Power and Sample Size Determination in the Rasch Model: Evaluation of the Robustness of a Numerical Method to Non-Normality of the Latent Trait

Patient-reported outcomes (PRO) have gained importance in clinical and epidemiological research and aim at assessing quality of life, anxiety or fatigue for instance. Item Response Theory (IRT) models are increasingly used to validate and analyse PRO. Such models relate observed variables to a latent variable (unobservable variable) which is commonly assumed to be normally distributed. A priori sample size determination is important to obtain adequately powered studies to determine clinically important changes in PRO. In previous developments, the Raschpower method has been proposed for the determination of the power of the test of group effect for the comparison of PRO in cross-sectional studies with an IRT model, the Rasch model. The objective of this work was to evaluate the robustness of this method (which assumes a normal distribution for the latent variable) to violations of distributional assumption. The statistical power of the test of group effect was estimated by the empirical rejection rate in data sets simulated using a non-normally distributed latent variable. It was compared to the power obtained with the Raschpower method. In both cases, the data were analyzed using a latent regression Rasch model including a binary covariate for group effect. For all situations, both methods gave comparable results whatever the deviations from the model assumptions. Given the results, the Raschpower method seems to be robust to the non-normality of the latent trait for determining the power of the test of group effect.     

A Foundation for Reliable Spatial Proteomics Data Analysis

Quantitative mass-spectrometry-based spatial proteomics involves elaborate, expensive, and time-consuming experimental procedures, and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches for establishing high-quality proteome-wide datasets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis, as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering, and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques. We also propose sound analysis strategies for identifying dynamic changes in subcellular localization by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis.     

Protease-activated receptor-1 (hPar1), a survival factor eliciting tumor progression

Although ample evidence point to the central involvement of protease activated receptor-1 (PAR1) in tumor progression, little is known about the fate of the tumor when hPar1 is being silenced. We observed that hPar1 antisense clones exhibit low PAR1 levels, attenuated cell proliferation and invasion in vitro, and tumor formation in vivo. These clones showed noticeably reduced paxillin phosphorylation compared with the parental A375SM cells, whereas no change in the integrin levels was noticed. Antisense clones injected into the mice resulted in very few and only occasional small tumors, whereas advanced and vascularized tumors were observed in A375SM cells. The antisense-derived tumor sections expressed active caspase-3, increased terminal deoxynucleotidyl transferase-mediated nick-end labeling staining, and a markedly reduced proliferating cell nuclear antigen level compared with A375SM cell-derived tissue sections. Likewise, ablation of the hPar1 gene in a tetracycline-inducible hPar1 system leads to apoptosis in immature blood vessels, whereas mature vessels were unaffected. The activation of PAR1-induced pAkt/protein kinase B abrogated serum-deprived Bim(EL) induction and also markedly inhibited Bax levels. On the other hand, small interfering RNA silencing of the hPar1 gene induced the expression of Bim(EL), a direct substrate of Akt/protein kinase B and also induced expression of active caspase-9 and caspase-3. These results altogether identify PAR1 as a survival factor that protects cells from undergoing apoptosis. We conclude that whereas PAR1 gene expression correlates with tumor progression, its neutralization effectively initiates an apoptotic pathway leading at least in part to significantly reduced tumor formation.     

Microarray Analysis of Microbiota of Gingival Lesions in Noma Patients

Noma (cancrum oris) is a gangrenous disease of unknown etiology affecting the maxillo-facial region of young children in extremely limited resource countries. In an attempt to better understand the microbiological events occurring during this disease, we used phylogenetic and low-density microarrays targeting the 16S rRNA gene to characterize the gingival flora of acute noma and acute necrotizing gingivitis (ANG) lesions, and compared them to healthy control subjects of the same geographical and social background. Our observations raise doubts about Fusobacterium necrophorum, a previously suspected causative agent of noma, as this species was not associated with noma lesions. Various oral pathogens were more abundant in noma lesions, notably Atopobium spp., Prevotella intermedia, Peptostreptococcus spp., Streptococcus pyogenes and Streptococcus anginosus. On the other hand, pathogens associated with periodontal diseases such as Aggregatibacter actinomycetemcomitans, Capnocytophaga spp., Porphyromonas spp. and Fusobacteriales were more abundant in healthy controls. Importantly, the overall loss of bacterial diversity observed in noma samples as well as its homology to that of ANG microbiota supports the hypothesis that ANG might be the immediate step preceding noma.