino-terminus and epsilon-amino functions of lysine residues. During MS/MS fragmentation, quantification information is obtained through the losses of the reporter ions. After evaluation of the relative quantification with the 6-plex version of the TMT on a model protein mixture at various concentrations, the quantification of proteins in CSF samples was performed using shotgun methods. Human postmortem (PM) CSF was taken as a model of massive brain injury and comparison was carried out with antemortem (AM) CSF. After immunoaffinity depletion, triplicates of AM and PM CSF pooled samples were reduced, alkylated, digested by trypsin, and labeled, respectively, with the six isobaric variants of the TMT (with reporter ions from m/z = 126.1 to 131.1 Th). The samples were pooled and fractionated by SCX chromatography. After RP-LC separation, peptides were identified and quantified by MS/MS analysis with MALDI TOF/TOF and ESI-Q-TOF. The concentration of 78 identified proteins was shown to be clearly increased in PM CSF samples compared to AM. Some of these proteins, like GFAP, protein S100B, and PARK7, have been previously described as brain damage biomarkers, supporting the PM CSF as a valid model of brain insult. ELISA for these proteins confirmed their elevated concentration in PM CSF. This work demonstrates the validity and robustness of the tandem mass tag (TMT) approach for quantitative MS-based proteomics.