Matthew E. Ritchie

Molecular Biology · 2007–2020

A researcher in the Science corpus: 6 papers; trusts 12 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

Whom Matthew E. Ritchie trusts overall

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

  1. Gordon K. Smyth
  2. Mark D. Robinson
  3. Carl N. Morris
  4. Y. H. Yang
  5. Sandrine Dudoit
  6. Yihui Xie
  7. Friedrich Leisch
  8. Terence P. Speed
  9. Yang Liao
  10. Bradley Efron

Who trusts Matthew E. Ritchie overall

  1. Gordon K. Smyth
  2. Yunshun Chen
  3. Aaron T. L. Lun
  4. Belinda Phipson
  5. Malte D. Luecken
  6. Brett K. Beaulieu-Jones
  7. Yang Liao
  8. G. Korotkevich
  9. M. van Iterson
  10. Aurina Arnatkeviciute

Papers from Matthew E. Ritchie's perspective

Ordered by how strongly the researchers Matthew E. Ritchie 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. On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data (J. Comput. Biol.)
  3. Controlling the false discovery rate: a practical and powerful approach to multiple testing
  4. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data (Bioinform.)
  5. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. (Nucleic Acids Research)
  6. Sweave: Dynamic Generation of Statistical Reports Using Literate Data Analysis (International Conference on Computational Statistics)
  7. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias (Bioinform.)
  8. Understanding mechanisms underlying human gene expression variation with RNA sequencing (Nature)
  9. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome (BMC Bioinformatics)
  10. HTSeq—a Python framework to work with high-throughput sequencing data (bioRxiv)

Their papers