The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models
Open accessAbstract
The determination of a list of differentially expressed genes is a basic objective in many cDNA microarray experiments. We present a statistical approach that allows direct control over the percentage of false positives in such a list and, under certain reasonable assumptions, improves on existing methods with respect to the percentage of false negatives. The method accommodates a wide variety of experimental designs and can simultaneously assess significant differences between multiple types of biological samples. Two interconnected mixed linear models are central to the method and provide a flexible means to properly account for variability both across and within genes. The mixed model also provides a convenient framework for evaluating the statistical power of any particular experimental design and thus enables a researcher to a priori select an appropriate number of replicates. We also suggest some basic graphics for visualizing lists of significant genes. Analyses of published experiments studying human cancer and yeast cells illustrate the results.
Overview
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
Cites
Statistics
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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 16,184
Power Calculations for General Linear Multivariate Models Including Repeated Measures Applications
Muller, K. E., Lavange, L. M., Ramey, S. L., & Ramey, C. T. (1992) · Journal of the American Statistical Association · doi:10.1080/01621459.1992.10476281 · Cited by 230
Cited by
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
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
Sartor, M. A., Tomlinson, C. R., Wesselkamper, S. C., Sivaganesan, S., Leikauf, G. D., & Medvedovic, M. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-538 · Cited by 263
Geographical genomics of human leukocyte gene expression variation in southern Morocco
Idaghdour, Y., Czika, W., Shianna, K. V., Lee, S. H., Visscher, P. M., Martin, H. C., Miclaus, K., Jadallah, S. J., Goldstein, D. B., Wolfinger, R. D., et al. (2010) · Nature Genetics · doi:10.1038/ng.495 · Cited by 151
Self-citationSmeianov, V. V., Wechter, P., Broadbent, J. R., Hughes, J. E., Rodríguez, B. T., Christensen, T. K., Ardö, Y., & Steele, J. L. (2007) · Applied and Environmental Microbiology · doi:10.1128/aem.00005-07 · Cited by 97
Cryptococcus neoformans Histone Acetyltransferase Gcn5 Regulates Fungal Adaptation to the Host
O'Meara, T. R., Hay, C., Price, M. S., Giles, S., & Alspaugh, J. A. (2010) · Eukaryotic Cell · doi:10.1128/ec.00098-10 · Cited by 88
Blood-informative transcripts define nine common axes of peripheral blood gene expression
Preininger, M., Arafat, D., Kim, J., Nath, A. P., Idaghdour, Y., Brigham, K. L., & Gibson, G. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003362 · Cited by 59
Self-citationCash, A. C., Whitfield, C. W., Ismail, N., & Robinson, G. E. (2005) · Genes, Brain and Behavior · doi:10.1111/j.1601-183x.2005.00131.x · Cited by 47
Genomic and Proteomic Analysis of Myocarditis and Dilated Cardiomyopathy
Cooper, L. T., Onuma, O. K., Sagar, S., Oberg, A. L., