SEMtree: tree-based structure learning methods with structural equation models.

MOTIVATION: With the exponential growth of expression and protein-protein interaction (PPI) data, the identification of functional modules in PPI networks that show striking changes in molecular activity or phenotypic signatures becomes of particular interest to reveal process-specific information that is correlated with cellular or disease states. This requires both the identification of network nodes with reliability scores and the availability of an efficient technique to locate the network regions with the highest scores. In the literature, a number of heuristic methods have been suggested. We propose SEMtree(), a set of tree-based structure discovery algorithms, combining graph and statistically interpretable parameters together with a user-friendly R package based on structural equation models framework. RESULTS: Condition-specific changes from differential expression and gene-gene co-expression are recovered with statistical testing of node, directed edge, and directed path difference between groups. In the end, from a list of seed (i.e. disease) genes or gene P-values, the perturbed modules with undirected edges are generated with five state-of-the-art active subnetwork detection methods. The latter are supplied to causal additive trees based on Chu-Liu-Edmonds' algorithm (Chow and Liu, Approximating discrete probability distributions with dependence trees. IEEE Trans Inform Theory 1968;14:462-7) in SEMtree() to be converted in directed trees. This conversion allows to compare the methods in terms of directed active subnetworks. We applied SEMtree() to both Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) and simulated datasets with various differential expression patterns. Compared to existing methods, SEMtree() is able to capture biologically relevant subnetworks with simple visualization of directed paths, good perturbation extraction, and classifier performance. AVAILABILITY AND IMPLEMENTATION: SEMtree() function is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph.

SEMgsa: topology-based pathway enrichment analysis with structural equation models.

BACKGROUND: Pathway enrichment analysis is extensively used in high-throughput experimental studies to gain insight into the functional roles of pre-defined subsets of genes, proteins and metabolites. Methods that leverages information on the topology of the underlying pathways outperform simpler methods that only consider pathway membership, leading to improved performance. Among all the proposed software tools, there's the need to combine high statistical power together with a user-friendly framework, making it difficult to choose the best method for a particular experimental environment. RESULTS: We propose SEMgsa, a topology-based algorithm developed into the framework of structural equation models. SEMgsa combine the SEM p values regarding node-specific group effect estimates in terms of activation or inhibition, after statistically controlling biological relations among genes within pathways. We used SEMgsa to identify biologically relevant results in a Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) together with a frontotemporal dementia (FTD) DNA methylation dataset (GEO accession: GSE53740) and compared its performance with some existing methods. SEMgsa is highly sensitive to the pathways designed for the specific disease, showing low p values ([Formula: see text]) and ranking in high positions, outperforming existing software tools. Three pathway dysregulation mechanisms were used to generate simulated expression data and evaluate the performance of methods in terms of type I error followed by their statistical power. Simulation results confirm best overall performance of SEMgsa. CONCLUSIONS: SEMgsa is a novel yet powerful method for identifying enrichment with regard to gene expression data. It takes into account topological information and exploits pathway perturbation statistics to reveal biological information. SEMgsa is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph .

SARS-CoV-2 infection and acute ischemic stroke in Lombardy, Italy.

OBJECTIVE: To characterize patients with acute ischemic stroke related to SARS-CoV-2 infection and assess the classification performance of clinical and laboratory parameters in predicting in-hospital outcome of these patients. METHODS: In the setting of the STROKOVID study including patients with acute ischemic stroke consecutively admitted to the ten hub hospitals in Lombardy, Italy, between March 8 and April 30, 2020, we compared clinical features of patients with confirmed infection and non-infected patients by logistic regression models and survival analysis. Then, we trained and tested a random forest (RF) binary classifier for the prediction of in-hospital death among patients with COVID-19. RESULTS: Among 1013 patients, 160 (15.8%) had SARS-CoV-2 infection. Male sex (OR 1.53; 95% CI 1.06-2.27) and atrial fibrillation (OR 1.60; 95% CI 1.05-2.43) were independently associated with COVID-19 status. Patients with COVID-19 had increased stroke severity at admission [median NIHSS score, 9 (25th to75th percentile, 13) vs 6 (25th to75th percentile, 9)] and increased risk of in-hospital death (38.1% deaths vs 7.2%; HR 3.30; 95% CI 2.17-5.02). The RF model based on six clinical and laboratory parameters exhibited high cross-validated classification accuracy (0.86) and precision (0.87), good recall (0.72) and F1-score (0.79) in predicting in-hospital death. CONCLUSIONS: Ischemic strokes in COVID-19 patients have distinctive risk factor profile and etiology, increased clinical severity and higher in-hospital mortality rate compared to non-COVID-19 patients. A simple model based on clinical and routine laboratory parameters may be useful in identifying ischemic stroke patients with SARS-CoV-2 infection who are unlikely to survive the acute phase.

Impact of SARS-CoV-2 on reperfusion therapies for acute ischemic stroke in Lombardy, Italy: the STROKOVID network.

Whether and how SARS-CoV-2 outbreak affected in-hospital acute stroke care system is still matter of debate. In the setting of the STROKOVID network, a collaborative project between the ten centers designed as hubs for the treatment of acute stroke during SARS-CoV-2 outbreak in Lombardy, Italy, we retrospectively compared clinical features and process measures of patients with confirmed infection (COVID-19) and non-infected patients (non-COVID-19) who underwent reperfusion therapies for acute ischemic stroke. Between March 8 and April 30, 2020, 296 consecutive patients [median age, 74 years (interquartile range (IQR), 62-80.75); males, 154 (52.0%); 34 (11.5%) COVID-19] qualified for the analysis. Time from symptoms onset to treatment was longer in the COVID-19 group [230 (IQR 200.5-270) minutes vs. 190 (IQR 150-245) minutes; p = 0.007], especially in the first half of the study period. Patients with COVID-19 who underwent endovascular thrombectomy had more frequently absent collaterals or collaterals filling ≤ 50% of the occluded territory (50.0% vs. 16.6%; OR 5.05; 95% CI 1.82-13.80) and a lower rate of good/complete recanalization of the primary arterial occlusive lesion (55.6% vs. 81.0%; OR 0.29; 95% CI 0.10-0.80). Post-procedural intracranial hemorrhages were more frequent (35.3% vs. 19.5%; OR 2.24; 95% CI 1.04-4.83) and outcome was worse among COVID-19 patients (in-hospital death, 38.2% vs. 8.8%; OR 6.43; 95% CI 2.85-14.50). Our findings showed longer delays in the intra-hospital management of acute ischemic stroke in COVID-19 patients, especially in the early phase of the outbreak, that likely impacted patients outcome and should be the target of future interventions.