The Pediatric Cell Atlas: defining the growth phase of human development at single-cell resolution

Single-cell gene expression analyses of mammalian tissues have uncovered profound stage-specific molecular regulatory phenomena that have changed the understanding of unique cell types and signaling pathways critical for lineage determination, morphogenesis, and growth. We discuss here the case for a Pediatric Cell Atlas as part of the Human Cell Atlas consortium to provide single-cell profiles and spatial characterization of gene expression across human tissues and organs. Such data will complement adult and developmentally focused HCA projects to provide a rich cytogenomic framework for understanding not only pediatric health and disease but also environmental and genetic impacts across the human lifespan

Sequential targeted exome sequencing of 1001 patients affected by unexplained limb-girdle weakness

Several hundred genetic muscle diseases have been described, all of which are rare. Their clinical and genetic heterogeneity means that a genetic diagnosis is challenging. We established an international consortium, MYO-SEQ, to aid the work-ups of muscle disease patients and to better understand disease etiology. Exome sequencing was applied to 1001 undiagnosed patients recruited from more than 40 neuromuscular disease referral centers; standardized phenotypic information was collected for each patient. Exomes were examined for variants in 429 genes associated with muscle conditions. We identified suspected pathogenic variants in 52% of patients across 87 genes. We detected 401 novel variants, 116 of which were recurrent. Variants in CAPN3, DYSF, ANO5, DMD, RYR1, TTN, COL6A2, and SGCA collectively accounted for over half of the solved cases; while variants in newer disease genes, such as BVES and POGLUT1, were also found. The remaining well-characterized unsolved patients (48%) need further investigation. Using our unique infrastructure, we developed a pathway to expedite muscle disease diagnoses. Our data suggest that exome sequencing should be used for pathogenic variant detection in patients with suspected genetic muscle diseases, focusing first on the most common disease genes described here, and subsequently in rarer and newly characterized disease genes

How the Choice of Distance Measure Influences the Detection of Prior-Data Conflict

Thepresentpapercontraststworelatedcriteriafortheevaluationofprior-dataconflict: the Data Agreement Criterion (DAC; Bousquet, 2008) and the criterion of Nott et al. (2016). One aspect that these criteria have in common is that they depend on a distance measure, of which dozens are available, but so far, only the Kullback-Leibler has been used. We describe and compare both criteria to determine whether a different choice of distance measure might impact the results. By means of a simulation study, we investigate how the choice of a specific distance measure influences the detection of prior-data conflict. The DAC seems more susceptible to the choice of distance measure, while the criterion of Nott et al. seems to lead to reasonably comparable conclusions of prior-data conflict, regardless of the distance measure choice. We conclude with some practical suggestions for the user of the DAC and the criterion of Nott et al

How the Choice of Distance Measure Influences the Detection of Prior-Data Conflict

Thepresentpapercontraststworelatedcriteriafortheevaluationofprior-dataconflict: the Data Agreement Criterion (DAC; Bousquet, 2008) and the criterion of Nott et al. (2016). One aspect that these criteria have in common is that they depend on a distance measure, of which dozens are available, but so far, only the Kullback-Leibler has been used. We describe and compare both criteria to determine whether a different choice of distance measure might impact the results. By means of a simulation study, we investigate how the choice of a specific distance measure influences the detection of prior-data conflict. The DAC seems more susceptible to the choice of distance measure, while the criterion of Nott et al. seems to lead to reasonably comparable conclusions of prior-data conflict, regardless of the distance measure choice. We conclude with some practical suggestions for the user of the DAC and the criterion of Nott et al

Approximate measurement invariance

This chapter focuses on a practical analysis of the Bayesian approximate measurement invariance model using standard software. It introduces the concept of approximate measurement invariance and illustrates the use of its most basic variant. The chapter discusses the use of measurement invariance testing in latent variable measurement models. In such models, the response functions are estimated through presumed conditional independence assumptions, and investigation of measurement invariance proceeds through restrictions on the parameters of these estimated functions. The most common model for this test is the confirmatory factor model, but this framework also includes item response theory (IRT) models, latent class models, and generalized multitrait‐multimethod models. The chapter focuses on a multigroup confirmatory factor analysis (MGCFA). The methodological literature on cross‐cultural and cross‐country analysis has recommended testing for measurement equivalence to guarantee that differences across groups are due to substantive true differences and not methodological artifact

Het betrouwbaarheidsinterval in intelligentietests : Hoe zat het ook alweer?

Het 95% betrouwbaarheidsinterval in de verslaglegging van intelligentietests kan grofweg geïnterpreteerd worden als een soort ‘foutenmarge’ rondom de verkregen IQ-score van het kind. De exacte interpretatie ligt ingewikkelder. Genoemd interval staat er dan ook om bekend vaak verkeerd geïnterpreteerd te worden. Daarom bieden Kimberley Lek en collega’s een ‘opfrisser’: hoe wordt een betrouwbaarheidsinterval ook al weer opgesteld ? De WISC-IIInl wordt gebruikt ter illustratie