Cited by 520Gene expression and cancer classificationMolecular Biology Techniques and ApplicationsGenomics and Phylogenetic Studies

The DOI matches its Crossref record; 19 of the 20 references it cites were found in a registry or an index.

Accuracy and calibration of commercial oligonucleotide and custom cDNA microarrays

Open access

Yuen, T.·2002·Nucleic Acids Research

DOI 10.1093/nar/30.10.e48PMC PMC115302

Open this paper in CoChat — free account Open PDF

Abstract

We compared the accuracy of microarray measurements obtained with oligonucleotide arrays (GeneChip, Affymetrix) with a laboratory-developed cDNA array by assaying test RNA samples from an experiment using a paradigm known to regulate many genes measured on both arrays. We selected 47 genes represented on both arrays, including both known regulated and unregulated transcripts, and established reference relative expression measurements for these genes in the test RNA samples using quantitative reverse transcriptase real-time PCR (QRTPCR) assays. The validity of the reproducible (average coefficient of variation = 11.8%) QRTPCR measurements were established through application of a new mathematical model. The performance of both array platforms in identifying regulated and non-regulated genes was identical. With either platform, 16 of 17 definitely regulated genes were correctly identified, and no definitely unregulated transcript was falsely identified as regulated. Accuracy of the fold-change measurements obtained with each platform was assessed by determining measurement bias. Both platforms consistently underestimate the relative changes in mRNA expression between experimental and control samples. The bias observed with cDNA arrays was predictable for fold-changes <250-fold by QRTPCR and could be corrected by the calibration function F(c) = F(a(cDNA))(q), where F(a(cDNA)) is the microarray-determined fold-change comparing experimental with control samples, q is the correction factor and F(c) is the calibrated value. The bias observed with the commercial oligonucleotide arrays was less predictable and calibration was unfeasible. Following calibration, fold-change measurements generated by custom cDNA arrays were more accurate than those obtained by commercial oligonucleotide arrays. Our study demonstrates systematic bias of microarray measurements and identifies a calibration function that improves the accuracy of cDNA array data.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1093/nar/30.10.e48

Cites

19 of 19 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Single-Step Method of RNA Isolation by Acid Guanidinium Thiocyanate–Phenol–Chloroform Extraction
Chomczynski, P., & Sacchi, N. (1987) · Analytical Biochemistry · doi:10.1006/abio.1987.9999 · Cited by 65,515
A new mathematical model for relative quantification in real-time RT-PCR
Pfaffl, M. W. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.9.e45 · Cited by 34,610
Modern Applied Statistics with S
Venables, W. N., & Ripley, B. D. (2002) · Statisctics and computing/Statistics and computing · doi:10.1007/978-0-387-21706-2 · Cited by 25,941
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring
Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al. (1999) · Science · doi:10.1126/science.286.5439.531 · Cited by 11,516
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., et al. (2000) · Nature · doi:10.1038/35000501 · Cited by 9,925
Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays
Bustin, S. A. (2000) · Journal of Molecular Endocrinology · doi:10.1677/jme.0.0250169 · Cited by 3,844
Modern Applied Statistics with S-Plus.
Venables, W. N., & Ripley, B. D. (1996) · Biometrics · doi:10.2307/2532871 · Cited by 3,012
Exploring the new world of the genome with DNA microarrays
Brown, P. O., & Botstein, D. (1999) · Nature Genetics · doi:10.1038/4462 · Cited by 2,430
High density synthetic oligonucleotide arrays
Lipshutz, R. J., Fodor, S. P. A., Gingeras, T., & Lockhart, D. J. (1999) · Nature Genetics · doi:10.1038/4447 · Cited by 2,204
The Transcriptional Program in the Response of Human Fibroblasts to Serum
Iyer, V. R., Eisen, M. B., Ross, D. T., Schuler, G. D., Moore, T. A., Lee, J. C. F., Trent, J. M., Staudt, L. M., Hudson, J., Boguski, M. S., et al. (1999) · Science · doi:10.1126/science.283.5398.83 · Cited by 1,966

Flagged first, then by citations

Show all 20 references

1 of the 20 references couldn’t be matched to a known paper.

