Shian Su
Molecular Biology · 2015–2020
A researcher in the Science corpus: 3 papers; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 82 citations into it
- Genetics — 18 citations into it
Whom Shian Su trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Papers from Shian Su's perspective
Ordered by how strongly the researchers Shian Su trusts endorse each paper. Same corpus, a different order for every researcher.
- Controlling the false discovery rate: a practical and powerful approach to multiple testing
- From RNA-seq reads to differential expression results (Genome Biology)
- baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data (BMC Bioinformatics)
- Bioconductor: open software development for computational biology and bioinformatics (Genome Biology)
- Testing significance relative to a fold-change threshold is a TREAT (Bioinform.)
- Normalization of RNA-seq data using factor analysis of control genes or samples (Nature Biotechnology)
- Biases in Illumina transcriptome sequencing caused by random hexamer priming (Nucleic Acids Research)
- The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity (Nature)
- Robustly detecting differential expression in RNA sequencing data using observation weights (Nucleic Acids Research)
- A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control consortium (Nature Biotechnology)
Their papers
- Opportunities and challenges in long-read sequencing data analysis (2020) — 1,305 citations
- RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR (2016) — 738 citations
- Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses (2015) — 502 citations