Microarray data normalization and transformation

Data Reduction and Error Analysis for the Physical Sciences

Bevington, P. R., Robinson, D. K., Blair, J. M., Mallinckrodt, A. J., & McKay, S. R. (1993) · Computers in Physics · doi:10.1063/1.4823194 · Cited by 14,992 · see citing works

Cluster analysis and display of genome-wide expression patterns

Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 12,617 · see citing works

Robust Locally Weighted Regression and Smoothing Scatterplots

Cleveland, W. S. (1979) · Journal of the American Statistical Association · doi:10.1080/01621459.1979.10481038 · Cited by 11,594 · see citing works

Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.

Yang, Y., Dudoit, S., Luu, P., Lin, D., Peng, V., Ngai, J., & Speed, T. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.4.e15 · Cited by 3,722 · see citing works

Data Reduction and Error Analysis for the Physical Sciences.

C., E. L., & Bevington, P. R. (1972) · Journal of the American Statistical Association · doi:10.2307/2284752 · Cited by 3,586 · see citing works

Regression Analysis by Example.

Kmenta, J., Chatterjee, S., & Price, B. P. (1980) · Journal of the American Statistical Association · doi:10.2307/2287213 · Cited by 3,211 · see citing works

Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection.

Li, C., & Wong, W. H. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.1.31 · Cited by 3,133 · see citing works

Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation

Tamayo, P., Slonim, D. K., Mesirov, J. P., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E. S., & Golub, T. R. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.6.2907 · Cited by 2,924 · see citing works

Variance stabilization applied to microarray data calibration and to the quantification of differential expression

Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A., & Vingron, M. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s96 · Cited by 2,440 · see citing works

Fundamentals of experimental design for cDNA microarrays

Churchill, G. A. (2002) · Nature Genetics · doi:10.1038/ng1031 · Cited by 1,049 · see citing works

Large-scale temporal gene expression mapping of central nervous system development

Wen, X., Fuhrman, S., Michaels, G. S., Carr, D. B., Smith, S. V., Barker, J. L., & Somogyi, R. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.1.334 · Cited by 645 · see citing works

Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects

Tseng, G. C. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.12.2549 · Cited by 574 · see citing works

Ratio-based decisions and the quantitative analysis of cDNA microarray images

Chen, Y., Dougherty, E. R., & Bittner, M. L. (1997) · Journal of Biomedical Optics · doi:10.1117/12.281504 · Cited by 551 · see citing works

Regression Analysis by Example

Kanji, G. K., Chatterjee, S., & Price, B. (1993) · Biometrics · doi:10.2307/2532627 · Cited by 513 · see citing works

A Model for Measurement Error for Gene Expression Arrays

Rocke, D. M., & Durbin, B. (2001) · Journal of Computational Biology · doi:10.1089/106652701753307485 · Cited by 437 · see citing works

Regression Analysis by Example.

Woodward, P., Chatterjee, S., & Price, B. (1994) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2348349 · Cited by 430 · see citing works

Within the fold: assessing differential expression measures and reproducibility in microarray assays

Yang, I. V., Chen, E., Hasseman, J. P., Liang, W. P., Frank, B. C., Wang, S., Sharov, V., Saeed, A. I., White, J., Li, J., et al. (2002) · Genome biology · doi:10.1186/gb-2002-3-11-research0062 · Cited by 336 · see citing works

Testing for Differentially-Expressed Genes by Maximum-Likelihood Analysis of Microarray Data

Ideker, T., Thórsson, V., Siegel, A. F., et al. (2000) · Journal of Computational Biology · doi:10.1089/10665270050514945 · Cited by 327 · see citing works

Microarray databases: standards and ontologies

Stoeckert, C. J., Causton, H. C., & Ball, C. A. (2002) · Nature Genetics · doi:10.1038/ng1028 · Cited by 159 · see citing works

An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex

Zhang, Y., Chen, K., Sloan, S. A., Bennett, M. L., Scholze, A. R., O'Keeffe, S., Phatnani, H. P., Guarnieri, P., Caneda, C., Ruderisch, N., et al. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.1860-14.2014 · Cited by 4,875 · see citing works

The rise of big data on cloud computing: Review and open research issues

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015) · Information Systems · doi:10.1016/j.is.2014.07.006 · Cited by 2,656 · see citing works

Microarray data analysis: from disarray to consolidation and consensus

Allison, D. B., Cui, X., Page, G. P., & Sabripour, M. (2006) · Nature Reviews Genetics · doi:10.1038/nrg1749 · Cited by 1,339 · see citing works

