MOTIVATION: Mixed molecular data combines continuous and categorical features of the same samples, such as OMICS profiles with genotypes, diagnoses, or patient sex. Like all high-dimensional molecular data, it is prone to incorrect values that can stem from various sources for example the technical limitations of the measurement devices, errors in the sample preparation, or contamination. Most anomaly detection algorithms identify complete samples as outliers or anomalies. However, in most cases, not all measurements of those samples are erroneous but only a few one-dimensional features within the samples are incorrect. These one-dimensional data errors are continuous measurements that are either located outside or inside the normal ranges of their features but in both cases show atypical values given all other continuous and categorical features in the sample. Additionally, categorical anomalies can occur for example when the genotype or diagnosis was submitted wrongly. RESULTS: We introduce ADMIRE (Anomaly Detection using MIxed gRaphical modEls), a novel approach for the detection and correction of anomalies in mixed high-dimensional data. Hereby, we focus on the detection of single (one-dimensional) data errors in the categorical and continuous features of a sample. For that the joint distribution of continuous and categorical features is learned by mixed graphical models, anomalies are detected by the difference between measured and model-based estimations and are corrected using imputation. We evaluated ADMIRE in simulation and by screening for anomalies in one of our own metabolic datasets. In simulation experiments, ADMIRE outperformed the state-of-the-art methods of Local Outlier Factor, stray, and Isolation Forest. AVAILABILITY AND IMPLEMENTATION: All data and code is available at https://github.com/spang-lab/adadmire. ADMIRE is implemented in a Python package called adadmire which can be found at https://pypi.org/project/adadmire.

MOTIVATION: Estimating the effects of interventions on patient outcome is one of the key aspects of personalized medicine. Their inference is often challenged by the fact that the training data comprises only the outcome for the administered treatment, and not for alternative treatments (the so-called counterfactual outcomes). Several methods were suggested for this scenario based on observational data, i.e. data where the intervention was not applied randomly, for both continuous and binary outcome variables. However, patient outcome is often recorded in terms of time-to-event data, comprising right-censored event times if an event does not occur within the observation period. Albeit their enormous importance, time-to-event data are rarely used for treatment optimization. We suggest an approach named BITES (Balanced Individual Treatment Effect for Survival data), which combines a treatment-specific semi-parametric Cox loss with a treatment-balanced deep neural network; i.e. we regularize differences between treated and non-treated patients using Integral Probability Metrics (IPM). RESULTS: We show in simulation studies that this approach outperforms the state of the art. Furthermore, we demonstrate in an application to a cohort of breast cancer patients that hormone treatment can be optimized based on six routine parameters. We successfully validated this finding in an independent cohort. AVAILABILITY AND IMPLEMENTATION: We provide BITES as an easy-to-use python implementation including scheduled hyper-parameter optimization (https://github.com/sschrod/BITES). The data underlying this article are available in the CRAN repository at https://rdrr.io/cran/survival/man/gbsg.html and https://rdrr.io/cran/survival/man/rotterdam.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Nowadays, several databases store information about patients and diseases, but only a few exists that focus directly on myocardial events and treatments. This paper is divided into two parts. In the first part, we list and summarize the ongoing European myocardial projects (Myocardial Ischaemia National Audit Project (MINAP) in England, Swedish Web-system for Enhancement and Development of Evidence-based care in Heart disease Evaluated According to Recommended Therapies (SWEDEHEART) in Sweden, National Registry of Acute Myocardial Infarction in Switzerland (AMIS Plus) in Switzerland). Where possible, we discuss the validity and accuracy of the stored data. In the second part, we introduce the history and legal environment of the Hungarian Myocardial Infarction Registry (HUMIR), and some research results that were achieved with the help of the information in the Hungarian registry.

