Motivation Tissue-level omics data such as transcriptomics and epigenomics are an average across diverse cell types. To extract cell-type-specific (CTS) signals, dozens of cellular deconvolution methods have been proposed to infer cell-type fractions from tissue-level data. However, these methods produce vastly different results under various real data settings. Simulation-based benchmarking studies showed no universally best deconvolution approaches. There have been attempts of ensemble methods, but they only aggregate multiple single-cell references or reference-free deconvolution methods. Results To achieve a robust estimation of cellular fractions, we proposed EnsDeconv (Ensemble Deconvolution), which adopts CTS robust regression to synthesize the results from eleven single deconvolution methods, ten reference datasets, five marker gene selection procedures, five data normalizations, and two transformations. Unlike most benchmarking studies based on simulations, we compiled four large real datasets of 4,937 tissue samples in total with measured cellular fractions and bulk gene expression from different tissues. Comprehensive evaluations demonstrated that EnsDeconv yields more stable, robust, and accurate fractions than existing methods. We illustrated that EnsDeconv estimated cellular fractions enable various CTS downstream analyses such as differential fractions associated with clinical variables. We further extended EnsDeconv to analyze bulk DNA methylation data. Availability EnsDeconv is freely available as an R-package from https://github.com/randel/EnsDeconv. Supplementary information Supplementary data are available at Bioinformatics online.

In patients with chronic kidney disease (CKD), self-rated health ("In general, how do you rate your health?") is associated with mortality. The association of selfrated health with functional status is unknown. We evaluated the association of limitations in activities of daily living (ADLs) with self-rated health and clinical correlates in a cohort of patients with CKD stages 1-5.

A practical and mild metal-free oxidative C-H functionalization of N-carbamoyl tetrahydro-β-carbolines has been reported. This reaction has excellent functional group tolerance, and exhibits a broad range of potassium trifluoroborate components, allowing for the facile C-H functionalization of electronically varied N-carbamoyl THCs in high efficiency with excellent regioselectivity.

Background. The Surprise Question (SQ; "Would you be surprised if this patient died in the next 12 months?") is a validated prognostication tool for mortality and hospitalization among patients with advanced CKD. Barriers in clinical workflow have slowed SQ implementation into practice. Objectives. (1) To evaluate implementation outcomes following use of electronic health record (EHR) decision support to automate collection of the SQ. (2) To assess the prognostic utility of the SQ for mortality and hospitalization/emergency room (ER) visits. Methods. We developed and implemented a best practice alert (BPA) in the electronic health record (EHR) to identify nephrology outpatients > 60 years of age with an eGFR<30 ml/min. At appointment, the BPA prompted the physician to answer the SQ. We assessed the rate and timeliness of provider responses. We conducted a post-hoc open-ended survey to assess physician perceptions of SQ implementation. We assessed the SQ's prognostic utility in survival and time-to-hospital encounter (hospitalization/ER visit) analyses. Results. Among 510 patients for whom the BPA triggered, 95 (18.6%) had the SQ completed by 16 physicians. Among those completed, nearly all (97.9%) were on appointment day, and 61 (64.2%) the first time the BPA fired. Providers answered "No" for 27 (28.4%) and "Yes" for 68 (71.6%) patients. By 12 months, 6 (22.2%) "No" patients died; 3 (4.4%) "Yes" patients died (hazards ratio [HR] 2.86, ref:Yes, 95% CI[1.06, 7.69]). About 35% of "No" patients and 32% of "Yes" patients had a hospital encounter by 12 months (HR 1.85, ref:Yes, 95% CI[0.93, 3.69]). Physicians noted (1) they had goals-of-care conversations unprompted; (2) EHR-based interventions alone for goals-of-care are ineffective; and (3) more robust engagement is necessary. Conclusions. We successfully integrated the SQ into the EHR to aid in clinical practice. Additional implementation efforts are needed to encourage further integration of the SQ in clinical practice.

Bulk transcriptomics in tissue samples reflects the average expression levels across different cell types and is highly influenced by cellular fractions. As such, it is critical to estimate cellular fractions to both deconfound differential expression analyses and infer cell type specific differential expression. Since experimentally counting cells is infeasible in most tissues and studies, in silico cellular deconvolution methods have been developed as an alternative. However, existing methods are designed for tissues consisting of clearly distinguishable cell types and have difficulties estimating highly correlated or rare cell types. To address this challenge, we propose Hierarchical Deconvolution (HiDecon) that uses single-cell RNA sequencing references and a hierarchical cell type tree, which models the similarities among cell types and cell differentiation relationships, to estimate cellular fractions in bulk data. By coordinating cell fractions across layers of the hierarchical tree, cellular fraction information is passed up and down the tree, which helps correct estimation biases by pooling information across related cell types. The flexible hierarchical tree structure also enables estimating rare cell fractions by splitting the tree to higher resolutions. Through simulations and real data applications with the ground truth of measured cellular fractions, we demonstrate that HiDecon significantly outperforms existing methods and accurately estimates cellular fractions.