GenoGAM: genome-wide generalized additive models for ChIP-Seq analysis.

MOTIVATION: Chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) is a widely used approach to study protein-DNA interactions. Often, the quantities of interest are the differential occupancies relative to controls, between genetic backgrounds, treatments, or combinations thereof. Current methods for differential occupancy of ChIP-Seq data rely however on binning or sliding window techniques, for which the choice of the window and bin sizes are subjective. RESULTS: Here, we present GenoGAM (Genome-wide Generalized Additive Model), which brings the well-established and flexible generalized additive models framework to genomic applications using a data parallelism strategy. We model ChIP-Seq read count frequencies as products of smooth functions along chromosomes. Smoothing parameters are objectively estimated from the data by cross-validation, eliminating ad hoc binning and windowing needed by current approaches. GenoGAM provides base-level and region-level significance testing for full factorial designs. Application to a ChIP-Seq dataset in yeast showed increased sensitivity over existing differential occupancy methods while controlling for type I error rate. By analyzing a set of DNA methylation data and illustrating an extension to a peak caller, we further demonstrate the potential of GenoGAM as a generic statistical modeling tool for genome-wide assays. AVAILABILITY AND IMPLEMENTATION: Software is available from Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/GenoGAM.html . CONTACT: gagneur@in.tum.de. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

Fecal Calprotectin in Cystic Fibrosis and Its Relation to Disease Parameters: A Longitudinal Analysis for 12 Years.

OBJECTIVES: Fecal calprotectin (FC) is a marker of inflammation in the intestinal tract. We assessed FC levels longitudinally in patients with cystic fibrosis (CF) and evaluated the relation between FC results and relevant markers of disease. METHODS: Calprotectin was measured in fecal samples starting in 2003 and values were stored in the center's patient database. In this retrospective analysis, we searched for associations of FC concentrations with disease severity and progression. Linear mixed effects models were used to model the logarithm of FC levels. RESULTS: A total of 171 patients (0-61 years) had 2434 FC measurements between 2003 and 2015, with a total observation period of 1686 patient-years. Median (interquartile range) FC concentrations were 60.9 (75.9) µg/g and 61% of the samples showed elevated FC concentrations (>50 µg/g). Despite some statistically significant effects, there was no clinically relevant association among FC and sex, age, forced expiratory volume in 1 second z score, or body mass index z score. Pancreatic insufficiency (ie, fecal elastase <100 µg/g stool) was associated with considerably higher FC values compared to normal pancreatic function (median FC 68 vs 29 µg/g, P < 0.0001). F508del homozygous subjects showed a trend to higher FC values than heterozygous patients (median 71 vs 62 µg/g, P = 0.173). In addition, a significant association with increasing serum C-reactive protein concentrations (P < 0.0001) was observed. CONCLUSIONS: FC was elevated in two-thirds of stool specimens. Increased FC was more common in patients with pancreatic insufficiency. Whether increased FC reflects intestinal inflammation in patients with CF remains to be determined.

Constructing an ROC Curve to Assess a Treatment-Predictive Continuous Biomarker.

This paper presents the idea of an ROC curve, which quantifies the discriminatory potential of a continuous biomarker for treatment selection when the outcome is continuous. The analysis assumes data from a randomized parallel group design. We use non-parametric density estimators to construct an ROC curve based on the probabilities that a (non-)responder, defined by better (worse) response to treatment as opposed to control, in the treatment group has a biomarker value above a value c. Our non-parametric approach comes close to the true AUC in a simulation study based on a normal distribution. Application to a real data set shows that despite a significant interaction term in a proportional hazards model, a biomarker may not be helpful for treatment decisions. Our proof-of-principle study opens the door to further developments and generalizations.

Efficient Maximum Likelihood Estimation for Pedigree Data with the Sum-Product Algorithm.

OBJECTIVE: We analyze data sets consisting of pedigrees with age at onset of colorectal cancer (CRC) as phenotype. The occurrence of familial clusters of CRC suggests the existence of a latent, inheritable risk factor. We aimed to compute the probability of a family possessing this risk factor as well as the hazard rate increase for these risk factor carriers. Due to the inheritability of this risk factor, the estimation necessitates a costly marginalization of the likelihood. METHODS: We propose an improved EM algorithm by applying factor graphs and the sum-product algorithm in the E-step. This reduces the computational complexity from exponential to linear in the number of family members. RESULTS: Our algorithm is as precise as a direct likelihood maximization in a simulation study and a real family study on CRC risk. For 250 simulated families of size 19 and 21, the runtime of our algorithm is faster by a factor of 4 and 29, respectively. On the largest family (23 members) in the real data, our algorithm is 6 times faster. CONCLUSION: We introduce a flexible and runtime-efficient tool for statistical inference in biomedical event data with latent variables that opens the door for advanced analyses of pedigree data.

Comparing classification methods for diffuse reflectance spectra to improve tissue specific laser surgery.

BACKGROUND: In the field of oral and maxillofacial surgery, newly developed laser scalpels have multiple advantages over traditional metal scalpels. However, they lack haptic feedback. This is dangerous near e.g. nerve tissue, which has to be preserved during surgery. One solution to this problem is to train an algorithm that analyzes the reflected light spectra during surgery and can classify these spectra into different tissue types, in order to ultimately send a warning or temporarily switch off the laser when critical tissue is about to be ablated. Various machine learning algorithms are available for this task, but a detailed analysis is needed to assess the most appropriate algorithm. METHODS: In this study, a small data set is used to simulate many larger data sets according to a multivariate Gaussian distribution. Various machine learning algorithms are then trained and evaluated on these data sets. The algorithms' performance is subsequently evaluated and compared by averaged confusion matrices and ultimately by boxplots of misclassification rates. The results are validated on the smaller, experimental data set. RESULTS: Most classifiers have a median misclassification rate below 0.25 in the simulated data. The most notable performance was observed for the Penalized Discriminant Analysis, with a misclassifiaction rate of 0.00 in the simulated data, and an average misclassification rate of 0.02 in a 10-fold cross validation on the original data. CONCLUSION: The results suggest a Penalized Discriminant Analysis is the most promising approach, most probably because it considers the functional, correlated nature of the reflectance spectra.The results of this study improve the accuracy of real-time tissue discrimination and are an essential step towards improving the safety of oral laser surgery.