Direct conversion of mouse fibroblasts to hepatocyte-like cells by defined factors

Sekiya, S., & Suzuki, A. (2011) · Nature · doi:10.1038/nature10263 · Cited by 846 · see citing works

Mining the Human Gut Microbiota for Immunomodulatory Organisms

Geva-Zatorsky, N., Sefik, E., Kua, L., Pasman, L., Tan, T. G., Ortiz-Lopez, A., Yanortsang, T. B., Yang, L., Jupp, R., Mathis, D., et al. (2017) · Cell · doi:10.1016/j.cell.2017.01.022 · Cited by 762 · see citing works

Gene regulatory network inference: Data integration in dynamic models—A review

Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., & Guthke, R. (2009) · Biosystems · doi:10.1016/j.biosystems.2008.12.004 · Cited by 755 · see citing works

The lack of a systematic validation of reference genes: a serious pitfall undervalued in reverse transcription‐polymerase chain reaction (RT‐PCR) analysis in plants

Gutierrez, L., Mauriat, M., Guénin, S., Pelloux, J., Lefebvre, J., Louvet, R., Rusterucci, C., Moritz, T., Guerineau, F., Bellini, C., et al. (2008) · Plant Biotechnology Journal · doi:10.1111/j.1467-7652.2008.00346.x · Cited by 696 · see citing works

Revisiting Global Gene Expression Analysis

Lovén, J., Orlando, D. A., Sigova, A. A., Lin, C. Y., Rahl, P. B., Burge, C. B., Levens, D. L., Lee, T. I., & Young, R. A. (2012) · Cell · doi:10.1016/j.cell.2012.10.012 · Cited by 616 · see citing works

Weight loss regulates inflammation‐related genes in white adipose tissue of obese subjects

ClÉment, K., Viguerie, N., Poitou, C., Carette, C., Pelloux, V., Curat, C. A., Sicard, A., Rome, S., Benis, A., Zucker, J., et al. (2004) · The FASEB Journal · doi:10.1096/fj.04-2204com · Cited by 640 · see citing works

A High-Fat Diet Coordinately Downregulates Genes Required for Mitochondrial Oxidative Phosphorylation in Skeletal Muscle

Sparks, L. M., Xie, H., Koza, R. A., Mynatt, R., Hulver, M. W., Bray, G. A., & Smith, S. R. (2005) · Diabetes · doi:10.2337/diabetes.54.7.1926 · Cited by 629 · see citing works

A systematic evaluation of normalization methods in quantitative label-free proteomics

Välikangas, T., Suomi, T., & Elo, L. L. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw095 · Cited by 336 · see citing works

Global methylation profiling of lymphoblastoid cell lines reveals epigenetic contributions to autism spectrum disorders and a novel autism candidate gene, <i>RORA</i> , whose protein product is reduced in autistic brain

Nguyen, A., Rauch, T. A., Pfeifer, G. P., & Hu, V. W. (2010) · The FASEB Journal · doi:10.1096/fj.10-154484 · Cited by 350 · see citing works

Deficient Hippocampal Neuron Expression of Proteasome, Ubiquitin, and Mitochondrial Genes in Multiple Schizophrenia Cohorts

Altar, C. A., Jurata, L. W., Charles, V., Lemire, A., Liu, P., Bukhman, Y., Young, T. A., Bullard, J., Yokoe, H., Webster, M. J., et al. (2005) · Biological Psychiatry · doi:10.1016/j.biopsych.2005.03.031 · Cited by 318 · see citing works

Classification of human chromosome 21 gene-expression variations in down syndrome: impact on disease phenotypes

Aït Yahya-Graison, E., Aubert, J., Dauphinot, L., Rivals, I., Prieur, M., Golfier, G., Rossier, J., Personnaz, L., Créau, N., Bléhaut, H., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/520000 · Cited by 338 · see citing works

Islet cell hyperexpression of HLA class I antigens: a defining feature in type 1 diabetes

Richardson, S. J., Rodriguez-Calvo, T., Gerling, I. C., Mathews, C. E., Kaddis, J. S., Russell, M. A., Zeissler, M., Leete, P., Krogvold, L., Dahl-Jørgensen, K., et al. (2016) · Diabetologia · doi:10.1007/s00125-016-4067-4 · Cited by 298 · see citing works

<i>Lactobacillus</i> -dominated cervicovaginal microbiota associated with reduced HIV/STI prevalence and genital HIV viral load in African women