Sorted by citations

In vitro propagation and transcriptional profiling of human mammary stem/progenitor cellsPreprint
Dontu, G., Abdallah, W. M., Foley, J. M., Jackson, K. W., Clarke, M. F., Kawamura, M., & Wicha, M. S. (2003) · Genes & Development · doi:10.1101/gad.1061803 · Cited by 2,413
"Stemness": Transcriptional Profiling of Embryonic and Adult Stem Cells
Ramalho‐Santos, M., Yoon, S., Matsuzaki, Y., Mulligan, R. C., & Melton, D. A. (2002) · Science · doi:10.1126/science.1072530 · Cited by 1,711
Capturing and profiling adult hair follicle stem cells
Morris, R. J., Liu, Y., Marles, L., Yang, Z., Trempus, C., Li, S., Lin, J. S., Sawicki, J. A., & Cotsarelis, G. (2004) · Nature Biotechnology · doi:10.1038/nbt950 · Cited by 1,283
Chromosome Conformation Capture Carbon Copy (5C): A massively parallel solution for mapping interactions between genomic elementsPreprint
Dostie, J., Richmond, T., Arnaout, R., Selzer, R. R., Lee, W. L., Honan, T., Rubio, E. D., Krumm, A., Lamb, J., Nusbaum, C., et al. (2006) · Genome Research · doi:10.1101/gr.5571506 · Cited by 1,237
Microarray Analysis of Pseudomonas aeruginosa Quorum-Sensing Regulons: Effects of Growth Phase and Environment
Wagner, V., Bushnell, D. S., Passador, L., Brooks, A. I., & Iglewski, B. H. (2003) · Journal of Bacteriology · doi:10.1128/jb.185.7.2080-2095.2003 · Cited by 923
Prediction of Survival in Diffuse Large-B-Cell Lymphoma Based on the Expression of Six Genes
Lossos, I. S., Czerwinski, D. K., Alizadeh, A. A., Wechser, M. A., Tibshirani, R. J., Botstein, D., & Levy, R. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa032520 · Cited by 912
Multiple-laboratory comparison of microarray platforms
Irizarry, R. A., Warren, D., Spencer, F., Kim, I. F., Biswal, S., Frank, B. C., Gabrielson, E., Garcia, J. G. N., Geoghegan, J., Germino, G., et al. (2005) · Nature Methods · doi:10.1038/nmeth756 · Cited by 862
Coexpression Analysis of Human Genes Across Many Microarray Data Sets
Lee, H. K., Hsu, A. K., Sajdak, J., Qin, J., & Pavlidis, P. (2004) · Genome Research · doi:10.1101/gr.1910904 · Cited by 770
Comparative analysis of proteome and transcriptome variation in mouse
Ghazalpour, A., Bennett, B., Petyuk, V. A., Orozco, L., Hagopian, R., Mungrue, I. N., Farber, C. R., Sinsheimer, J., Kang, H. M., Furlotte, N., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001393 · Cited by 624
“Previous reports have documented conflicting results about the reproducibility measurements generated by microarray platforms [17]–[21].”
Result
Gene Expression Profiles in Paraffin-Embedded Core Biopsy Tissue Predict Response to Chemotherapy in Women With Locally Advanced Breast Cancer
Gianni, L., Zambetti, M., Clark, K., Baker, J. B., Cronin, M. T., Wu, J., Mariani, G., Rodriguez‐Canales, J., Carcangiu, M. L., Watson, D., et al. (2005) · Journal of Clinical Oncology · doi:10.1200/jco.2005.02.0818 · Cited by 621
Characterization and isolation of stem cell-enriched human hair follicle bulge cells
Ohyama, M. (2005) · Journal of Clinical Investigation · doi:10.1172/jci26043 · Cited by 623
Reliability and reproducibility issues in DNA microarray measurements
DRAGHICI, S., KHATRI, P., EKLUND, A., & SZALLASI, Z. (2006) · Trends in Genetics · doi:10.1016/j.tig.2005.12.005 · Cited by 590
Transcriptional profiling of the LPS induced NF-κB response in macrophages
Sharif, O., Bolshakov, V. N., Raines, S., Newham, P., & Perkins, N. D. (2007) · BMC Immunology · doi:10.1186/1471-2172-8-1 · Cited by 513
pH Regulates Genes for Flagellar Motility, Catabolism, and Oxidative Stress in Escherichia coli K-12
Maurer, L. M., Yohannes, E., BonDurant, S. S., Radmacher, M. D., & Slonczewski, J. L. (2004) · Journal of Bacteriology · doi:10.1128/jb.187.1.304-319.2005 · Cited by 454
Microarray Analysis and Tumor Classification
Quackenbush, J. F. (2006) · New England Journal of Medicine · doi:10.1056/nejmra042342 · Cited by 459
A high-fat, ketogenic diet induces a unique metabolic state in mice
Kennedy, A. R., Pissios, P., Otu, H., Xue, B., Asakura, K., Furukawa, N., Marino, F. E., Liu, F.-F., Kahn, B. B., Libermann, T. A., et al. (2007) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00717.2006 · Cited by 420
Differential gene expression during thermal stress and bleaching in the Caribbean coral Montastraea faveolata
DeSalvo, M. K., Voolstra, C. R., Sunagawa, S., Schwarz, J. A., Stillman, J. H., Coffroth, M. A., Szmant, A. M., & Medina, M. (2008) · Molecular Ecology · doi:10.1111/j.1365-294x.2008.03879.x · Cited by 436
