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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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A Wnt-mediated transformation of the bone marrow stromal cell identity orchestrates skeletal regeneration.

TL;DR: It is shown that quiescent CXCL12-expressing BMSCs can convert into a skeletal stem cell-like state, and differentiate into cortical bone osteoblasts only in response to injury.
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MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics.

TL;DR: MATCHER uses manifold alignment to infer single cell multi-omic profiles from transcriptomic and epigenetic measurements performed on different cells of the same type, and accurately predicts true single cell correlations between DNA methylation and gene expression without using known cell correspondences.
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A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex.

Zizhen Yao, +88 more
- 06 Oct 2021 - 
TL;DR: In this paper, a reference atlas of diverse neuronal and non-neuronal cell types in the mouse primary motor cortex is presented, including a population of excitatory neurons that resemble pyramidal cells in layer 4.
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Single-Cell Transcriptomic Analyses of Cell Fate Transitions during Human Cardiac Reprogramming.

TL;DR: A single-cell transcriptomic study of human cardiac (hiCM) reprograming that utilizes an analysis pipeline incorporating current data normalization methods, multiple trajectory prediction algorithms, and a cell fate index calculation to measure reprogramming progression is reported.
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Jointly defining cell types from multiple single-cell datasets using LIGER.

TL;DR: In this article, a step-by-step protocol for using linked inference of genomic experimental relationships (LIGER) to jointly define cell types from multiple single-cell datasets is presented.