Paul J. Hoffman
Molecular Biology · 2018–2022
A researcher in the Science corpus: 5 papers; trusts 9 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 204 citations into it; #5,325 of 17,773 by trust here, #1,360 by citations
- Artificial Intelligence — 12 citations into it
- Computer Vision and Pattern Recognition — 12 citations into it
Whom Paul J. Hoffman trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Rahul Satija
- Allan Aasbjerg Nielsen
- Toni Giorgino
- Robert Tibshirani
- Ludo Waltman
- Laleh Haghverdi
- Chen Xu
- James Baglama
- Daniela M. Witten
- Trevor J. Hastie
Who trusts Paul J. Hoffman overall
- Tim Stuart
- Zixuan Cang
- Malte D. Luecken
- Nicholas C Borcherding
- Matthew D. Young
- Yuqi Tan
- Hoa T M Tran
- Rahul Satija
- Brian L. Hie
- Christopher S. McGinnis
Papers from Paul J. Hoffman's perspective
Ordered by how strongly the researchers Paul J. Hoffman trusts endorse each paper. Same corpus, a different order for every researcher.
- Massively parallel single cell RNA-Seq for marker-free decomposition of tissues into cell types (Science)
- Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets
- Bayesian approach to single-cell differential expression analysis (Nature Methods)
- Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis
- Adult Mouse Cortical Cell Taxonomy by Single Cell Transcriptomics
- Ultrafast and memory-efficient alignment of short DNA sequences to the human genome (Genome Biology)
- Spatial reconstruction of single-cell gene expression
- Adjusting batch effects in microarray expression data using empirical Bayes methods. (Biostatistics)
- Relations Between Two Sets of Variates
- UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Their papers
- Comprehensive integration of single-cell data (2018) — 13,024 citations
- Integrating single-cell transcriptomic data across different conditions, technologies, and species (2018) — 10,784 citations
- Dictionary learning for integrative, multimodal, and massively scalable single-cell analysis (2022) — 3,442 citations
- Integrated analysis of multimodal single-cell data (2020) — 12,409 citations
- The GTEx Consortium atlas of genetic regulatory effects across human tissues (2019) — 4,770 citations