Properties of the Reverse Transcription Reaction in mRNA Quantification
Ståhlberg, A., Håkansson, J., Xian, X., Semb, H., & Kubista, M. (2004) · Clinical Chemistry · doi:10.1373/clinchem.2003.026161 · Cited by 409
Blocking FSH induces thermogenic adipose tissue and reduces body fat
Liu, P., Ji, Y., Yuen, T., Rendina-Ruedy, E., DeMambro, V., Dhawan, S., Abu‐Amer, W., Izadmehr, S., Zhou, B., Shin, A. C., et al. (2017) · Nature · doi:10.1038/nature22342 · Cited by 348
Self-citation
Gene expression levels assessed by oligonucleotide microarray analysis and quantitative real-time RT-PCR – how well do they correlate?
Dallas, P. B., Gottardo, N. G., Firth, M. J., Beesley, A. H., Hoffmann, K., Terry, P. A., Freitas, J. R., Boag, J. M., Cummings, A. J., & Kees, U. R. (2005) · BMC Genomics · doi:10.1186/1471-2164-6-59 · Cited by 341
IL-1β Regulates Blood-Brain Barrier Permeability via Reactivation of the Hypoxia-Angiogenesis Program
Argaw, A. T., Zhang, Y., Snyder, B., Zhao, M., Kopp, N., Lee, S. C., Raine, C. S., Brosnan, C. F., & John, G. R. (2006) · The Journal of Immunology · doi:10.4049/jimmunol.177.8.5574 · Cited by 349
The Anopheles gambiae detoxification chip: A highly specific microarray to study metabolic-based insecticide resistance in malaria vectors
David, J., Strode, C., VONTAS, J. G., Nikou, D. C., Vaughan, A. M., Pignatelli, P. M., Louis, C., Hemingway, J., & Ranson, H. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0409348102 · Cited by 338
A model of molecular interactions on short oligonucleotide microarrays
Zhang, L., Miles, M. F., & Aldape, K. (2003) · Nature Biotechnology · doi:10.1038/nbt836 · Cited by 320
Cross-induction of detoxification genes by environmental xenobiotics and insecticides in the mosquito Aedes aegypti: Impact on larval tolerance to chemical insecticides
Poupardin, R., Reynaud, S., Strode, C., Ranson, H., VONTAS, J. G., & David, J. (2008) · Insect Biochemistry and Molecular Biology · doi:10.1016/j.ibmb.2008.01.004 · Cited by 317
Influenza Virus Evades Innate and Adaptive Immunity via the NS1 Protein
Fernández-Sesma, A., Marukian, S., Ebersole, B. J., Kaminski, D. A., Park, M., Yuen, T., Sealfon, S. C., García‐Sastre, A., & Moran, T. M. (2006) · Journal of Virology · doi:10.1128/jvi.02381-05 · Cited by 305
Self-citation
Analysis of microarray experiments of gene expression profiling
Tarca, A. L., Romero, R. J., & Drăghici, S. (2006) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2006.07.001 · Cited by 312
Transcriptome fingerprints distinguish hallucinogenic and nonhallucinogenic 5-hydroxytryptamine 2A receptor agonist effects in mouse somatosensory cortex
González-Maeso, J., Yuen, T., Ebersole, B. J., Wurmbach, E., Lira, A., Zhou, M., Weisstaub, N., Hen, R., Gingrich, J. A., & Sealfon, S. C. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-26-08836.2003 · Cited by 297
Self-citation
Gene expression profile of zebrafish exposed to hypoxia during development
Ton, C., Stamatiou, D., & Liew, C. (2003) · Physiological Genomics · doi:10.1152/physiolgenomics.00128.2002 · Cited by 293
A benchmark for Affymetrix GeneChip expression measures
Cope, L., Irizarry, R. A., Jaffee, H. A., Wu, Z. J., & Speed, T. P. (2004) · Bioinformatics · doi:10.1093/bioinformatics/btg410 · Cited by 282
Trends in microarray analysis
Stears, R. L., Martinsky, T., & Schena, M. (2003) · Nature Medicine · doi:10.1038/nm0103-140 · Cited by 280
Whole Genome Analysis of Genetic Alterations in Small DNA Samples Using Hyperbranched Strand Displacement Amplification and Array–CGHPreprint
Lage, J. M., Leamon, J. H., Pejovic, T. B., Hamann, S., Lacey, M. R., Dillon, D. A. R., Segraves, R., Debrunner‐Vossbrinck, B. A., González, A., Pinkel, D., et al. (2003) · Genome Research · doi:10.1101/gr.377203 · Cited by 261
ESTs, cDNA microarrays, and gene expression profiling: tools for dissecting plant physiology and development
Alba, R., Fei, Z., Payton, P., Liu, Y., Moore, S. L., Debbie, P., Cohn, J., D'Ascenzo, M. D., Gordon, J. S., Rose, J. K. C., et al. (2004) · The Plant Journal · doi:10.1111/j.1365-313x.2004.02178.x · Cited by 254
Glucocorticoid Receptor-Dependent Gene Regulatory Networks
Le, P. P., Friedman, J. R., Schug, J., Brestelli, J. E., Parker, J. B., Bochkis, I. M., & Kaestner, K. H. (2005) · PLoS Genetics · doi:10.1371/journal.pgen.0010016 · Cited by 252
