Correspondence analysis applied to microarray data
By K. Fellenberg, N. Hauser, B. Brors, A. Neutzner, Jörg D. Hoheisel, Martin Vingron. Proceedings of the National Academy of Sciences of the United States of America, 2001
359 citations recorded by the source corpus.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/41054d0224d15c10646241c4c27692d4d3e2cc66
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Citations in the corpus
- 9) TM4 Microarray Software Suite (2006, methodology): “A related method, correspondence analysis (Fellenberg et al., 2001; Culhane et al., 2002), maps both genes and samples onto the same set of axes, revealing associations between genes and experiments.”
- Archetypal analysis of diverse Pseudomonas aeruginosa transcriptomes reveals adaptation in cystic fibrosis airways (2013, methodology): “Table 1 List of samples Sample # Sample name Study Patient Clone Year Mucoid Mutator State(1) Medium(2) OD(3) [1,2] Huse_A1 Study 1 A “A” ~1983 No N/A Early SCFSM 0.”
- Identifying periodically expressed transcripts in microarray time series data (2008, methodology): “This distinguishes this method from other visualization strategies, such as geometric trajectories and correspondence analysis (Fellenberg et al., 2001) The g-statistic requires works on the level of the individual gene and hence requires more sample points per gene than the average periodogram.”