Charlotte Soneson

Molecular Biology · 2013–2020

A researcher in the Science corpus: 12 papers; trusts 58 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 375 citations into it; #1,028 of 17,773 by trust here, #4,731 by citations
• Cancer Research — 78 citations into it; #320 of 1,780 by trust here, #999 by citations
• Genetics — 45 citations into it
• Statistics and Probability — 35 citations into it
• Artificial Intelligence — 22 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Management Science and Operations Research — 11 citations into it
• Representation & Self-Supervised Learning — 10 citations into it

Whom Charlotte Soneson trusts overall

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

1. Mark D. Robinson
2. Gordon K. Smyth
3. Robert Tibshirani
4. Davis J. McCarthy
5. Jun Li
6. John C. Marioni
7. Thomas J. Hardcastle
8. Yunshun Chen
9. Håvard Rue
10. Simon Anders

Who trusts Charlotte Soneson overall

1. K. Van den Berge
2. Hugo Varet
3. Yunshun Chen
4. Malte D. Luecken
5. N. Davidson
6. Paul J. McMurdie
7. Michael I. Love
8. Xiaobei Zhou
9. Gabriel E. Hoffman
10. Fatemeh Seyednasrollah

Papers from Charlotte Soneson's perspective

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

1. A scaling normalization method for differential expression analysis of RNA-seq data (Genome Biology)
2. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data (Bioinform.)
3. Controlling the false discovery rate: a practical and powerful approach to multiple testing
4. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome (Genome Biology)
5. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments (BMC Bioinformatics)
6. Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (Statistical Applications in Genetics and Molecular Biology)
7. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. (Biostatistics)
8. Moderated statistical tests for assessing differences in tag abundance (Bioinform.)
9. Detecting differential usage of exons from RNA-seq data (Genome Research)
10. Understanding mechanisms underlying human gene expression variation with RNA sequencing (Nature)

Their papers

• A comparison of methods for differential expression analysis of RNA-seq data (2013) — 891 citations
• Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences (2015) — 3,582 citations
• Batch Effect Confounding Leads to Strong Bias in Performance Estimates Obtained by Cross-Validation (2014) — 82 citations
• muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data (2020) — 376 citations
• Bias, robustness and scalability in single-cell differential expression analysis (2018) — 666 citations
• Orchestrating single-cell analysis with Bioconductor (2019) — 840 citations
• A systematic performance evaluation of clustering methods for single-cell RNA-seq data (2018) — 331 citations
• Tximeta: Reference sequence checksums for provenance identification in RNA-seq (2019) — 305 citations
• RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018) — 116 citations
• The Consensus Molecular Subtypes of Colorectal Cancer (2015) — 4,640 citations
• The SIB Swiss Institute of Bioinformatics’ resources: focus on curated databases (2015) — 60 citations
• Distal and proximal colon cancers differ in terms of molecular, pathological, and clinical features. (2014) — 603 citations