Borgdorff, H., Tsivtsivadze, E., Verhelst, R., Marzorati, M., Jurriaans, S., Ndayisaba, G. F., Schuren, F. H., & van de Wijgert, J. H. H. M. (2014) · The ISME Journal · doi:10.1038/ismej.2014.26 · Cited by 287 · see citing works

Cellular response to 5-fluorouracil (5-FU) in 5-FU-resistant colon cancer cell lines during treatment and recovery

De Angelis, P. M., Svendsrud, D. H., Kravik, K. L., & Stokke, T. (2006) · Molecular Cancer · doi:10.1186/1476-4598-5-20 · Cited by 233 · see citing works

Progress in the use of microarray technology to study the neurobiology of disease

Mirnics, K., & Pevsner, J. (2004) · Nature Neuroscience · doi:10.1038/nn1230 · Cited by 150 · see citing works

Heme Levels Switch the Function of Hap1 of <i>Saccharomyces cerevisiae</i> between Transcriptional Activator and Transcriptional Repressor

Hickman, M. J., & Winston, F. (2007) · Molecular and Cellular Biology · doi:10.1128/mcb.00887-07 · Cited by 137 · see citing works

Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications: A Taxonomy and Survey

Qian, B., Su, J., Wen, Z., Jha, D. N., Li, Y., Guan, Y., Puthal, D., James, P., Yang, R., Zomaya, A. Y., et al. (2021) · ACM Computing Surveys · doi:10.1145/3398020 · Cited by 132 · see citing works

DNA microarray data imputation and significance analysis of differential expression

Jörnsten, R., Wang, H.-Y., Welsh, W. J., & Ouyang, M. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti638 · Cited by 121 · see citing works

Machine learning in bioinformatics: A brief survey and recommendations for practitioners

Bhaskar, H., Hoyle, D. C., & Singh, S. (2006) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2005.09.002 · Cited by 109 · see citing works

Molecular responses of genetically modified maize to abiotic stresses as determined through proteomic and metabolomic analyses

Benevenuto, R. F., Agapito-Tenfen, S. Z., Vilperte, V., Wikmark, O.-G., van Rensburg, P. J., & Nodari, R. O. (2017) · PLOS ONE · doi:10.1371/journal.pone.0173069 · Cited by 72 · see citing works

Normalization of boutique two-color microarrays with a high proportion of differentially expressed probes

Oshlack, A., Emslie, D., Corcoran, L. M., & Smyth, G. K. (2007) · Genome biology · doi:10.1186/gb-2007-8-1-r2 · Cited by 69 · see citing works

Integrated Quantitative Transcriptome Maps of Human Trisomy 21 Tissues and Cells

Pelleri, M. C., Cattani, C., Vitale, L., Antonaros, F., Strippoli, P., Locatelli, C., Cocchi, G., Piovesan, A., & Caracausi, M. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00125 · Cited by 61 · see citing works

MIPHENO: data normalization for high throughput metabolite analysis

Bell, S. M., Burgoon, L. D., & Last, R. L. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-10 · Cited by 17 · see citing works

Expression profiling of drug response - from genes to pathways

Herwig, R., & Lehrach, H. (2006) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2006.8.3/rherwig · Cited by 14 · see citing works

Positional artifacts in microarrays: experimental verification and construction of COP, an automated detection tool

Yu, H., Nguyen, K., Royce, T., Qian, J., Nelson, K., Snyder, M., & Gerstein, M. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl871 · Cited by 13 · see citing works

Advanced Microarray Data Analysis

Kim, J. H. (2019) · Learning materials in biosciences · doi:10.1007/978-981-13-1942-6_5 · Cited by 1 · see citing works

Data Normalization

Priddy, K. L., & Keller, P. E. (2009) · SPIE eBooks · doi:10.1117/3.633187.ch3

NORMA-Gene: A simple and robust method for qPCR normalization based on target gene data

Heckmann, L., Sørensen, P. B., Krogh, P. H., & Sørensen, J. G. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-250 · Cited by 151 · see citing works

Normalization of Biological Expression Data Based on Selection of a Stable Element Set

Bouki, Y., Yoshihiro, T., Inoue, E., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23854-3_17

Minimum information about a microarray experiment (MIAME)-toward standards for microarray data

Brazma, A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C., Aach, J., Ansorge, W., Ball, C. A., Causton, H. C., et al. (2001) · Nature Genetics · doi:10.1038/ng1201-365 · Cited by 4,109 · see citing works