The tidyomics ecosystem: enhancing omic data analyses.

The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.

Development of an Electronic Decision Aid Tool to Facilitate Mainstream Genetic Testing in Ovarian Cancer Patients.

BACKGROUND: Multigene panel testing is an important component of cancer treatment plans and risk assessment, but there are many different panel options and choosing the most appropriate panel can be challenging for health care providers and patients. Electronic tools have been proposed to help patients make informed decisions about which gene panel to choose by considering their preferences and priorities. MATERIALS AND METHODS: An electronic decision aid (DA) tool was developed in line with the International Patient Decision Aids Standards collaboration. The multidisciplinary project team collaborated with an external health care communications agency and the MGH Cancer Center Patient and Family Advisory Council (PFAC) to develop the DA. Surveys of genetic counselors and patients were used to scope the content, and alpha testing was used to refine the design and content. RESULTS: Surveys of genetic counselors (n = 12) and patients (n = 228) identified common themes in discussing panel size and strategies for helping patients decide between panels and in identifying confusing terms for patients and distribution of patients' choices. The DA, organized into 2 major sections, provides educational text, graphics, and videos to guide patients through the decision-making process. Alpha testing feedback from the PFAC (n = 4), genetic counselors (n = 3) and a group of lay people (n = 8) identified areas to improve navigation, simplify wording, and improve layout. CONCLUSION: The DA developed in this study has the potential to facilitate informed decision-making by patients regarding cancer genetic testing. The distinctive feature of this DA is that it addresses the specific question of which multigene panel may be most suitable for the patient. Its acceptability and effectiveness will be evaluated in future studies.

Treatment of Relapsing HPV Diseases by Restored Function of Natural Killer Cells.

Human papillomavirus (HPV) infections underlie a wide spectrum of both benign and malignant epithelial diseases. In this report, we describe the case of a young man who had encephalitis caused by herpes simplex virus during adolescence and currently presented with multiple recurrent skin and mucosal lesions caused by HPV. The patient was found to have a pathogenic germline mutation in the X-linked interleukin-2 receptor subunit gamma gene (IL2RG), which was somatically reverted in T cells but not in natural killer (NK) cells. Allogeneic hematopoietic-cell transplantation led to restoration of NK cytotoxicity, with normalization of the skin microbiome and persistent remission of all HPV-related diseases. NK cytotoxicity appears to play a role in containing HPV colonization and the ensuing HPV-related hyperplastic or dysplastic lesions. (Funded by the National Institutes of Health and the Herbert Irving Comprehensive Cancer Center Flow Cytometry Shared Resources.).

Quantification and statistical modeling of droplet-based single-nucleus RNA-sequencing data.

In complex tissues containing cells that are difficult to dissociate, single-nucleus RNA-sequencing (snRNA-seq) has become the preferred experimental technology over single-cell RNA-sequencing (scRNA-seq) to measure gene expression. To accurately model these data in downstream analyses, previous work has shown that droplet-based scRNA-seq data are not zero-inflated, but whether droplet-based snRNA-seq data follow the same probability distributions has not been systematically evaluated. Using pseudonegative control data from nuclei in mouse cortex sequenced with the 10x Genomics Chromium system and mouse kidney sequenced with the DropSeq system, we found that droplet-based snRNA-seq data follow a negative binomial distribution, suggesting that parametric statistical models applied to scRNA-seq are transferable to snRNA-seq. Furthermore, we found that the quantification choices in adapting quantification mapping strategies from scRNA-seq to snRNA-seq can play a significant role in downstream analyses and biological interpretation. In particular, reference transcriptomes that do not include intronic regions result in significantly smaller library sizes and incongruous cell type classifications. We also confirmed the presence of a gene length bias in snRNA-seq data, which we show is present in both exonic and intronic reads, and investigate potential causes for the bias.

Activity-regulated gene expression across cell types of the mouse hippocampus.