MOTIVATION: In biomedicine, every molecular measurement is relative to a reference point, like a fixed aliquot of RNA extracted from a tissue, a defined number of blood cells, or a defined volume of biofluid. Reference points are often chosen for practical reasons. For example, we might want to assess the metabolome of a diseased organ but can only measure metabolites in blood or urine. In this case, the observable data only indirectly reflects the disease state. The statistical implications of these discrepancies in reference points have not yet been discussed. RESULTS: Here, we show that reference point discrepancies compromise the performance of regression models like the LASSO. As an alternative, we suggest zero-sum regression for a reference point insensitive analysis. We show that zero-sum regression is superior to the LASSO in case of a poor choice of reference point both in simulations and in an application that integrates intestinal microbiome analysis with metabolomics. Moreover, we describe a novel coordinate descent based algorithm to fit zero-sum elastic nets. AVAILABILITY AND IMPLEMENTATION: The R-package "zeroSum" can be downloaded at https://github.com/rehbergT/zeroSum Moreover, we provide all R-scripts and data used to produce the results of this manuscript as Supplementary Material CONTACT: [email protected], [email protected] and [email protected] information: Supplementary material is available at Bioinformatics online.

MOTIVATION: Molecular signatures for treatment recommendations are well researched. Still it is challenging to apply them to data generated by different protocols or technical platforms. RESULTS: We analyzed paired data for the same tumors (Burkitt lymphoma, diffuse large B-cell lymphoma) and features that had been generated by different experimental protocols and analytical platforms including the nanoString nCounter and Affymetrix Gene Chip transcriptomics as well as the SWATH and SRM proteomics platforms. A statistical model that assumes independent sample and feature effects accounted for 69-94% of technical variability. We analyzed how variability is propagated through linear signatures possibly affecting predictions and treatment recommendations. Linear signatures with feature weights adding to zero were substantially more robust than unbalanced signatures. They yielded consistent predictions across data from different platforms, both for transcriptomics and proteomics data. Similarly stable were their predictions across data from fresh frozen and matching formalin-fixed paraffin-embedded human tumor tissue. AVAILABILITY AND IMPLEMENTATION: The R-package 'zeroSum' can be downloaded at https://github.com/rehbergT/zeroSum . Complete data and R codes necessary to reproduce all our results can be received from the authors upon request. CONTACT: [email protected].

Bioinformatics (2017) 33 (14): i333-i340. The publisher wishes to inform readers that figure 5 was incorrect as published due to a production error re-positioning the letters (c), (d), (e) and (f), in the labelling of the figure, making it out of sync with the caption. The figure has now been corrected online.

Inter-cellular communication with stromal cells is vital for cancer cells. Molecules involved in the communication are potential drug targets. To identify them systematically, we applied a systems level analysis that combined reverse network engineering with causal effect estimation. Using only observational transcriptome profiles we searched for paracrine factors sending messages from activated hepatic stellate cells (HSC) to hepatocellular carcinoma (HCC) cells. We condensed these messages to predict ten proteins that, acting in concert, cause the majority of the gene expression changes observed in HCC cells. Among the 10 paracrine factors were both known and unknown cancer promoting stromal factors, the former including Placental Growth Factor (PGF) and Periostin (POSTN), while Pregnancy-Associated Plasma Protein A (PAPPA) was among the latter. Further support for the predicted effect of PAPPA on HCC cells came from both in vitro studies that showed PAPPA to contribute to the activation of NFκB signaling, and clinical data, which linked higher expression levels of PAPPA to advanced stage HCC. In summary, this study demonstrates the potential of causal modeling in combination with a condensation step borrowed from gene set analysis [Model-based Gene Set Analysis (MGSA)] in the identification of stromal signaling molecules influencing the cancer phenotype.

BACKGROUND: The Affymetrix MitoChip v2.0 is an oligonucleotide tiling array for the resequencing of the human mitochondrial (mt) genome. For each of 16,569 nucleotide positions of the mt genome it holds two sets of four 25-mer probes each that match the heavy and the light strand of a reference mt genome and vary only at their central position to interrogate all four possible alleles. In addition, the MitoChip v2.0 carries alternative local context probes to account for known mtDNA variants. These probes have been neglected in most studies due to the lack of software for their automated analysis. RESULTS: We provide ReseqChip, a free software that automates the process of resequencing mtDNA using multiple local context probes on the MitoChip v2.0. ReseqChip significantly improves base call rate and sequence accuracy. ReseqChip is available at http://code.open-bio.org/svnweb/index.cgi/bioperl/browse/bioperl-live/trunk/Bio/Microarray/Tools/. CONCLUSIONS: ReseqChip allows for the automated consolidation of base calls from alternative local mt genome context probes. It thereby improves the accuracy of resequencing, while reducing the number of non-called bases.