Large-scale estimation of random graph models with local dependence.

A class of random graph models is considered, combining features of exponential-family models and latent structure models, with the goal of retaining the strengths of both of them while reducing the weaknesses of each of them. An open problem is how to estimate such models from large networks. A novel approach to large-scale estimation is proposed, taking advantage of the local structure of such models for the purpose of local computing. The main idea is that random graphs with local dependence can be decomposed into subgraphs, which enables parallel computing on subgraphs and suggests a two-step estimation approach. The first step estimates the local structure underlying random graphs. The second step estimates parameters given the estimated local structure of random graphs. Both steps can be implemented in parallel, which enables large-scale estimation. The advantages of the two-step estimation approach are demonstrated by simulation studies with up to 10,000 nodes and an application to a large Amazon product recommendation network with more than 10,000 products.

Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes.

BACKGROUND: RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq, scnRNA-seq for short), can help characterize the composition of tissues and reveal cells that influence key functions in both healthy and disease tissues. However, the use of these technologies is operationally challenging because of high costs and stringent sample-collection requirements. Computational deconvolution methods that infer the composition of bulk-profiled samples using scnRNA-seq-characterized cell types can broaden scnRNA-seq applications, but their effectiveness remains controversial. RESULTS: We produced the first systematic evaluation of deconvolution methods on datasets with either known or scnRNA-seq-estimated compositions. Our analyses revealed biases that are common to scnRNA-seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-seq and scnRNA-seq profiles can help improve the accuracy of both scnRNA-seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), which combines RNA-seq transformation and dampened weighted least-squares deconvolution approaches, consistently outperformed other methods in predicting the composition of cell mixtures and tissue samples. CONCLUSIONS: We showed that analysis of concurrent RNA-seq and scnRNA-seq profiles with SQUID can produce accurate cell-type abundance estimates and that this accuracy improvement was necessary for identifying outcomes-predictive cancer cell subclones in pediatric acute myeloid leukemia and neuroblastoma datasets. These results suggest that deconvolution accuracy improvements are vital to enabling its applications in the life sciences.

Chronic infection drives Dnmt3a-loss-of-function clonal hematopoiesis via IFNγ signaling.

Age-related clonal hematopoiesis (CH) is a risk factor for malignancy, cardiovascular disease, and all-cause mortality. Somatic mutations in DNMT3A are drivers of CH, but decades may elapse between the acquisition of a mutation and CH, suggesting that environmental factors contribute to clonal expansion. We tested whether infection provides selective pressure favoring the expansion of Dnmt3a mutant hematopoietic stem cells (HSCs) in mouse chimeras. We created Dnmt3a-mosaic mice by transplanting Dnmt3a-/- and WT HSCs into WT mice and observed the substantial expansion of Dnmt3a-/- HSCs during chronic mycobacterial infection. Injection of recombinant IFNγ alone was sufficient to phenocopy CH by Dnmt3a-/- HSCs upon infection. Transcriptional and epigenetic profiling and functional studies indicate reduced differentiation associated with widespread methylation alterations, and reduced secondary stress-induced apoptosis accounts for Dnmt3a-/- clonal expansion during infection. DNMT3A mutant human HSCs similarly exhibit defective IFNγ-induced differentiation. We thus demonstrate that IFNγ signaling induced during chronic infection can drive DNMT3A-loss-of-function CH.