K
Kyle Coleman
Researcher at University of Pennsylvania
Publications - 9
Citations - 247
Kyle Coleman is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Biology & Transcriptome. The author has an hindex of 3, co-authored 3 publications receiving 17 citations.
Papers
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SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network.
Jian Hu,Xiangjie Li,Kyle Coleman,Amelia Schroeder,Nan Ma,David J. Irwin,Edward B. Lee,Russell T. Shinohara,Mingyao Li +8 more
TL;DR: Analyzing five spatially resolved transcriptomics datasets using SpaGCN, it is shown it can detect genes with much more enriched spatial expression patterns than existing methods and are transferrable and can be utilized to study spatial variation of gene expression in other datasets.
Journal ArticleDOI
Statistical and machine learning methods for spatially resolved transcriptomics with histology.
TL;DR: In this paper, the authors focus on the statistical and machine learning aspects for spatially resolved transcriptomics (SRT) data analysis and discuss how spatial location and histology information can be integrated with gene expression to advance our understanding of the transcriptional complexity.
Posted ContentDOI
Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network
Jian Hu,Xiangjie Li,Kyle Coleman,Amelia Schroeder,David J. Irwin,Edward B. Lee,Russell T. Shinohara,Mingyao Li +7 more
TL;DR: SpaGCN as mentioned in this paper is a graph convolutional network approach that integrates gene expression, spatial location and histology in spatial transcriptomics data analysis, which can detect genes with much more enriched spatial expression patterns than existing methods.
Posted ContentDOI
Spatially resolved human kidney multi-omics single cell atlas highlights the key role of the fibrotic microenvironment in kidney disease progression
Amin Abedini,Ziyu Ma,Julia Frederick,Poonam Dhillon,Michael S. Balzer,Rojesh Shrestha,Hongbo Liu,Steven Vitale,Kishor Devalaraja-Narashimha,Paola Grandi,Tanmoy Bhattacharyya,Erding Hu,Steven S. Pullen,Carine M. Boustany-Kari,Paolo Guarnieri,Anil Karihaloo,Hanying Yan,Kyle Coleman,Matthew D. Palmer,Lea Sarov-Blat,Lori G Morton,Christopher A. Hunter,Mingyao Li,Katalin Susztak +23 more
TL;DR: A comprehensive spatially-resolved molecular roadmap for the human kidney and the fibrotic process is provided and the clinical utility of spatial transcriptomics is demonstrated.
Journal ArticleDOI
Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA
TL;DR: TESLA as mentioned in this paper integrates histological information with gene expression to annotate heterogeneous immune and tumor cells directly on the histology image, which represents a promising avenue for understanding the spatial architecture of the TME.