%0 Journal Article %1 Fellenberg2001 %A Fellenberg, K. %A Hauser, N. C. %A Brors, B. %A Neutzner, A. %A Hoheisel, J. D. %A Vingron, M. %D 2001 %J Proc Natl Acad Sci U S A %K Analysis, Array Cell Cycle Cycle; Data Expression; Gene Genetic Interpretation, Oligonucleotide Phosphatases; Protein Proteins, Proteins; Saccharomyces Sequence Statistical; Transcription, Tyrosine cerevisiae cerevisiae, genetics; methods; %N 19 %P 10781--10786 %R 10.1073/pnas.181597298 %T Correspondence analysis applied to microarray data. %U http://dx.doi.org/10.1073/pnas.181597298 %V 98 %X Correspondence analysis is an explorative computational method for the study of associations between variables. Much like principal component analysis, it displays a low-dimensional projection of the data, e.g., into a plane. It does this, though, for two variables simultaneously, thus revealing associations between them. Here, we demonstrate the applicability of correspondence analysis to and high value for the analysis of microarray data, displaying associations between genes and experiments. To introduce the method, we show its application to the well-known Saccharomyces cerevisiae cell-cycle synchronization data by Spellman et al. Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P. O., Botstein, D. & Futcher, B. (1998) Mol. Biol. Cell 9, 3273-3297, allowing for comparison with their visualization of this data set. Furthermore, we apply correspondence analysis to a non-time-series data set of our own, thus supporting its general applicability to microarray data of different complexity, underlying structure, and experimental strategy (both two-channel fluorescence-tag and radioactive labeling).

@article{Fellenberg2001, __markedentry = {[bbrors:6]}, abstract = {Correspondence analysis is an explorative computational method for the study of associations between variables. Much like principal component analysis, it displays a low-dimensional projection of the data, e.g., into a plane. It does this, though, for two variables simultaneously, thus revealing associations between them. Here, we demonstrate the applicability of correspondence analysis to and high value for the analysis of microarray data, displaying associations between genes and experiments. To introduce the method, we show its application to the well-known Saccharomyces cerevisiae cell-cycle synchronization data by Spellman et al. [Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P. O., Botstein, D. & Futcher, B. (1998) Mol. Biol. Cell 9, 3273-3297], allowing for comparison with their visualization of this data set. Furthermore, we apply correspondence analysis to a non-time-series data set of our own, thus supporting its general applicability to microarray data of different complexity, underlying structure, and experimental strategy (both two-channel fluorescence-tag and radioactive labeling).}, added-at = {2015-04-09T12:36:21.000+0200}, author = {Fellenberg, K. and Hauser, N. C. and Brors, B. and Neutzner, A. and Hoheisel, J. D. and Vingron, M.}, biburl = {https://www.bibsonomy.org/bibtex/2a389aa2c906c28b17d251d44c2164349/bbrors}, doi = {10.1073/pnas.181597298}, institution = {Department of Theoretical Bioinformatics, German Cancer Research Center, PO 101949, D-69009 Heidelberg, Germany.}, interhash = {ebd403fe438a8484c623e4938635386f}, intrahash = {a389aa2c906c28b17d251d44c2164349}, journal = {Proc Natl Acad Sci U S A}, keywords = {Analysis, Array Cell Cycle Cycle; Data Expression; Gene Genetic Interpretation, Oligonucleotide Phosphatases; Protein Proteins, Proteins; Saccharomyces Sequence Statistical; Transcription, Tyrosine cerevisiae cerevisiae, genetics; methods;}, language = {eng}, medline-pst = {ppublish}, month = Sep, number = 19, owner = {bbrors}, pages = {10781--10786}, pii = {181597298}, pmid = {11535808}, timestamp = {2015-04-09T12:36:21.000+0200}, title = {Correspondence analysis applied to microarray data.}, url = {http://dx.doi.org/10.1073/pnas.181597298}, volume = 98, year = 2001 }