BACKGROUND: The matched case-control design, up until recently mostly pertinent to epidemiological studies, is becoming customary in biomedical applications as well. For instance, in omics studies, it is quite common to compare cancer and healthy tissue from the same patient. Furthermore, researchers today routinely collect data from various and variable sources that they wish to relate to the case-control status. This highlights the need to develop and implement statistical methods that can take these tendencies into account. RESULTS: We present an R package penalizedclr, that provides an implementation of the penalized conditional logistic regression model for analyzing matched case-control studies. It allows for different penalties for different blocks of covariates, and it is therefore particularly useful in the presence of multi-source omics data. Both L1 and L2 penalties are implemented. Additionally, the package implements stability selection for variable selection in the considered regression model. CONCLUSIONS: The proposed method fills a gap in the available software for fitting high-dimensional conditional logistic regression models accounting for the matched design and block structure of predictors/features. The output consists of a set of selected variables that are significantly associated with case-control status. These variables can then be investigated in terms of functional interpretation or validation in further, more targeted studies.

BACKGROUND: Cancer genomic studies often include data collected from several omics platforms. Each omics data source contributes to the understanding of the underlying biological process via source specific ("individual") patterns of variability. At the same time, statistical associations and potential interactions among the different data sources can reveal signals from common biological processes that might not be identified by single source analyses. These common patterns of variability are referred to as "shared" or "joint". In this work, we show how the use of joint and individual components can lead to better predictive models, and to a deeper understanding of the biological process at hand. We identify joint and individual contributions of DNA methylation, miRNA and mRNA expression collected from blood samples in a lung cancer case-control study nested within the Norwegian Women and Cancer (NOWAC) cohort study, and we use such components to build prediction models for case-control and metastatic status. To assess the quality of predictions, we compare models based on simultaneous, integrative analysis of multi-source omics data to a standard non-integrative analysis of each single omics dataset, and to penalized regression models. Additionally, we apply the proposed approach to a breast cancer dataset from The Cancer Genome Atlas. RESULTS: Our results show how an integrative analysis that preserves both components of variation is more appropriate than standard multi-omics analyses that are not based on such a distinction. Both joint and individual components are shown to contribute to a better quality of model predictions, and facilitate the interpretation of the underlying biological processes in lung cancer development. CONCLUSIONS: In the presence of multiple omics data sources, we recommend the use of data integration techniques that preserve the joint and individual components across the omics sources. We show how the inclusion of such components increases the quality of model predictions of clinical outcomes.

MOTIVATION: Visualization of high-dimensional data is an important step in exploratory data analysis and knowledge discovery. However, it is challenging, because the interpretation is highly subjective. If we see dimensionality reduction (DR) techniques as the main tool for data visualization, they are like multiple cameras that look into the data from different perspectives or angles. We can hardly prescribe one single perspective for all datasets and problems. One snapshot of data cannot reveal all the relevant aspects of the data in higher dimensions. The reason is that each of these methods has its own specific strategy, normally based on well-established mathematical theories to obtain a low-dimensional projection of the data, which sometimes is totally different from the others. Therefore, relying only on one single projection can be risky, because it can close our eyes to important parts of the full knowledge space. RESULTS: We propose the first framework for multi-insight data visualization of multi-omics data. This approach, contrary to single-insight approaches, is able to uncover the majority of data features through multiple insights. The main idea behind the methodology is to combine several DR methods via tensor factorization and group the solutions into an optimal number of clusters (or insights). The experimental evaluation with low-dimensional synthetic data, simulated multi-omics data related to ovarian cancer, as well as real multi-omics data related to breast cancer show the competitive advantage over state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: https://folk.uio.no/hadift/MIV/ [user/pass via hadift@medisin. uio.no]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dimension reduction (DR) methods play an inevitable role in analyzing and visualizing high-dimensional multi-source data. In the recent decades many variants of these methods have been developed in various disciplines and domains. Due to the diversity and an ever-increasing number of developed techniques, choosing the right method for the given problem is a difficult task. In this study we benchmark 87 methods for integrative dimension reduction of mRNA expression and DNA methylation data, which is a common problem in biology and medicine. Our ranking is obtained based on four main factors: quality of dimension reduction (local, global, and local-global neighborhood preservation), clustering quality, speed and sensitivity to input parameters on multiple datasets generated by InterSIM (a semi-realistic multi-source data simulator in the genomics domain). The results are later validated on a real dataset for breast cancer through visual evaluation metrics such as co-ranking matrices, inspection of true cancer sub-types in two-dimensional projections, and LCMC curves. We also demonstrate the relationship between the methods via network inference. The findings in this study can be useful in algorithm selection and planning of experimental design in multi-source data analysis.

The aim of this study was to investigate if participants with pain experience reduced pain development when using a mouse allowing a more neutral position of the wrist (Anir) compared with development of pain using a traditional mouse. The study population consisted of 67 participants with mean intensity of pain of approximately 50 mm on a 100-mm Visual Analog Scale (VAS). The total group was randomly divided into 1 intervention group and 1 control group. The study was performed as a prospective parallel group study. VAS was used to assess the average level of pain in the musculoskeletal system during a 6-month period. An earlier article on this study found that after using the Anir mouse for 6 months, a significant reduction was reported in neck pain (48.9 to 33.9). Corresponding data for other areas of the upper extremities were shoulder (54.1 to 31.8), forearm (52.9 to 32.8), and wrist and hand (42.5 to 22.3), respectively (Aarås, Ro, & Thoresen, 1999). The control group using the traditional mouse reported no significant changes in pain level. This article describes the results after giving an identical intervention to the control group. After 6 months, the former control group reported a significant reduction in average pain for the following body areas: shoulder, M = 48.0 (CI = 32.5-63.5) to M = 28.7 (CI = 18.7-38.8); forearm, M = 45.6 (CI = 30.8-60.4) to M = 15.6 (CI = 5.5-25.7); and wrist and hand, M = 34.8 (CI = 20.1-49.5) to M = 15.8 (6.4-25.2). Neck pain was marginally significantly reduced, M = 39.4 (CI = 25.2-53.6) to M = 27.4 (CI = 15.2-39.6), p = .07. The group getting the initial intervention did not report any significant changes in any of the body areas from 6 to 12 months after the study period; that is, the reduction in pain level obtained still existed. The results from this study indicate clearly the importance of using a more neutral position of the forearm when using a computer mouse. Laboratory tests on performance measures (speed and accuracy) showed that the Anir mouse falls well within the range of performance measures associated with already existing commercially available input devices.

In a field study, a newly developed mouse that gives the operator a more neutral forearm position was compared with a traditional mouse using a more pronated forearm. After using the new mouse for 6 months, a significant reduction was reported regarding pain intensity and frequency for wrist/hand, forearm, shoulder, and neck (p £ .009). The control group using the traditional mouse reported only small changes in the pain level (p ³ .24). Total duration of pain the last 6 months was also significantly improved for the shoulder and forearm in the intervention group, whereas no such changes were observed in the control group. These results clearly indicate the importance of using a computer mouse with a more neutral position of the forearm.