J
Joshua D. Welch
Researcher at University of Michigan
Publications - 62
Citations - 3819
Joshua D. Welch is an academic researcher from University of Michigan. The author has contributed to research in topics: Biology & Gene. The author has an hindex of 20, co-authored 50 publications receiving 2081 citations. Previous affiliations of Joshua D. Welch include Broad Institute & University of North Carolina at Chapel Hill.
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Jointly Defining Cell Types from Multiple Single-Cell Datasets Using LIGER
TL;DR: This protocol describes how to jointly define cell types from single-cell RNA-seq and single-nucleus ATAC-seq data, but similar steps apply across a wide range of other settings and data types, including cross-species analysis, single-Nucleus DNA methylation, and spatial transcriptomics.
Posted ContentDOI
MorphNet Predicts Cell Morphology from Single-Cell Gene Expression
Hojae Lee,Joshua D. Welch +1 more
TL;DR: The approach leverages paired morphology and molecular data to train a neural network that can predict nuclear or whole-cell morphology from gene expression, and provides a web server that allows users to predict neuron morphologies for their own scRNA-seq data.
Posted ContentDOI
Slide-seq: A Scalable Technology for Measuring Genome-Wide Expression at High Spatial Resolution
Samuel G. Rodriques,Robert R. Stickels,Robert R. Stickels,Aleksandrina Goeva,Carly A. Martin,Evan Murray,Charles R. Vanderburg,Joshua D. Welch,Linlin M. Chen,Fei Chen,Evan Z. Macosko,Evan Z. Macosko +11 more
TL;DR: Slide-seq is introduced, a highly scalable method that enables facile generation of large volumes of unbiased spatial transcriptomes with 10 µm spatial resolution, comparable to the size of individual cells, and will accelerate biological discovery by enabling routine, high-resolution spatial mapping of gene expression.
Computational Methods for Inferring Transcriptome Dynamics
TL;DR: This dissertation presents computational methods to address some of the challenges involved in inferring dynamic transcriptome changes and focuses two types of challenges: discovering important biological variation within a population of single cells and robustly extracting information from sequencing reads.
Posted ContentDOI
Single-Cell Analysis of the 3D Topologies of Genomic Loci Using Genome Architecture Mapping
Lonnie R. Welch,Catherine Baugher,Yingnan Zhang,Trenton Davis,William F. Marzluff,Joshua D. Welch,Ana Pombo +6 more
TL;DR: Using GAM data from mouse embryonic stem cells, new discoveries are made about the structure of the major mammalian histone gene locus, which is incorporated into the Histone Locus Body (HLB), including structural fluctuations and putative causal molecular mechanisms.