Activity-regulated gene (ARG) expression patterns in the hippocampus (HPC) regulate synaptic plasticity, learning, and memory, and are linked to both risk and treatment responses for many neuropsychiatric disorders. The HPC contains discrete classes of neurons with specialized functions, but cell type-specific activity-regulated transcriptional programs are not well characterized. Here, we used single-nucleus RNA-sequencing (snRNA-seq) in a mouse model of acute electroconvulsive seizures (ECS) to identify cell type-specific molecular signatures associated with induced activity in HPC neurons. We used unsupervised clustering and a priori marker genes to computationally annotate 15,990 high-quality HPC neuronal nuclei from N = 4 mice across all major HPC subregions and neuron types. Activity-induced transcriptomic responses were divergent across neuron populations, with dentate granule cells being particularly responsive to activity. Differential expression analysis identified both upregulated and downregulated cell type-specific gene sets in neurons following ECS. Within these gene sets, we identified enrichment of pathways associated with varying biological processes such as synapse organization, cellular signaling, and transcriptional regulation. Finally, we used matrix factorization to reveal continuous gene expression patterns differentially associated with cell type, ECS, and biological processes. This work provides a rich resource for interrogating activity-regulated transcriptional responses in HPC neurons at single-nuclei resolution in the context of ECS, which can provide biological insight into the roles of defined neuronal subtypes in HPC function.

A scalable and unbiased discordance metric with H.

A standard unsupervised analysis is to cluster observations into discrete groups using a dissimilarity measure, such as Euclidean distance. If there does not exist a ground-truth label for each observation necessary for external validity metrics, then internal validity metrics, such as the tightness or separation of the clusters, are often used. However, the interpretation of these internal metrics can be problematic when using different dissimilarity measures as they have different magnitudes and ranges of values that they span. To address this problem, previous work introduced the "scale-agnostic" $G_{+}$ discordance metric; however, this internal metric is slow to calculate for large data. Furthermore, in the setting of unsupervised clustering with $k$ groups, we show that $G_{+}$ varies as a function of the proportion of observations assigned to each of the groups (or clusters), referred to as the group balance, which is an undesirable property. To address this problem, we propose a modification of $G_{+}$, referred to as $H_{+}$, and demonstrate that $H_{+}$ does not vary as a function of group balance using a simulation study and with public single-cell RNA-sequencing data. Finally, we provide scalable approaches to estimate $H_{+}$, which are available in the $\mathtt{fasthplus}$ R package.

Orchestrating single-cell analysis with Bioconductor.

Recent technological advancements have enabled the profiling of a large number of genome-wide features in individual cells. However, single-cell data present unique challenges that require the development of specialized methods and software infrastructure to successfully derive biological insights. The Bioconductor project has rapidly grown to meet these demands, hosting community-developed open-source software distributed as R packages. Featuring state-of-the-art computational methods, standardized data infrastructure and interactive data visualization tools, we present an overview and online book (https://osca.bioconductor.org) of single-cell methods for prospective users.

Publisher Correction: Orchestrating single-cell analysis with Bioconductor.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

SpatialExperiment: infrastructure for spatially-resolved transcriptomics data in R using Bioconductor.

SUMMARY: SpatialExperiment is a new data infrastructure for storing and accessing spatially-resolved transcriptomics data, implemented within the R/Bioconductor framework, which provides advantages of modularity, interoperability, standardized operations and comprehensive documentation. Here, we demonstrate the structure and user interface with examples from the 10x Genomics Visium and seqFISH platforms, and provide access to example datasets and visualization tools in the STexampleData, TENxVisiumData and ggspavis packages. AVAILABILITY AND IMPLEMENTATION: The SpatialExperiment, STexampleData, TENxVisiumData and ggspavis packages are available from Bioconductor. The package versions described in this manuscript are available in Bioconductor version 3.15 onwards. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Addressing the mean-correlation relationship in co-expression analysis.

Estimates of correlation between pairs of genes in co-expression analysis are commonly used to construct networks among genes using gene expression data. As previously noted, the distribution of such correlations depends on the observed expression level of the involved genes, which we refer to this as a mean-correlation relationship in RNA-seq data, both bulk and single-cell. This dependence introduces an unwanted technical bias in co-expression analysis whereby highly expressed genes are more likely to be highly correlated. Such a relationship is not observed in protein-protein interaction data, suggesting that it is not reflecting biology. Ignoring this bias can lead to missing potentially biologically relevant pairs of genes that are lowly expressed, such as transcription factors. To address this problem, we introduce spatial quantile normalization (SpQN), a method for normalizing local distributions in a correlation matrix. We show that spatial quantile normalization removes the mean-correlation relationship and corrects the expression bias in network reconstruction.

