Robrecht Cannoodt

Representation & Self-Supervised Learning · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 1 researcher and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Representation & Self-Supervised Learning — 6 citations into it

Whom Robrecht Cannoodt trusts overall

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

1. Laleh Haghverdi

Who trusts Robrecht Cannoodt overall

1. Malte D. Luecken
2. Alexander Tong

Papers from Robrecht Cannoodt's perspective

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

1. Diffusion pseudotime robustly reconstructs lineage branching
2. Controlling the false discovery rate: a practical and powerful approach to multiple testing
3. Visualizing Data using t-SNE
4. Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors. (Cell)
5. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias (Bioinform.)
6. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
7. Recovering gene interactions from single-cell data using data diffusion (Cell)
8. Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data
9. Dimensionality reduction for visualizing single-cell data using UMAP
10. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics (BMC Genomics)

Their papers

• A comparison of single-cell trajectory inference methods (2019) — 1,398 citations
• Trajectory-based differential expression analysis for single-cell sequencing data (2019) — 661 citations
• A scalable SCENIC workflow for single-cell gene regulatory network analysis (2020) — 1,216 citations