Functional Analysis: Evaluation of Response Intensities - Tailoring ANOVA for Lists of Expression Subsets

Background: Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, co-regulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms. Results: As several methods assume different null hypotheses, we propose to reformulate the main question that biologists seek to answer. To determine which genesets are associated with expression values that differ between two experiments, we focused on three ad hoc criteria: expression levels, the direction of individual gene expression changes (up or down regulation), and correlations between genes. We introduce the FAERI methodology, tailored from a two-way ANOVA to examine these criteria. The significance of the results was evaluated according to the self-contained null hypothesis, using label sampling or by inferring the null distribution from normally distributed random data. Evaluations performed on simulated data revealed that FAERI outperforms currently available methods for each type of set tested. We then applied the FAERI method to analyze three real-world datasets on hypoxia response. FAERI was able to detect more genesets than other methodologies, and the genesets selected were coherent with current knowledge of cellular response to hypoxia. Moreover, the genesets selected by FAERI were confirmed when the analysis was repeated on two additional related datasets. Conclusions: The expression values of genesets are associated with several biological effects. The underlying mathematical structure of the genesets allows for analysis of data from several genes at the same time. Focusing on expression levels, the direction of the expression changes, and correlations, we showed that two-step data reduction allowed us to significantly improve the performance of geneset analysis using a modified two-way ANOVA procedure, and to detect genesets that current methods fail to detect

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine

Predictors and associations of the persistent airflow limitation phenotype in asthma: a post-hoc analysis of the ATLANTIS study

Background: Persistent airflow limitation (PAL) occurs in a subset of patients with asthma. Previous studies on PAL in asthma have included relatively small populations, mostly restricted to severe asthma, or have no included longitudinal data. The aim of this post-hoc analysis was to investigate the determinants, clinical implications, and outcome of PAL in patients with asthma who were included in the ATLANTIS study. Methods: In this post-hoc analysis of the ATLANTIS study, we assessed the prevalence, clinical characteristics, and implications of PAL across the full range of asthma severity. The study population included patients aged 18–65 years who had been diagnosed with asthma at least 6 months before inclusion. We defined PAL as a post-bronchodilator FEV1/forced vital capacity (FVC) of less than the lower limit of normal at recruitment. Asthma severity was defined according to the Global Initiative for Asthma. We used Mann-Whitney U test, t test, or χ2 test to analyse differences in baseline characteristics between patients with and without PAL. Logistic regression was used for multivariable analysis of the associations between PAL and baseline data. Cox regression was used to analyse risk of exacerbation in relation to PAL, and a linear mixed-effects model was used to analyse change in FEV1 over time in patients with versus patients without PAL. Results were validated in the U-BIOPRED cohort. Findings: Between June 30, 2014 and March 3, 2017, 773 patients were enrolled in the ATLANTIS study of whom 760 (98%) had post-bronchodilator FEV1/FVC data available. Of the included patients with available data, mean age was 44 years (SD 13), 441 (58%) of 760 were women, 578 (76%) were never-smokers, and 248 (33%) had PAL. PAL was not only present in patients with severe asthma, but also in 21 (16%) of 133 patients with GINA step 1 and 24 (29%) of 83 patients with GINA step 2. PAL was independently associated with older age at baseline (46 years in PAL group vs 43 years in non-PAL group), longer duration of asthma (24 years vs 12 years), male sex (51% vs 38%), higher blood eosinophil counts (median 0·27 × 109 cells per L vs 0·20 × 109 cells per L), more small airway dysfunction, and more exacerbations during 1 year of follow-up. Associations between PAL, age, and eosinophilic inflammation were validated in the U-BIOPRED cohort, whereas associations with sex, duration of asthma, and risk of exacerbations were not validated. Interpretation: PAL is not only present in severe disease, but also in a considerable proportion of patients with milder disease. In patients with mild asthma, PAL is associated with eosinophilic inflammation and a higher risk of exacerbations. Our findings are important because they suggest that increasing treatment intensity should be considered in patients with milder asthma and PAL. Funding: Chiesi Farmaceutici and Dutch Ministry of Economic Affairs and Climate Policy (by means of the public–private partnership programme)

Candida bloodstream infections in intensive care units: analysis of the extended prevalence of infection in intensive care unit study

