Summary: RNA-Seq is an exciting methodology that leverages the power of high-throughput sequencing to measure RNA transcript counts at an unprecedented accuracy. However, the data generated from this process are extremely large and biologist-friendly tools with which to analyze it are sorely lacking. MultiExperiment Viewer (MeV) is a Java-based desktop application that allows advanced analysis of gene expression data through an intuitive graphical user interface. Here, we report a significant enhancement to MeV that allows analysis of RNA-Seq data with these familiar, powerful tools. We also report the addition to MeV of several RNA-Seq-specific functions, addressing the differences in analysis requirements between this data type and traditional gene expression data. These tools include automatic conversion functions from raw count data to processed RPKM or FPKM values and differential expression detection and functional annotation enrichment detection based on published methods.Availability: MeV version 4.7 is written in Java and is freely available for download under the terms of the open-source Artistic License version 2.0. The website (http://mev.tm4.org/) hosts a full user manual as well as a short quick-start guide suitable for new users.Contact: johnq@jimmy.harvard.edu

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Data describing outcomes of solid organ transplant (SOT) recipients with coronavirus disease 2019 (COVID‐19) are variable, and the association between SOT status and mortality remains unclear. In this study, we compare clinical outcomes of SOT recipients hospitalized with COVID‐19 between March 10, and September 1, 2020, to a matched cohort of non‐SOT recipients at a national healthcare system in the United States (US). From a population of 43 461 hospitalized COVID‐19‐positive patients, we created a coarsened exact matched cohort of 4035 patients including 128 SOT recipients and 3907 weighted matched non‐SOT controls. Multiple logistic regression was used to evaluate association between SOT status and clinical outcomes. Among the 4035 patients, median age was 60 years, 61.7% were male, 21.9% were Black/African American, and 50.8% identified as Hispanic/Latino ethnicity. Patients with a history of SOT were more likely to die within the study period when compared to matched non‐SOT recipients (21.9% and 14.9%, respectively; odds ratio [OR] 1.93; 95% confidence interval [CI]: 1.18–3.15). Moreover, SOT status was associated with increased odds of receiving invasive mechanical ventilation (OR [95% CI]: 2.34 [1.51–3.65]), developing acute kidney injury (OR [95% CI]: 2.41 [1.59–3.65]), and receiving vasopressor support during hospitalization (OR [95% CI]: 2.14 [1.31–3.48]).

BACKGROUND. Evidence supporting convalescent plasma (CP), one of the first investigational treatments for COVID-19, has been inconclusive, leading to conflicting recommendations. The primary objective was to perform a comparative effectiveness study of CP for all-cause, in-hospital mortality in patients with COVID-19. METHODS.The multicenter, electronic health records-based, retrospective study included 44,770 patients hospitalized with COVID-19 in one of 176 HCA Healthcare-affiliated community hospitals. Coarsened exact matching (1:k) was employed, resulting in a sample of 3,774 CP and 10,687 comparison patients. RESULTS.Examination of mortality using a shared frailty model, controlling for concomitant medications, date of admission, and days from admission to transfusion, demonstrated a significant association of CP with lower mortality risk relative to the comparison group (aHR=0.71, 95%CI 0.59-0.86, p<0.001). Examination of patient risk trajectories, represented by 400 clinicodemographic features from our Real-Time Risk Model (RTRM), indicated that patients who received CP recovered quicker. The stratification of days to transfusion revealed that CP within 3 days after admission, but not 4-7 days, was associated with a significantly lower mortality risk (aHR=0.53, 95%CI 0.47-0.60, p<0.001). CP serology level was inversely associated with mortality when controlling for its interaction with days to transfusion (HR=0.998, 95%CI 0.997-0.999, p=0.013) yet not reaching univariable significance.CONCLUSIONS. This large, diverse, multicenter cohort study demonstrated that CP, compared to matched controls, is significantly associated with reduced risk of in-hospital mortality. These observations highlight the utility of real-world evidence and suggest the need for further evaluation prior to abandoning CP as a viable therapy for COVID-19.

BackgroundSpecific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks.ResultsHere we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state.ConclusionsWe demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0517-y) contains supplementary material, which is available to authorized users.