Imputing Missing Data for Gene Expression Arrays

By Trevor J. Hastie, Robert Tibshirani, Gavin Sherlock, Michael B. Eisen, Patrick O. Brown, David Botstein. 2001

282 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Rahul Mazumder
2. Robert Tibshirani
3. Trevor J. Hastie
4. Peter Langfelder
5. Steve Horvath
6. Jianqing Fan
7. Yoshua Bengio
8. Francis R. Bach

Citations in the corpus

• Spectral Regularization Algorithms for Learning Large Incomplete Matrices (2010, methodology): “In penalized regression there have been recent developments directed towards “bridging” the gap between the l1 and l0 penalties [ Fri08 , FL01, Zha07].”
• Spectral biclustering of microarray data: coclustering genes and conditions. (2003, background): “Each of the above normalization approaches (independent scaling, bistochastization, or log interactions) gives rise, after the SVD, to a set of gene and condition eigenvectors (that in the context of microarray analysis are sometimes termed eigengenes and eigenarrays; Hastie et al. 1999; Alter et al”
• WGCNA: an R package for weighted correlation network analysis (2008, methodology): “Eigengene calculation incorporates imputation of missing values implemented in the package impute [31,32].”