La vie des abeilles du Patalours

Travail personnel du module 1 des cours de préparation au Brevet Féréral d'apiculture. L'emplacement d'un rucher joue un rôle primordial pour la santé et le développement d'une colonie. Dans ce rapport, nous analysons différents aspects des emplacements de nos ruchers : la situation générale, la disponibilité des ressources et les possibles nuisances ou problèmes de voisinage. L'analyse des ruchers personnels est complétée d'informations tirées d'une recherche documentaire qui permettent une mise en contexte plus générale. Nous identifions les points faibles de la situation actuelle et discutons comment ces aspects pourraient être améliorés

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data

The consensus molecular subtypes of colorectal cancer

Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression-based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features: CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation; CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation; CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; and CMS4 (mesenchymal, 23%), prominent transforming growth factor-beta activation, stromal invasion and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intratumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC-with clear biological interpretability-and the basis for future clinical stratification and subtype-based targeted interventions

The Consensus Molecular Subtypes of Colorectal Cancer

Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use -- https://www.nature.com/authors/policies/license.html#termsColorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression-based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMS) with distinguishing features: CMS1 (MSI Immune, 14%), hypermutated, microsatellite unstable, strong immune activation; CMS2 (Canonical, 37%), epithelial, chromosomally unstable, marked WNT and MYC signaling activation; CMS3 (Metabolic, 13%), epithelial, evident metabolic dysregulation; and CMS4 (Mesenchymal, 23%), prominent transforming growth factor β activation, stromal invasion, and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intra-tumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC - with clear biological interpretability - and the basis for future clinical stratification and subtype-based targeted interventions

protocole_55.pdf

Résultats des MM16/17 pour les 3 reines dans le protocole 55

Open research data at SNSF

As of October 2017 SNSF funding applications must include a Data Management Plan (DMP). The DMP holds information on what data will be collected or generated, how the data will be handled during the project, and how the data will be published and preserved. Additionally, the SNSF expects that research data will be published and made freely available whenever possible. Dr. Sarah Gerster and Dr. Martin von Arx will present the new SNSF guidelines on Open Research Data and answer your questions

Step-by-step description of the flowchart illustrated in Figure 2.

<p>The code used to produce the results presented in this manuscript is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100335#pone.0100335.s002" target="_blank">Supporting Information S2</a>.</p

The cross-validation scheme employed in the study.

<p>The upper panel illustrates the combination of the inner cross-validation loop, which is used to estimate the optimal combination of the classifier hyperparameter and number of features, and the outer cross-validation loop, which is used to estimate the predictive performance of the constructed classifier. The lower panel shows how the final classifier is built on the whole input data set, and its performance is estimated on an external validation data set. The bias of the estimate from the cross-validation procedure is obtained by comparing the values in the two colored boxes.</p