Christoph Hafemeister

Molecular Biology · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 37 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 114 citations into it; #2,341 of 17,773 by trust here, #2,226 by citations
• Cancer Research — 29 citations into it
• Biophysics — 25 citations into it
• Genetics — 10 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Christoph Hafemeister trusts overall

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

1. Gordon K. Smyth
2. Leland McInnes
3. Aaron T. L. Lun
4. Simon Anders
5. Rahul Satija
6. John Healy
7. Peter V. Kharchenko
8. Dominic Grün
9. Romain Lopez
10. Charity W. Law

Who trusts Christoph Hafemeister overall

1. Tim Stuart
2. Saket Kumar Choudhary
3. Rahul Satija
4. Chenling A. Xu
5. F. W. Townes
6. Romain Lopez
7. Cameron G. Williams
8. Lukas Heumos
9. Huan Fu
10. Malte D. Luecken

Papers from Christoph Hafemeister's perspective

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

1. Bayesian approach to single-cell differential expression analysis (Nature Methods)
2. Massively parallel digital transcriptional profiling of single cells
3. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data (Genome Biology)
4. Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (Statistical Applications in Genetics and Molecular Biology)
5. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
6. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome (Genome Biology)
7. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis
8. Single-cell mRNA quantification and differential analysis with Census (Nature Methods)
9. Computational and analytical challenges in single-cell transcriptomics (Nature reviews genetics)
10. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Their papers

• Comprehensive integration of single-cell data (2018) — 13,024 citations
• Large-scale simultaneous measurement of epitopes and transcriptomes in single cells (2017) — 2,234 citations
• Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) — 3,813 citations
• Developmental diversification of cortical inhibitory interneurons (2017) — 431 citations