Malte D. Luecken
Molecular Biology · 2019–2023
A researcher in the Science corpus: 4 papers; trusts 70 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 269 citations into it; #11,563 of 17,773 by trust here, #9,030 by citations
- Biophysics — 80 citations into it; #799 of 1,196 by trust here, #469 by citations
- Cancer Research — 48 citations into it
- Artificial Intelligence — 22 citations into it
- Computational Biology & Bioinformatics — 19 citations into it
Whom Malte D. Luecken trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- F. Alexander Wolf
- Mark D. Robinson
- Laleh Haghverdi
- Aaron T. L. Lun
- Fabian J Theis
- Davide Risso
- Davis J. McCarthy
- Florian Buettner
- Mark Newman
- Charlotte Soneson
Who trusts Malte D. Luecken overall
- R. Argelaguet
- Lukas Heumos
- Anna C. Schaar
- Maren Büttner
- Damien Arnol
- Mohammad Lotfollahi
- Christopher Lance
Papers from Malte D. Luecken's perspective
Ordered by how strongly the researchers Malte D. Luecken trusts endorse each paper. Same corpus, a different order for every researcher.
- Droplet barcoding for single cell transcriptomics applied to embryonic stem cells (Cell)
- ImageNet classification with deep convolutional neural networks
- MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data (Genome Biology)
- Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B cell Development (Cell)
- Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells (Nature Biotechnology)
- Bayesian approach to single-cell differential expression analysis (Nature Methods)
- Controlling the false discovery rate: a practical and powerful approach to multiple testing
- Visualizing Data using t-SNE
- SCANPY: large-scale single-cell gene expression data analysis
- Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis
Their papers
- Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019) — 1,950 citations
- Benchmarking atlas-level data integration in single-cell genomics (2020) — 1,189 citations
- Mapping single-cell data to reference atlases by transfer learning (2021) — 497 citations
- Best practices for single-cell analysis across modalities (2023) — 828 citations