A post-transcriptional regulon controlled by TtpA, the single tristetraprolin family member expressed in Dictyostelium discoideum.

Post-transcriptional processes mediated by mRNA binding proteins represent important control points in gene expression. In eukaryotes, mRNAs containing specific AU-rich motifs are regulated by binding of tristetraprolin (TTP) family tandem zinc finger proteins, which promote mRNA deadenylation and decay, partly through interaction of a conserved C-terminal CNOT1 binding (CNB) domain with CCR4-NOT protein complexes. The social ameba Dictyostelium discoideum shared a common ancestor with humans more than a billion years ago, and expresses only one TTP family protein, TtpA, in contrast to three members expressed in humans. Evaluation of ttpA null-mutants identified six transcripts that were consistently upregulated compared to WT during growth and early development. The 3'-untranslated regions (3'-UTRs) of all six 'TtpA-target' mRNAs contained multiple TTP binding motifs (UUAUUUAUU), and one 3'-UTR conferred TtpA post-transcriptional stability regulation to a heterologous mRNA that was abrogated by mutations in the core TTP-binding motifs. All six target transcripts were upregulated to similar extents in a C-terminal truncation mutant, in contrast to less severe effects of analogous mutants in mice. All six target transcripts encoded probable membrane proteins. In Dictyostelium, TtpA may control an 'RNA regulon', where a single RNA binding protein, TtpA, post-transcriptionally co-regulates expression of several functionally related proteins.

spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data.

BACKGROUND: Spatially-resolved transcriptomics has now enabled the quantification of high-throughput and transcriptome-wide gene expression in intact tissue while also retaining the spatial coordinates. Incorporating the precise spatial mapping of gene activity advances our understanding of intact tissue-specific biological processes. In order to interpret these novel spatial data types, interactive visualization tools are necessary. RESULTS: We describe spatialLIBD, an R/Bioconductor package to interactively explore spatially-resolved transcriptomics data generated with the 10x Genomics Visium platform. The package contains functions to interactively access, visualize, and inspect the observed spatial gene expression data and data-driven clusters identified with supervised or unsupervised analyses, either on the user's computer or through a web application. CONCLUSIONS: spatialLIBD is available at https://bioconductor.org/packages/spatialLIBD . It is fully compatible with SpatialExperiment and the Bioconductor ecosystem. Its functionality facilitates analyzing and interactively exploring spatially-resolved data from the Visium platform.

Genetic testing to gain diagnostic clarity in neurofibromatosis type 2 and schwannomatosis.

Neurofibromatosis type 2 (NF2) and schwannomatosis (SWN) have distinct genetic etiologies but overlapping phenotypes. Genetic testing may be required for accurate diagnosis, which is critical for determining prognosis, screening recommendations, and treatment options. Our study aimed to compare the efficacy of germline-only versus paired (germline and tumor) genetic testing for clarifying the diagnosis in patients with features of NF2 and SWN. We performed a retrospective chart review of patients referred for NF2/SWN genetic testing at Massachusetts General Hospital from 2015 to 2020. Logistic regression analysis was performed to assess factors associated with diagnostic clarity. Overall, paired testing had 8.5 times greater odds of providing diagnostic clarity than germline-only testing (p < 0.01). Among patients who underwent paired testing, those who had analysis of two or more tumors had the greatest likelihood of gaining diagnostic clarity, with odds 13 times greater than patients who underwent germline-only testing (p < 0.01). Paired testing with analysis of one tumor significantly increased the odds of diagnostic clarity over germline-only testing by a factor of 6.5 (p < 0.01). These results have implications for genetic testing strategies and counseling patients about genetic testing utility. They also support the routine use of testing in individuals with suspected NF2 or SWN and improved insurance coverage for paired testing within this population.

mbkmeans: Fast clustering for single cell data using mini-batch k-means.