Estrogen Receptor-Dependent Genomic Responses in the Uterus Mirror the Biphasic Physiological Response to Estrogen
Hewitt, S. C., Deroo, B. J., Hansen, K., Collins, J. B., Grissom, S. F., Afshari, C. A., & Korach, K. S. (2003) · Molecular Endocrinology · doi:10.1210/me.2003-0146 · Cited by 246
Inhibition of the Type I Interferon Response by the Nucleoprotein of the Prototypic Arenavirus Lymphocytic Choriomeningitis Virus
Martínez‐Sobrido, L., Zúñiga, E. I., Rosario, D., García‐Sastre, A., & de la Torre, J. C. (2006) · Journal of Virology · doi:10.1128/jvi.00555-06 · Cited by 247
Comprehensive comparison of six microarray technologies
Yauk, C. L. (2004) · Nucleic Acids Research · doi:10.1093/nar/gnh123 · Cited by 244
A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments
Hong, F., & Breitling, R. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btm620 · Cited by 234
Comprehensive Analysis of Human Endogenous Retrovirus Transcriptional Activity in Human Tissues with a Retrovirus-Specific Microarray
Seifarth, W., Frank, O., Zeilfelder, U., Spieß, B., Greenwood, A. D., Hehlmann, R., & Leib‐Mösch, C. (2004) · Journal of Virology · doi:10.1128/jvi.79.1.341-352.2005 · Cited by 229
Gene expression in insecticide resistant and susceptible Anopheles gambiae strains constitutively or after insecticide exposure
VONTAS, J. G., Blass, C., Koutsos, A. C., David, J., Kafatos, F. C., Louis, C., Hemingway, J., Christophides, G. K., & Ranson, H. (2005) · Insect Molecular Biology · doi:10.1111/j.1365-2583.2005.00582.x · Cited by 228
Rank aggregation methods
Lin, S. (2010) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.111 · Cited by 215
Microarray analysis of transcriptional responses to abscisic acid and osmotic, salt, and drought stress in the moss, Physcomitrella patens
Cuming, A. C., Cho, S. H., Kamisugi, Y., Graham, H., & Quatrano, R. S. (2007) · New Phytologist · doi:10.1111/j.1469-8137.2007.02187.x · Cited by 214
Nuclear Localization of the Nipah Virus W Protein Allows for Inhibition of both Virus- and Toll-Like Receptor 3-Triggered Signaling Pathways
Shaw, M. L., Cardenas, W. B., Zamarin, D., Palese, P., & Basler, C. F. (2005) · Journal of Virology · doi:10.1128/jvi.79.10.6078-6088.2005 · Cited by 209
Transcriptional characterizations of differences between eutopic and ectopic endometrium
Wu, Y., Kajdacsy-Balla, A., Strawn, E., Basir, Z., Halverson, G., Jailwala, P., Wang, Y., Wang, X., Ghosh, S., & Guo, S.-W. (2006) · Endocrinology · doi:10.1210/en.2005-0426 · Cited by 208
Comparative Toxicogenomic Analysis of the Hepatotoxic Effects of TCDD in Sprague Dawley Rats and C57BL/6 Mice
Boverhof, D. R., Burgoon, L. D., Tashiro, C. H. M., Sharratt, B., Chittim, B. G., Harkema, J. R., Mendrick, D. L., & Zacharewski, T. R. (2006) · Toxicological Sciences · doi:10.1093/toxsci/kfl100 · Cited by 203
Exploring the molecular basis of insecticide resistance in the dengue vector Aedes aegypti: a case study in Martinique Island (French West Indies)
Marcombe, S., Poupardin, R., Darriet, F., Reynaud, S., Bonnet, J., Strode, C., Brengues, C., Yébakima, A., Ranson, H., Corbel, V., et al. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-494 · Cited by 215
Cross-platform comparability of microarray technology: Intra-platform consistency and appropriate data analysis procedures are essential
Shi, L. M., Tong, W., Fang, H., Scherf, U., Han, J., Puri, R. K., Frueh, F. W., Goodsaid, F. M., Guo, L., Su, Z., et al. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-s2-s12 · Cited by 201
Support for involvement of neuregulin 1 in schizophrenia pathophysiology
Petryshen, T. L., Middleton, F. A., Kirby, A. W., Aldinger, K. A., Purcell, S., Tahl, A. R., Morley, C. P., McGann, L., Gentile, K. L., Rockwell, G. N., et al. (2004) · Molecular Psychiatry · doi:10.1038/sj.mp.4001608 · Cited by 196
Common and unique elements of the ABA-regulated transcriptome of Arabidopsis guard cells
Wang, R., Pandey, S., Song, L., Gookin, T. E., Zhao, Z., Albert, R., & Assmann, S. M. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-216 · Cited by 196
CXC chemokine receptors on human oligodendrocytes: implications for multiple sclerosis
Omari, K. M., John, G. R., Sealfon, S. C., & Raine, C. S. (2005) · Brain · doi:10.1093/brain/awh479 · Cited by 196
Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements
Mecham, B. H. (2004) · Nucleic Acids Research · doi:10.1093/nar/gnh071 · Cited by 195
