Correlating gene and protein expression data using Correlated Factor Analysis

[1] Nie L(2007)Integrative analysis of transcriptomic and proteomic data: challenges, solutions and applications Crit Rev Biotechnol 27 63-75

[2] Wu G(2006)Data merging for integrated microarray and proteomic analysis Brief Funct Genomic Proteomic 5 261-272

[3] Culley DE(2005)Integrating gene and protein expression data: pattern analysis and profile mining Methods 35 303-314

[4] Scholten JCM(2006)Integrated analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: zero-inflated Poisson regression models to predict abundance of undetected proteins Bioinformatics 22 1641-1647

[5] Zhang W(2001)Missing value estimation methods for DNA microarrays Bioinformatics 17 520-525

[6] Waters KM(2004)LSimpute: accurate estimation of missing values in microarray data with least squares methods Nucleic Acids Res 32 e34-2171

[7] Pounds JG(2007)A multivariate analysis approach to the integration of proteomic and gene expression data Proteomics 7 2162-24

[8] Thrall BD(2007)Model-based maximum covariance analysis for irregularly observed climatological data J Agric Biol Environ Stat 12 1-16

[9] Cox B(2006)Jointly analyzing gene expression and copy number data in breast cancer using data reduction models IEEE/ACM Trans Comput Biol Bioinform 3 2-3356

[10] Kislinger T(2003)Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms Proc Natl Acad Sci USA 100 3351-832

[1] Nie L(2007)Integrative analysis of transcriptomic and proteomic data: challenges, solutions and applications Crit Rev Biotechnol 27 63-75

[2] Wu G(2006)Data merging for integrated microarray and proteomic analysis Brief Funct Genomic Proteomic 5 261-272

[3] Culley DE(2005)Integrating gene and protein expression data: pattern analysis and profile mining Methods 35 303-314

[4] Scholten JCM(2006)Integrated analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: zero-inflated Poisson regression models to predict abundance of undetected proteins Bioinformatics 22 1641-1647

[5] Zhang W(2001)Missing value estimation methods for DNA microarrays Bioinformatics 17 520-525

[6] Waters KM(2004)LSimpute: accurate estimation of missing values in microarray data with least squares methods Nucleic Acids Res 32 e34-2171

[7] Pounds JG(2007)A multivariate analysis approach to the integration of proteomic and gene expression data Proteomics 7 2162-24

[8] Thrall BD(2007)Model-based maximum covariance analysis for irregularly observed climatological data J Agric Biol Environ Stat 12 1-16

[9] Cox B(2006)Jointly analyzing gene expression and copy number data in breast cancer using data reduction models IEEE/ACM Trans Comput Biol Bioinform 3 2-3356

[10] Kislinger T(2003)Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms Proc Natl Acad Sci USA 100 3351-832