Single-cell RNA-Sequencing (scRNA-seq) is the most widely used high-throughput technology to measure genome-wide gene expression at the single-cell level. One of the most common analyses of scRNA-seq data detects distinct subpopulations of cells through the use of unsupervised clustering algorithms. However, recent advances in scRNA-seq technologies result in current datasets ranging from thousands to millions of cells. Popular clustering algorithms, such as k-means, typically require the data to be loaded entirely into memory and therefore can be slow or impossible to run with large datasets. To address this problem, we developed the mbkmeans R/Bioconductor package, an open-source implementation of the mini-batch k-means algorithm. Our package allows for on-disk data representations, such as the common HDF5 file format widely used for single-cell data, that do not require all the data to be loaded into memory at one time. We demonstrate the performance of the mbkmeans package using large datasets, including one with 1.3 million cells. We also highlight and compare the computing performance of mbkmeans against the standard implementation of k-means and other popular single-cell clustering methods. Our software package is available in Bioconductor at https://bioconductor.org/packages/mbkmeans.

miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data.

Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a 'low-quality' cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA (mtDNA) encoded genes and (ii) if a small number of genes are detected. Current best practices use these QC metrics independently with either arbitrary, uniform thresholds (e.g. 5%) or biological context-dependent (e.g. species) thresholds, and fail to jointly model these metrics in a data-driven manner. Current practices are often overly stringent and especially untenable on certain types of tissues, such as archived tumor tissues, or tissues associated with mitochondrial function, such as kidney tissue [1]. We propose a data-driven QC metric (miQC) that jointly models both the proportion of reads mapping to mtDNA genes and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. We demonstrate how our QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses. Our software package is available at https://bioconductor.org/packages/miQC.

Differential expression of single-cell RNA-seq data using Tweedie models.

The performance of computational methods and software to identify differentially expressed features in single-cell RNA-sequencing (scRNA-seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA-seq expression features. To model the technological variability in cross-platform scRNA-seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA-seq expression profiles across experimental platforms induced by platform- and gene-specific statistical properties such as heavy tails, sparsity, and gene expression distributions. We also propose a zero-inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero-inflated scRNA-seq data with excessive zero counts. Using both synthetic and published plate- and droplet-based scRNA-seq datasets, we perform a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state-of-the-art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open-source software (R/Bioconductor package) is available at https://github.com/himelmallick/Tweedieverse.

Toward a better understanding of the experience of patients with moderate penetrance breast cancer gene pathogenic/likely pathogenic variants: A focus on ATM and CHEK2.

This study explored the experiences of patients with pathogenic or likely pathogenic variants in the moderate penetrance breast cancer genes, ATM and CHEK2. There were 139 eligible female patients who received genetic counseling at the Massachusetts General Hospital Center for Cancer Risk Assessment (MGH CCRA) from 2014 to 2018. They were sent surveys assessing their understanding of the clinical significance of their genetic test results, adherence to medical management recommendations, dissemination of genetic test results to relatives, and informational resource needs. In total, 66 surveys were returned with a response rate of 47.5%. Most participants reported understanding the clinical implications of their genetic test results and adhering to medical management recommendations. Although 20.3% found it upsetting, nearly all participants shared their genetic test results with relatives. When asked about resource needs, 54.5% reported seeking out additional resources. Our ATM/CHEK2 sample appears to have a good understanding of the personal and familial implications of their genetic test results but may benefit from additional resources. It is unclear whether similar results would be found in patients who do not receive genetic counseling from a board-certified genetic counselor, and this should be examined. This study is one of the first to assess the experiences and needs of the moderate risk population.

Effectiveness and cost effectiveness of palliative care interventions in people with chronic heart failure and their caregivers: a systematic review.

