Allon M. Klein

Molecular Biology · 2007–2019

A researcher in the Science corpus: 15 papers; trusts 18 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 156 citations into it; #2,302 of 17,773 by trust here, #1,059 by citations
• Biophysics — 28 citations into it; #463 of 1,196 by trust here, #448 by citations
• Biomedical Engineering — 27 citations into it; #8,288 of 10,734 by trust here, #1,348 by citations
• Cancer Research — 25 citations into it; #726 of 1,780 by trust here, #745 by citations
• Computer Vision and Pattern Recognition — 15 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Statistical and Nonlinear Physics — 6 citations into it

Whom Allon M. Klein trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Caleb Weinreb
2. S. Picelli
3. Diego Jaitin
4. Jean-Christophe Baret
5. Peng Qiu
6. Yoav Benjamini
7. Samuel L. Wolock
8. L. V. D. van der Maaten
9. Chen Xu
10. Joshua E. Elias

Who trusts Allon M. Klein overall

1. Valentine Svensson
2. Samuel L. Wolock
3. Romain Lopez
4. Caleb Weinreb
5. F. Alexander Wolf
6. Tallulah S. Andrews
7. Saket Kumar Choudhary
8. Dominic Grün
9. Rhonda L. Bacher
10. Cole Trapnell

Papers from Allon M. Klein's perspective

Ordered by how strongly the researchers Allon M. Klein trusts endorse each paper. Same corpus, a different order for every researcher.

1. Massively parallel single cell RNA-Seq for marker-free decomposition of tissues into cell types (Science)
2. Droplet barcoding for single cell transcriptomics applied to embryonic stem cells (Cell)
3. Fast unfolding of communities in large networks
4. Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum
5. Smart-seq2 for sensitive full-length transcriptome profiling in single cells (Nature Methods)
6. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. (Cell Reports)
7. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia
8. Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors. (Cell)
9. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis
10. Clonal dynamics of native haematopoiesis (Nature)

Their papers

• Droplet barcoding for single cell transcriptomics applied to embryonic stem cells (2015) — 3,144 citations
• Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo (2018) — 748 citations
• Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data (2018) — 2,265 citations
• A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure (2016) — 1,337 citations
• Fundamental limits on dynamic inference from single-cell snapshots (2017) — 343 citations
• SPRING: a kinetic interface for visualizing high dimensional single-cell expression data (2017) — 305 citations
• Lineage tracing on transcriptional landscapes links state to fate during differentiation (2018) — 638 citations
• Intestinal crypt homeostasis results from neutral competition between symmetrically dividing Lgr5 stem cells. (2010) — 1,941 citations
• Intestinal Stem Cell Replacement Follows a Pattern of Neutral Drift (2010) — 655 citations
• Single-Cell Analysis of Experience-Dependent Transcriptomic States in Mouse Visual Cortex (2017) — 461 citations
• Single-cell barcoding and sequencing using droplet microfluidics (2016) — 659 citations
• Single cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species (2019) — 1,306 citations
• A single cell atlas of the tracheal epithelium reveals the CFTR-rich pulmonary ionocyte (2018) — 717 citations
• Clonal dynamics of native haematopoiesis (2014) — 751 citations
• A single type of progenitor cell maintains normal epidermis (2007) — 839 citations