Item does not contain fulltextOBJECTIVES: To provide a global, up-to-date picture of the prevalence, treatment, and outcomes of Candida bloodstream infections in intensive care unit patients and compare Candida with bacterial bloodstream infection. DESIGN: A retrospective analysis of the Extended Prevalence of Infection in the ICU Study (EPIC II). Demographic, physiological, infection-related and therapeutic data were collected. Patients were grouped as having Candida, Gram-positive, Gram-negative, and combined Candida/bacterial bloodstream infection. Outcome data were assessed at intensive care unit and hospital discharge. SETTING: EPIC II included 1265 intensive care units in 76 countries. PATIENTS: Patients in participating intensive care units on study day. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: Of the 14,414 patients in EPIC II, 99 patients had Candida bloodstream infections for a prevalence of 6.9 per 1000 patients. Sixty-one patients had candidemia alone and 38 patients had combined bloodstream infections. Candida albicans (n = 70) was the predominant species. Primary therapy included monotherapy with fluconazole (n = 39), caspofungin (n = 16), and a polyene-based product (n = 12). Combination therapy was infrequently used (n = 10). Compared with patients with Gram-positive (n = 420) and Gram-negative (n = 264) bloodstream infections, patients with candidemia were more likely to have solid tumors (p < .05) and appeared to have been in an intensive care unit longer (14 days [range, 5-25 days], 8 days [range, 3-20 days], and 10 days [range, 2-23 days], respectively), but this difference was not statistically significant. Severity of illness and organ dysfunction scores were similar between groups. Patients with Candida bloodstream infections, compared with patients with Gram-positive and Gram-negative bloodstream infections, had the greatest crude intensive care unit mortality rates (42.6%, 25.3%, and 29.1%, respectively) and longer intensive care unit lengths of stay (median [interquartile range]) (33 days [18-44], 20 days [9-43], and 21 days [8-46], respectively); however, these differences were not statistically significant. CONCLUSION: Candidemia remains a significant problem in intensive care units patients. In the EPIC II population, Candida albicans was the most common organism and fluconazole remained the predominant antifungal agent used. Candida bloodstream infections are associated with high intensive care unit and hospital mortality rates and resource use

Candida bloodstream infections in intensive care units: analysis of the extended prevalence of infection in intensive care unit study

To provide a global, up-to-date picture of the prevalence, treatment, and outcomes of Candida bloodstream infections in intensive care unit patients and compare Candida with bacterial bloodstream infection. DESIGN: A retrospective analysis of the Extended Prevalence of Infection in the ICU Study (EPIC II). Demographic, physiological, infection-related and therapeutic data were collected. Patients were grouped as having Candida, Gram-positive, Gram-negative, and combined Candida/bacterial bloodstream infection. Outcome data were assessed at intensive care unit and hospital discharge. SETTING: EPIC II included 1265 intensive care units in 76 countries. PATIENTS: Patients in participating intensive care units on study day. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: Of the 14,414 patients in EPIC II, 99 patients had Candida bloodstream infections for a prevalence of 6.9 per 1000 patients. Sixty-one patients had candidemia alone and 38 patients had combined bloodstream infections. Candida albicans (n = 70) was the predominant species. Primary therapy included monotherapy with fluconazole (n = 39), caspofungin (n = 16), and a polyene-based product (n = 12). Combination therapy was infrequently used (n = 10). Compared with patients with Gram-positive (n = 420) and Gram-negative (n = 264) bloodstream infections, patients with candidemia were more likely to have solid tumors (p < .05) and appeared to have been in an intensive care unit longer (14 days [range, 5-25 days], 8 days [range, 3-20 days], and 10 days [range, 2-23 days], respectively), but this difference was not statistically significant. Severity of illness and organ dysfunction scores were similar between groups. Patients with Candida bloodstream infections, compared with patients with Gram-positive and Gram-negative bloodstream infections, had the greatest crude intensive care unit mortality rates (42.6%, 25.3%, and 29.1%, respectively) and longer intensive care unit lengths of stay (median [interquartile range]) (33 days [18-44], 20 days [9-43], and 21 days [8-46], respectively); however, these differences were not statistically significant. CONCLUSION: Candidemia remains a significant problem in intensive care units patients. In the EPIC II population, Candida albicans was the most common organism and fluconazole remained the predominant antifungal agent used. Candida bloodstream infections are associated with high intensive care unit and hospital mortality rates and resource use