Local Independence Testing for Point Processes.

Constraint-based causal structure learning for point processes require empirical tests of local independence. Existing tests require strong model assumptions, e.g., that the true data generating model is a Hawkes process with no latent confounders. Even when restricting attention to Hawkes processes, latent confounders are a major technical difficulty because a marginalized process will generally not be a Hawkes process itself. We introduce an expansion similar to Volterra expansions as a tool to represent marginalized intensities. Our main theoretical result is that such expansions can approximate the true marginalized intensity arbitrarily well. Based on this, we propose a test of local independence and investigate its properties in real and simulated data.

FANCD2 binding identifies conserved fragile sites at large transcribed genes in avian cells.

Common Chromosomal Fragile Sites (CFSs) are specific genomic regions prone to form breaks on metaphase chromosomes in response to replication stress. Moreover, CFSs are mutational hotspots in cancer genomes, showing that the mutational mechanisms that operate at CFSs are highly active in cancer cells. Orthologs of human CFSs are found in a number of other mammals, but the extent of CFS conservation beyond the mammalian lineage is unclear. Characterization of CFSs from distantly related organisms can provide new insight into the biology underlying CFSs. Here, we have mapped CFSs in an avian cell line. We find that, overall the most significant CFSs coincide with extremely large conserved genes, from which very long transcripts are produced. However, no significant correlation between any sequence characteristics and CFSs is found. Moreover, we identified putative early replicating fragile sites (ERFSs), which is a distinct class of fragile sites and we developed a fluctuation analysis revealing high mutation rates at the CFS gene PARK2, with deletions as the most prevalent mutation. Finally, we show that avian homologs of the human CFS genes despite their fragility have resisted the general intron size reduction observed in birds suggesting that CFSs have a conserved biological function.

Survival prognosis and variable selection: A case study for metastatic castrate resistant prostate cancer patients.

Survival prognosis is challenging, and accurate prediction of individual survival times is often very difficult. Better statistical methodology and more data can help improve the prognostic models, but it is important that methods and data usages are evaluated properly. The Prostate Cancer DREAM Challenge offered a framework for training and blinded validation of prognostic models using a large and rich dataset on patients diagnosed with metastatic castrate resistant prostate cancer. Using the Prostate Cancer DREAM Challenge data we investigated and compared an array of methods combining imputation techniques of missing values for prognostic variables with tree-based and lasso-based variable selection and model fitting methods. The benchmark metric used was integrated AUC (iAUC), and all methods were benchmarked using cross-validation on the training data as well as via the blinded validation. We found that survival forests without prior variable selection achieved the best overall performance (cv-iAUC = 0.70, validation-iACU = 0.78), while a generalized additive model was best among those methods that used explicit prior variable selection (cv-iAUC = 0.69, validation-iACU = 0.76). Our findings largely concurred with previous results in terms of the choice of important prognostic variables, though we did not find the level of prostate specific antigen to have prognostic value given the other variables included in the data.

Development and validation of a microRNA based diagnostic assay for primary tumor site classification of liver core biopsies.

Identification of the primary tumor site in patients with metastatic cancer is clinically important, but remains a challenge. Hence, efforts have been made towards establishing new diagnostic tools. Molecular profiling is a promising diagnostic approach, but tissue heterogeneity and inadequacy may negatively affect the accuracy and usability of molecular classifiers. We have developed and validated a microRNA-based classifier, which predicts the primary tumor site of liver biopsies, containing a limited number of tumor cells. Concurrently we explored the influence of surrounding normal tissue on classification. MicroRNA profiling was performed using quantitative Real-Time PCR on formalin-fixed paraffin-embedded samples. 278 primary tumors and liver metastases, representing nine primary tumor classes, as well as normal liver samples were used as a training set. A statistical model was applied to adjust for normal liver tissue contamination. Performance was estimated by cross-validation, followed by independent validation on 55 liver core biopsies with a tumor content as low as 10%. A microRNA classifier developed, using the statistical contamination model, showed an overall classification accuracy of 74.5% upon independent validation. Two-thirds of the samples were classified with high-confidence, with an accuracy of 92% on high-confidence predictions. A classifier trained without adjusting for liver tissue contamination, showed a classification accuracy of 38.2%. Our results indicate that surrounding normal tissue from the biopsy site may critically influence molecular classification. A significant improvement in classification accuracy was obtained when the influence of normal tissue was limited by application of a statistical contamination model.

Modeling tissue contamination to improve molecular identification of the primary tumor site of metastases.

MOTIVATION: Contamination of a cancer tissue by the surrounding benign (non-cancerous) tissue is a concern for molecular cancer diagnostics. This is because an observed molecular signature will be distorted by the surrounding benign tissue, possibly leading to an incorrect diagnosis. One example is molecular identification of the primary tumor site of metastases because biopsies of metastases typically contain a significant amount of benign tissue. RESULTS: A model of tissue contamination is presented. This contamination model works independently of the training of a molecular predictor, and it can be combined with any predictor model. The usability of the model is illustrated on primary tumor site identification of liver biopsies, specifically, on a human dataset consisting of microRNA expression measurements of primary tumor samples, benign liver samples and liver metastases. For a predictor trained on primary tumor and benign liver samples, the contamination model decreased the test error on biopsies from liver metastases from 77 to 45%. A further reduction to 34% was obtained by including biopsies in the training data. AVAILABILITY AND IMPLEMENTATION: http://www.math.ku.dk/∼richard/msgl/. CONTACT: vincent@math.ku.dk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Statistical models for local occurrences of RNA structures.

We develop in this article the necessary statistical theory for computing, for instance, E-values when searching long sequences for the occurrences of local RNA-structures. We show in particular how the theory can be used for estimating scoring parameters with the purpose of optimizing the discriminative performance of the algorithm. The results are implemented in the program StemSearch, which can search for stem loop structures that are formed by, for example, micro RNA precursors. We illustrate the use of the estimation method in practice by considering three miRNA target datasets from Human, Arabidopsis, and C. elegans and by optimizing three penalty parameters in StemSearch. We show that the optimization can improve the discriminative performance considerably when using a first order Markov model as null-distribution. Finally, we compare the output from StemSearch with that of RNALfold, and we discuss some notable differences that are primarily due to fundamental differences in the choice of parameters.