Biology subjects

Love, M. I.

Publications and source records attributed to Love, M. I..

59 records · Page 4Linked to original sources

bioRxiv2018

Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences

In RNA-seq differential expression analysis, investigators aim to detect genes with changes in expression across conditions, despite technical and biological variability. A common task is to accurately estimate the effect size. When the counts are low or highly variable, the simple effect size estimate has high variance, leading to poor ranking of genes by effect size. Here we propose apeglm, which uses a heavy-tailed Cauchy prior distribution for effect sizes, resulting in lower bias than previous shrinkage estimators, while still reducing variance. apeglm is available at http://bioconductor.org/packages/apeglm, and can be used from within the DESeq2 software.

bioinformatics↗

bioRxiv2018

Observation weights to unlock bulk RNA-seq tools for zero inflation and single-cell applications

Dropout events in single-cell transcriptome sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial (ZINB) model, that identifies excess zero counts and generates gene and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.

bioinformatics↗

bioRxiv2017

SAFE-clustering: Single-cell Aggregated (From Ensemble) Clustering for Single-cell RNA-seq Data

MotivationAccurately clustering cell types from a mass of heterogeneous cells is a crucial first step for the analysis of single-cell RNA-seq (scRNA-Seq) data. Although several methods have been recently developed, they utilize different characteristics of data and yield varying results in terms of both the number of clusters and actual cluster assignments.\n\nResultsHere, we present SAFE-clustering, Single-cell Aggregated (From Ensemble) clustering, a flexible, accurate and robust method for clustering scRNA-Seq data. SAFE-clustering takes as input, results from multiple clustering methods, to build one consensus solution. SAFE-clustering currently embeds four state-of-the-art methods, SC3, CIDR, Seurat and t-SNE + k-means; and ensembles solutions from these four methods using three hypergraph-based partitioning algorithms. Extensive assessment across 12 datasets with the number of clusters ranging from 3 to 14, and the number of single cells ranging from 49 to 32,695 showcases the advantages of SAFE-clustering in terms of both cluster number (18.9 - 50.0% reduction in absolute deviation to the truth) and cluster assignment (on average 28.9% improvement, and up to 34.5% over the best of the four methods, measured by adjusted rand index). Moreover, SAFE-clustering is computationally efficient to accommodate large datasets, taking <10 minutes to process 28,733 cells.\n\nAvailability and implementationSAFE-clustering, including source codes and tutorial, is free available on the web at http://yunliweb.its.unc.edu/safe/.\n\nContactyunli@med.unc.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

zingeR: unlocking RNA-seq tools for zero-inflation and single cell applications

Dropout in single cell RNA-seq (scRNA-seq) applications causes many transcripts to go undetected. It induces excess zero counts, which leads to power issues in differential expression (DE) analysis and has triggered the development of bespoke scRNA-seq DE tools that cope with zero-inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce zingeR, a zero-inflated negative binomial model that identifies excess zero counts and generates observation weights to unlock bulk RNA-seq pipelines for zero-inflation, boosting performance in scRNA-seq differential expression analysis.

bioinformatics↗

bioRxiv2017

Static And Dynamic DNA Loops Form AP-1 Bound Activation Hubs During Macrophage Development

The three-dimensional arrangement of the human genome comprises a complex network of structural and regulatory chromatin loops important for coordinating changes in transcription during human development. To better understand the mechanisms underlying context-specific 3D chromatin structure and transcription during cellular differentiation, we generated comprehensive in situ Hi-C maps of DNA loops during human monocyte-to-macrophage differentiation. We demonstrate that dynamic looping events are regulatory rather than structural in nature and uncover widespread coordination of dynamic enhancer activity at preformed and acquired DNA loops. Enhancer-bound loop formation and enhancer-activation of preformed loops represent two distinct modes of regulation that together form multi-loop activation hubs at key macrophage genes. Activation hubs connect 3.4 enhancers per promoter and exhibit a strong enrichment for Activator Protein 1 (AP-1) binding events, suggesting multi-loop activation hubs driven by cell-type specific transcription factors may represent an important class of regulatory chromatin structures for the spatiotemporal control of transcription.\n\nHIGHLIGHTSO_LIHigh resolution and high sensitivity of loop detection via deeply sequenced in situ Hi-C experiments during monocyte to macrophage differentiation (> 10 billion total reads)\nC_LIO_LIMulti-loop interaction communities identified surrounding key macrophage genes.\nC_LIO_LIMulti-loop communities connect dynamic enhancers through both static and newly acquired DNA loops, forming hubs of activation\nC_LIO_LIMacrophage activation hubs are enriched for AP-1 bound long-range enhancer interactions, suggesting cell-type specific TFs drive changes in 3D structure and transcription through regulatory DNA loops\nC_LI

genomics↗