A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis
暂无分享,去创建一个
Jean-Pierre A. Kocher | Kellie J. Archer | Terry M. Therneau | Jeanette E. Eckel-Passow | Celine M. Vachon | Mariza de Andrade | Sarah E. Reese | Elizabeth J. Atkinson | T. Therneau | C. Vachon | J. Kocher | E. Atkinson | J. Eckel-Passow | M. Andrade | K. Archer | S. Reese
[1] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[2] Thomas A. Louis,et al. Quantifying uncertainty in genotype calls , 2010, Bioinform..
[3] Alexander V. Alekseyenko,et al. Visualization and Statistical Comparisons of Microbial Communities Using R Packages on Phylochip Data , 2011, Pacific Symposium on Biocomputing.
[4] Yufeng Liu,et al. R/DWD: distance-weighted discrimination for classification, visualization and batch adjustment , 2012, Bioinform..
[5] InzaIñaki,et al. Filter versus wrapper gene selection approaches in DNA microarray domains , 2004 .
[6] Nicholas J. Schork,et al. Preprocessing and Quality Control Strategies for Illumina DASL Assay-Based Brain Gene Expression Studies with Semi-Degraded Samples , 2012, Front. Gene..
[7] Chunyu Liu,et al. Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods , 2011, PloS one.
[8] J. S. Marron,et al. Distance-Weighted Discrimination , 2007 .
[9] Joel S. Parker,et al. Adjustment of systematic microarray data biases , 2004, Bioinform..
[10] John Quackenbush,et al. Integrated Analysis of Multiple Microarray Datasets Identifies a Reproducible Survival Predictor in Ovarian Cancer , 2011, PloS one.
[11] Jonathon Shlens,et al. A Tutorial on Principal Component Analysis , 2014, ArXiv.
[12] A. Butte,et al. Microarrays for an Integrative Genomics , 2002 .
[13] Lesley Jones,et al. Microarray Gene Expression Data Analysis: A Beginners Guide , 2004, Human Genetics.
[14] Hugues Bersini,et al. Batch effect removal methods for microarray gene expression data integration: a survey , 2013, Briefings Bioinform..
[15] S. Dudoit,et al. Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .
[16] Andrew E. Jaffe,et al. Bioinformatics Applications Note Gene Expression the Sva Package for Removing Batch Effects and Other Unwanted Variation in High-throughput Experiments , 2022 .
[17] Jeffrey T Leek,et al. A general framework for multiple testing dependence , 2008, Proceedings of the National Academy of Sciences.
[18] Pedro Larrañaga,et al. Filter versus wrapper gene selection approaches in DNA microarray domains , 2004, Artif. Intell. Medicine.
[19] Cheng Li,et al. Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.
[20] K. V. Donkena,et al. Batch effect correction for genome-wide methylation data with Illumina Infinium platform , 2011, BMC Medical Genomics.
[21] Peter Kraft,et al. Quality control and quality assurance in genotypic data for genome‐wide association studies , 2010, Genetic epidemiology.
[22] John D. Storey,et al. Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis , 2007, PLoS genetics.
[23] Richard A. Johnson,et al. Applied Multivariate Statistical Analysis , 1983 .
[24] T. Sellers,et al. Epidemiologic and genetic follo‐up study of 544 Minnesota breast cancer families: Design and methods , 1995, Genetic epidemiology.
[25] Gary A. Churchill,et al. Randomization in Laboratory Procedure Is Key to Obtaining Reproducible Microarray Results , 2008, PloS one.
[26] Crispin J. Miller,et al. The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis , 2008, BMC Medical Genomics.
[27] Rafael A Irizarry,et al. Frozen robust multiarray analysis (fRMA). , 2010, Biostatistics.