D
David Gennert
Researcher at Stanford University
Publications - 22
Citations - 10580
David Gennert is an academic researcher from Stanford University. The author has contributed to research in topics: T cell & Single-cell analysis. The author has an hindex of 15, co-authored 22 publications receiving 7541 citations. Previous affiliations of David Gennert include Broad Institute.
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Journal ArticleDOI
Spatial reconstruction of single-cell gene expression data
TL;DR: Seurat is a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns, and correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups.
Journal ArticleDOI
Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells
Alex K. Shalek,Rahul Satija,Xian Adiconis,Rona S. Gertner,Jellert T. Gaublomme,Raktima Raychowdhury,Schraga Schwartz,Nir Yosef,Christine M. Malboeuf,Diana Lu,John J. Trombetta,David Gennert,Andreas Gnirke,Alon Goren,Alon Goren,Nir Hacohen,Nir Hacohen,Joshua Z. Levin,Hongkun Park,Hongkun Park,Aviv Regev,Aviv Regev +21 more
TL;DR: The authors used single-cell RNA-Seq to investigate heterogeneity in the response of bone marrow derived dendritic cells (BMDCs) to lipopolysaccharide (LPS) and found extensive, and previously unobserved, bimodal variation in mRNA abundance and splicing patterns.
Journal ArticleDOI
Single-cell RNA-seq reveals dynamic paracrine control of cellular variation
Alex K. Shalek,Rahul Satija,Joe Shuga,John J. Trombetta,David Gennert,Diana Lu,Peilin Chen,Rona S. Gertner,Jellert T. Gaublomme,Nir Yosef,Schraga Schwartz,Brian Fowler,Suzanne Weaver,Jing Wang,Xiaohui Wang,Ruihua Ding,Raktima Raychowdhury,Nir Friedman,Nir Hacohen,Hongkun Park,Andrew May,Aviv Regev +21 more
TL;DR: This study highlights the importance of cell-to-cell communication in controlling cellular heterogeneity and reveals general strategies that multicellular populations can use to establish complex dynamic responses.
Spatial reconstruction of single-cell gene expression data
TL;DR: Seurat is a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns, and it correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups.
Journal ArticleDOI
Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma
Itay Tirosh,Andrew S. Venteicher,Andrew S. Venteicher,Christine Hebert,Christine Hebert,Leah E. Escalante,Leah E. Escalante,Anoop P. Patel,Keren Yizhak,Keren Yizhak,Jonathan M. Fisher,Christopher Rodman,Christopher Mount,Mariella G. Filbin,Mariella G. Filbin,Cyril Neftel,Cyril Neftel,Niyati Desai,Jackson Nyman,Benjamin Izar,Christina C. Luo,Joshua M. Francis,Joshua M. Francis,Aanand A. Patel,Maristela L. Onozato,Nicolo Riggi,Kenneth J. Livak,David Gennert,Rahul Satija,Brian V. Nahed,William T. Curry,Robert L. Martuza,Ravindra Mylvaganam,A. John Iafrate,Matthew P. Frosch,Todd R. Golub,Todd R. Golub,Todd R. Golub,Miguel Rivera,Miguel Rivera,Gad Getz,Gad Getz,Orit Rozenblatt-Rosen,Daniel P. Cahill,Michelle Monje,Bradley E. Bernstein,Bradley E. Bernstein,David N. Louis,Aviv Regev,Aviv Regev,Mario L. Suvà,Mario L. Suvà +51 more
TL;DR: In this paper, the authors profile 4,347 single cells from six IDH1 or IDH2 mutant human oligodendrogliomas by RNA sequencing and reconstruct their developmental programs from genome-wide expression signatures.