BACKGROUND: Chronic heart failure is a common condition, and its prevalence is expected to rise significantly over the next two decades. Research demonstrates the increasing multidimensional needs of patients and caregivers. However, access to palliative care services for this population has remained poor. This systematic review was to provide an evidence synthesis of the effectiveness and cost-effectiveness of palliative care interventions for people with chronic heart failure and their caregivers. METHODS: Relevant publications were identified via electronic searches of MEDLINE, Embase, PsychInfo, CINAHL, CENTRAL and HMIC from inception to June 2019. Grey literature databases, reference list, and citations of key review articles were also searched. Quality was assessed using the Revised Cochrane Risk of Bias Tool. RESULTS: Of the 2083 records, 18 studies were identified including 17 having randomised controlled trial (RCT) designs and one mixed methods study with an RCT component. There was significant heterogeneity in study settings, control groups, interventions delivered, and outcome measures used. The most commonly assessed outcome measures were functional status (n = 9), psychological symptoms (n = 9), disease-specific quality of life (n = 9), and physical symptom control (n = 8). The outcome measures with the greatest evidence for benefit included general and disease-specific quality of life, psychological symptom control, satisfaction with care, physical symptom control, medical utilisation, and caregiver burden. Moreover, the methodological quality of these studies was mixed, with only four having an overall low risk of bias and the remaining studies either demonstrating high risk of bias (n = 10) or showing some concerns (n = 4) due to small sample sizes and poor retention. Only two studies reported on economic costs. Both found statistically significant results showing the intervention group to be more cost effective than the control group, but the quality of both studies was at high risk of bias. CONCLUSIONS: This review supports the role of palliative care interventions in patients with chronic heart failure and their caregivers across various outcomes, particularly quality of life and psychological wellbeing. Due to the highly heterogeneous nature of palliative care interventions, it is not possible to provide definitive recommendations as to what guise palliative care interventions should take to best support the complex care of this population. Considerable future research, particularly focusing on quality of care after death and the caregiver population, is warranted.

Reproducible Research: A Retrospective.

Advances in computing technology have spurred two extraordinary phenomena in science: large-scale and high-throughput data collection coupled with the creation and implementation of complex statistical algorithms for data analysis. These two phenomena have brought about tremendous advances in scientific discovery but have raised two serious concerns. The complexity of modern data analyses raises questions about the reproducibility of the analyses, meaning the ability of independent analysts to recreate the results claimed by the original authors using the original data and analysis techniques. Reproducibility is typically thwarted by a lack of availability of the original data and computer code. A more general concern is the replicability of scientific findings, which concerns the frequency with which scientific claims are confirmed by completely independent investigations. Although reproducibility and replicability are related, they focus on different aspects of scientific progress. In this review, we discuss the origins of reproducible research, characterize the current status of reproducibility in public health research, and connect reproducibility to current concerns about the replicability of scientific findings. Finally, we describe a path forward for improving both the reproducibility and replicability of public health research in the future.

Prospective Study of a Novel, Radiation-Free, Reduced-Intensity Bone Marrow Transplantation Platform for Primary Immunodeficiency Diseases.

Allogeneic blood or marrow transplantation (BMT) is a potentially curative therapy for patients with primary immunodeficiency (PID). Safe and effective reduced-intensity conditioning (RIC) approaches that are associated with low toxicity, use alternative donors, and afford good immune reconstitution are needed to advance the field. Twenty PID patients, ranging in age from 4 to 58 years, were treated on a prospective clinical trial of a novel, radiation-free and serotherapy-free RIC, T-cell-replete BMT approach using pentostatin, low-dose cyclophosphamide, and busulfan for conditioning with post-transplantation cyclophosphamide-based graft-versus-host-disease (GVHD) prophylaxis. This was a high-risk cohort with a median hematopoietic cell transplantation comorbidity index of 3. With median follow-up of survivors of 1.9 years, 1-year overall survival was 90% and grade III to IV acute GVHD-free, graft-failure-free survival was 80% at day +180. Graft failure incidence was 10%. Split chimerism was frequently observed at early post-BMT timepoints, with a lower percentage of donor T cells, which gradually increased by day +60. The cumulative incidences of grade II to IV and grade III to IV acute GVHD (aGVHD) were 15% and 5%, respectively. All aGVHD was steroid responsive. No patients developed chronic GVHD. Few significant organ toxicities were observed. Evidence of phenotype reversal was observed for all engrafted patients, even those with significantly mixed chimerism (n = 2) or with unknown underlying genetic defect (n = 3). All 6 patients with pre-BMT malignancies or lymphoproliferative disorders remain in remission. Most patients have discontinued immunoglobulin replacement. All survivors are off immunosuppression for GVHD prophylaxis or treatment. This novel RIC BMT approach for patients with PID has yielded promising results, even for high-risk patients.