Probing the ArcA-P Modulon of Escherichia coli by Whole Genome Transcriptional Analysis and Sequence Recognition Profiling
Liu, X., & Wulf, P. D. (2004) · Journal of Biological Chemistry · doi:10.1074/jbc.m313454200 · Cited by 193
Temporal and Dose-Dependent Hepatic Gene Expression Patterns in Mice Provide New Insights into TCDD-Mediated Hepatotoxicity
Boverhof, D. R., Burgoon, L. D., Tashiro, C. H. M., Chittim, B. G., Harkema, J. R., Jump, D. Β., & Zacharewski, T. R. (2005) · Toxicological Sciences · doi:10.1093/toxsci/kfi162 · Cited by 188
Are data from different gene expression microarray platforms comparable?
Järvinen, A., Hautaniemi, S., Edgren, H., Auvinen, P., Saarela, J., Kallioniemi, O., & Monni, O. M. (2004) · Genomics · doi:10.1016/j.ygeno.2004.01.004 · Cited by 188
Mapping networks of physical interactions between genomic elements using 5C technology
Dostie, J., & Dekker, J. (2007) · Nature Protocols · doi:10.1038/nprot.2007.116 · Cited by 189
Transcription profile of Kaposi's sarcoma-associated herpesvirus in primary Kaposi's sarcoma lesions as determined by real-time PCR arrays.
Dittmer, D. P. (2003) · PubMed · Cited by 185
A Versatile Assay for High-Throughput Gene Expression Profiling on Universal Array MatricesPreprint
Fan, J., Yeakley, J. M., Bibikova, M. V., Chudin, E., Wickham‐Garcia, E., Chen, J., Doucet, D., Rigault, P., Zhang, B., Shen, R., et al. (2004) · Genome Research · doi:10.1101/gr.2167504 · Cited by 182
Whole genome expression profiling reveals a significant role for immune function in human abdominal aortic aneurysms
Lenk, G. M., Tromp, G. C., Weinsheimer, S., Gatalica, Z., Berguer, R., & Kuivaniemi, H. (2007) · BMC Genomics · doi:10.1186/1471-2164-8-237 · Cited by 183
Spotted Long Oligonucleotide Arrays for Human Gene Expression AnalysisPreprint
Barczak, A. J., Rodriguez, M. W., Hanspers, K., Koth, L. L., Tai, Y. C., Bolstad, B. M., Speed, T. P., & Erle, D. J. (2003) · Genome Research · doi:10.1101/gr.1048803 · Cited by 179
Using RNA sample titrations to assess microarray platform performance and normalization techniques
Shippy, R., Fulmer-Smentek, S., Jensen, R. V., Jones, W. D., Wolber, P. K., Johnson, C. D., Pine, P. S., Boysen, C., Guo, X., Chudin, E., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1241 · Cited by 173
Comparison and meta-analysis of microarray data: from the bench to the computer desk
Moreau, Y., Aerts, S., Moor, B. D., Strooper, B. D., & Dąbrowski, M. (2003) · Trends in Genetics · doi:10.1016/j.tig.2003.08.006 · Cited by 171
Cytokine Response Is Determined by Duration of Receptor and Signal Transducers and Activators of Transcription 3 (STAT3) Activation
Braun, D. A., Fribourg, M., & Sealfon, S. C. (2013) · Journal of Biological Chemistry · doi:10.1074/jbc.m112.386573 · Cited by 168
“A robust global normalization algorithm, using expression levels of the housekeeping genes ribosomal protein S11 (rps11), -actin (actb), and -tubulin (tuba), was used for all experiments, as described elsewhere (15, 16).”
Method
Comparison of mRNA gene expression by RT-PCR and DNA microarray
Etienne, W., Meyer, M. H., Peppers, J., & Meyer, R. A. (2004) · BioTechniques · doi:10.2144/04364st02 · Cited by 122
A 1 Mb minimal amplicon at 8p11–12 in breast cancer identifies new candidate oncogenes
García, M. J., Pole, J. C. M., Chin, S., Teschendorff, A. E., Naderi, A., Özdağ, H., Vias, M., Kranjac, T., Subkhankulova, T., Paish, C. E., et al. (2005) · Oncogene · doi:10.1038/sj.onc.1208741 · Cited by 162
Method for multiplex cellular detection of mRNAs using quantum dot fluorescent in situ hybridization
Chan, P. (2005) · Nucleic Acids Research · doi:10.1093/nar/gni162 · Cited by 162
A sequence-oriented comparison of gene expression measurements across different hybridization-based technologies
Kuo, W. P., Liu, F., Trimarchi, J. M., Punzo, C., Lombardi, M. J., Sarang, J., Whipple, M., Maysuria, M., Serikawa, K. A., Lee, S. Y., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1217 · Cited by 161
Diagnostics based on nucleic acid sequence variant profiling: PCR, hybridization, and NGS approaches
Khodakov, D. A., Wang, C., & Zhang, D. Y. (2016) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2016.04.005 · Cited by 159
Combining Results of Microarray Experiments: A Rank Aggregation Approach
DeConde, R. P., Hawley, S. J., Falcon, S., Clegg, N. J., Knudsen, B. S., & Etzioni, R. (2006) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1204 · Cited by 154
