Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology

Non-negative matrix factorization is a useful tool for reducing the dimension of large datasets. This work considers simultaneous non-negative matrix factorization of multiple sources of data. In particular, we perform the first study that involves more than two datasets. We discuss the algorithmic issues required to convert the approach into a practical computational tool and apply the technique to new gene expression data quantifying the molecular changes in four tissue types due to different dosages of an experimental panPPAR agonist in mouse. This study is of interest in toxicology because, whilst PPARs form potential therapeutic targets for diabetes, it is known that they can induce serious side-effects. Our results show that the practical simultaneous non-negative matrix factorization developed here can add value to the data analysis. In particular, we find that factorizing the data as a single object allows us to distinguish between the four tissue types, but does not correctly reproduce the known dosage level groups. Applying our new approach, which treats the four tissue types as providing distinct, but related, datasets, we find that the dosage level groups are respected. The new algorithm then provides separate gene list orderings that can be studied for each tissue type, and compared with the ordering arising from the single factorization. We find that many of our conclusions can be corroborated with known biological behaviour, and others offer new insights into the toxicological effects. Overall, the algorithm shows promise for early detection of toxicity in the drug discovery process

Sick leave among home-care personnel: a longitudinal study of risk factors

BACKGROUND: Sick leave due to neck, shoulder and back disorders (NSBD) is higher among health-care workers, especially nursing aides/assistant nurses, compared with employees in other occupations. More information is needed about predictors of sick leave among health care workers. The aim of the study was to assess whether self-reported factors related to health, work and leisure time could predict: 1) future certified sick leave due to any cause, in nursing aides/assistant nurses (Study group I) and 2) future self-reported sick leave due to NSBD in nursing aides/assistant nurses (Study group II). METHODS: Study group I, comprised 443 female nursing aides/assistant nurses, not on sick leave at baseline when a questionnaire was completed. Data on certified sick leave were collected after 18 months. Study group II comprised 274 of the women, who at baseline reported no sick leave during the preceding year due to NSBD and who participated at the 18 month follow-up. Data on sick leave due to NSBD were collected from the questionnaire at 18 months. The associations between future sick leave and factors related to health, work and leisure time were tested by logistic regression analyses. RESULTS: Health-related factors such as previous low back disorders (OR: 1.89; 95% CI 1.20–2.97) and previous sick leave (OR 6.40; 95%CI 3.97–10.31), were associated with a higher risk of future sick leave due to any cause. Factors related to health, work and leisure time, i.e. previous low back disorders (OR: 4.45; 95% CI 1.27–15.77) previous sick leave, not due to NSBD (OR 3.30; 95%CI 1.33–8.17), high strain work (OR 2.34; 95%CI 1.05–5.23) and high perceived physical exertion in domestic work (OR 2.56; 95%CI 1.12–5.86) were associated with a higher risk of future sick leave due to NSBD. In the final analyses, previous low back disorders and previous sick leave remained significant in both study groups. CONCLUSION: The results suggest a focus on previous low back disorders and previous sick leave for the design of early prevention programmes aiming at reducing future sick leave due to any cause, as well as due to NSBD, among nursing aides/assistant nurses. A multifactorial approach may be of importance in the early prevention of sick leave due to NSBD