Involvement of testicular growth factors in fetal Leydig cell aggregation after exposure to phthalate in utero
Lin, H. C., Ge, R., Chen, G., Hu, G., Dong, L., Lian, Q., Hardy, D. O., Sottas, C. M., Li, X., & Hardy, M. P. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0709260105 · Cited by 152
Variation in gene expression patterns as the insect pathogen Metarhizium anisopliae adapts to different host cuticles or nutrient deprivation in vitro
Freimoser, F. M., Hu, G., & Leger, R. J. S. (2005) · Microbiology · doi:10.1099/mic.0.27560-0 · Cited by 151
Biphasic Effects of Postnatal Exposure to Diethylhexylphthalate on the Timing of Puberty in Male Rats
Ge, R., Chen, G., Dong, Q. G., Akingbemi, B. T., Sottas, C. M., Santos, M. M., Sealfon, S. C., Bernard, D. J., & Hardy, M. P. (2007) · Journal of Andrology · doi:10.2164/jandrol.106.001909 · Cited by 149
Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations
Shippy, R., Sendera, T. J., Lockner, R., Palaniappan, C., Kaysser-Kranich, T., Watts, G. S., & Alsobrook, J. P. (2004) · BMC Genomics · doi:10.1186/1471-2164-5-61 · Cited by 147
Assessing unmodified 70-mer oligonucleotide probe performance on glass-slide microarrays
Wang, H.-Y., Malek, R. L., Kwitek, A. E., Greene, A. S., Luu, T. V., Babak, B., Frank, B. C., Quackenbush, J. F., & Lee, N. H. (2003) · Genome biology · doi:10.1186/gb-2003-4-1-r5 · Cited by 147
Microarray analysis of phosphate regulation in the marine cyanobacterium Synechococcus sp. WH8102
Tetu, S. G., Brahamsha, B., Johnson, D. A., Tai, V., Phillippy, K., Palenik, B. P., & Paulsen, I. T. (2009) · The ISME Journal · doi:10.1038/ismej.2009.31 · Cited by 149
Dosage-dependent over-expression of genes in the trisomic region of Ts1Cje mouse model for Down syndrome
Amano, K., Sago, H., Uchikawa, C., Suzuki, T., Kotliarova, S. E., Nukina, N., Epstein, C. J., & Yamakawa, K. (2004) · Human Molecular Genetics · doi:10.1093/hmg/ddh154 · Cited by 146
Gene expression profile analysis of Drosophila melanogaster selected for resistance to environmental stressors
Sørensen, J. G., Nielsen, M., & Loeschcke, V. (2007) · Journal of Evolutionary Biology · doi:10.1111/j.1420-9101.2007.01326.x · Cited by 144
Interleukin-11 Potentiates Oligodendrocyte Survival and Maturation, and Myelin Formation
Zhang, Y., Taveggia, C., Melendez‐Vasquez, C. V., Einheber, S., Raine, C. S., Salzer, J. L., Brosnan, C. F., & John, G. R. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.2289-06.2006 · Cited by 141
Sexual Dimorphism of Rat Liver Gene Expression: Regulatory Role of Growth Hormone Revealed by Deoxyribonucleic Acid Microarray Analysis
Ahluwalia, A., Clodfelter, K. H., & Waxman, D. J. (2003) · Molecular Endocrinology · doi:10.1210/me.2003-0138 · Cited by 137
Coordinated Regulation of Virulence during Systemic Infection of Salmonella enterica Serovar Typhimurium
Yoon, H., McDermott, J., Porwollik, S., McClelland, M., & Heffron, F. (2009) · PLoS Pathogens · doi:10.1371/journal.ppat.1000306 · Cited by 137
Quality Assessment for Short Oligonucleotide Microarray Data
Brettschneider, J. A., Collin, F., Bolstad, B. M., & Speed, T. P. (2008) · Technometrics · doi:10.1198/004017008000000334 · Cited by 132
A comparison of oligonucleotide and cDNA-based microarray systems
Mah, N., Thelin, A., Lu, T. T., Nikolaus, S., Kühbacher, T., Gürbüz, Y. S., Eickhoff, H., Klöppel, G., Lehrach, H., Mellgård, B., et al. (2004) · Physiological Genomics · doi:10.1152/physiolgenomics.00080.2003 · Cited by 133
Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements
Carter, S. L., Eklund, A. C., Mecham, B. H., Kohane, I. S., & Szállási, Z. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-107 · Cited by 131
Transcriptional profiling of myostatin‐knockout mice implicates Wnt signaling in postnatal skeletal muscle growth and hypertrophy
Steelman, C. A., Recknor, J. C., Nettleton, D. S., & Reecy, J. M. (2006) · The FASEB Journal · doi:10.1096/fj.05-5125fje · Cited by 133
Oxidative Stress and Atrial Fibrillation After Cardiac Surgery: A Case-Control Study
Ramlawi, B., Otu, H. H., Mieno, S., Boodhwani, M., Sodha, N. R., Clements, R., Bianchi, C. F., & Sellke, F. W. (2007) · The Annals of Thoracic Surgery · doi:10.1016/j.athoracsur.2007.04.126 · Cited by 127
Transcriptome Analysis of Human Colon Caco-2 Cells Exposed to Sulforaphane
Τράκα, Μ., Gasper, A. V., Smith, J. A., Hawkey, C. J., Bao, Y., & Mithen, R. F. (2005) · Journal of Nutrition · doi:10.1093/jn/135.8.1865 · Cited by 127
Microarrays: the use of oligonucleotides and cDNA for the analysis of gene expression
Barrett, J. C., & Kawasaki, E. S. (2003) · Drug Discovery Today · doi:10.1016/s1359-6446(02)02578-3 · Cited by 127
Salicylic Acid-Dependent Expression of Host Genes in Compatible Arabidopsis-Virus Interactions
Huang, Z., Yeakley, J. M., Garcia, E. W., Holdridge, J. D., Fan, J., & Whitham, S. A. (2005) · PLANT PHYSIOLOGY · doi:10.1104/pp.104.056028 · Cited by 126
Transcriptome analysis of Arabidopsis colonized by a plant‐growth promoting rhizobacterium reveals a general effect on disease resistance
Cartieaux, F., Thibaud, M., Zimmerli, L., Lessard, P., Sarrobert, C., David, P., Gerbaud, A., Robaglia, C., Somerville, S. C., & Nussaume, L. (2003) · The Plant Journal · doi:10.1046/j.1365-313x.2003.01867.x · Cited by 125
Alterations of gene expression in failing myocardium following left ventricular assist device support
Chen, Y., Park, S., Li, Y., Missov, E. D., Hou, M., Han, X., Hall, J. L., Miller, L. W., & Bache, R. J. (2003) · Physiological Genomics · doi:10.1152/physiolgenomics.00022.2003 · Cited by 125
Differences in gene expression between B-cell chronic lymphocytic leukemia and normal B cells: a meta-analysis of three microarray studies
Wang, J., Coombes, K. R., Highsmith, W. E., Keating, M., & Abruzzo, L. V. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth381 · Cited by 125
DNA microarray technology for the microbiologist: an overview
Ehrenreich, A. (2006) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-006-0584-2 · Cited by 123
Molecular Pathways Associated with Stress Resilience and Drug Resistance in the Chronic Mild Stress Rat Model of Depression—a Gene Expression Study
Bergström, A., Jayatissa, M. N., Thykjær, T., & Wiborg, O. (2007) · Journal of Molecular Neuroscience · doi:10.1007/s12031-007-0065-9 · Cited by 126
Use of a Low-Density Microarray for Studying Gene Expression Patterns Induced by Hepatotoxicants on Primary Cultures of Rat Hepatocytes
de Longueville, F. (2003) · Toxicological Sciences · doi:10.1093/toxsci/kfg196 · Cited by 123
Microarray scanner calibration curves: characteristics and implications
Shi, L. M., Tong, W., Su, Z., Han, T., Han, J., Puri, R. K., Fang, H., Frueh, F. W., Goodsaid, F. M., Guo, L., et al. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-s2-s11 · Cited by 121
A comparison of cDNA, oligonucleotide, and Affymetrix GeneChip gene expression microarray platforms.
Woo, Y., Affourtit, J. P., Daigle, S. L., Viale, A. J., Johnson, K., Naggert, J. Κ., & Churchill, G. A. (2004) · PubMed Central · Cited by 121
Whole-Genome Gene Expression Profiling of Formalin-Fixed, Paraffin-Embedded Tissue Samples
April, C., Klotzle, B., Royce, T. E., Wickham‐Garcia, E., Boyaniwsky, T., Izzo, J., Cox, D. J., Jones, W. D., Rubio, R., Holton, K. M., et al. (2009) · PLoS ONE · doi:10.1371/journal.pone.0008162 · Cited by 120
Microarray analysis and scale-free gene networks identify candidate regulators in drought-stressed roots of loblolly pine (P. taeda L.)
Lorenz, W. W., Alba, R., Yu, Y.-S., Bordeaux, J. M., Simões, M., & Dean, J. F. D. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-264 · Cited by 117
The KDM3A–KLF2–IRF4 axis maintains myeloma cell survival
Ohguchi, H., Hideshima, T., Bhasin, M., Görgün, G., Santo, L., Cea, M., Samur, M., Mimura, N., Suzuki, R., Tai, Y.-T., et al. (2016) · Nature Communications · doi:10.1038/ncomms10258 · Cited by 116
A Novel Role for Viral-Defective Interfering Particles in Enhancing Dendritic Cell Maturation
Yount, J. S., Kraus, T., Horvath, C. M., Moran, T. M., & López, C. B. (2006) · The Journal of Immunology · doi:10.4049/jimmunol.177.7.4503 · Cited by 115
Triple Signal Amplification Strategy for Ultrasensitive Determination of miRNA Based on Duplex Specific Nuclease and Bridge DNA–Gold Nanoparticles
Bo, B., Zhang, T., Jiang, Y., Cui, H., & Miao, P. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.7b05447 · Cited by 115
Biphenyl and Benzoate Metabolism in a Genomic Context: Outlining Genome-Wide Metabolic Networks in Burkholderia xenovorans LB400
Denef, V. J., Park, J., Tsoi, T. V., Rouillard, J.-M., Zhang, H., Wibbenmeyer, J. A., Verstraete, W., Gülari, E., Hashsham, S. A., & Tiedje, J. M. (2004) · Applied and Environmental Microbiology · doi:10.1128/aem.70.8.4961-4970.2004 · Cited by 114

Authors: Yuen, T.
Affiliations: Icahn School of Medicine at Mount Sinai (US)
Corresponding author: Yuen, T.
ORCID: Yuen, T.
Venue: Nucleic Acids Research
Publisher: Oxford University Press (OUP)
Published: 2002-05-15
Volume: 30
Issue: 10
Pages: 48e-48
ISSN: 0305-1048 (ISSN-L)
1362-4962
DOI: 10.1093/nar/30.10.e48
PMC: PMC115302
OpenAlex: W2098453858
Cited by: 520
References: 20
Citations by year: 2026: 1 · 2025: 2 · 2023: 7 · 2022: 1 · 2020: 1 · 2019: 2 · 2018: 3 · 2017: 5 · 2016: 11 · 2015: 5 · 2014: 12 · 2013: 15 · 2012: 16
FWCI: 22.54
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Nucleic Acids Research (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: National Institutes of Health (DK46943, RO1 DK46943)
National Institute on Drug Abuse (P01DA012923, P01-DA12923)
National Institute of Diabetes and Digestive and Kidney Diseases (R01 DK 46943., R01DK046943)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords: oligonucleotide microarrays, cDNA microarray, measurement bias, gene expression profiling, Affymetrix GeneChip, calibration function, mRNA expression
MeSH terms: Animals, Calibration, Cell Line, Gene Expression Regulation, RNA, Sensitivity and Specificity, Transcription, Genetic, Reproducibility of Results, Reverse Transcriptase Polymerase Chain Reaction, Oligonucleotide Array Sequence Analysis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Accuracy and calibration of commercial oligonucleotide and custom cDNA microarrays

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks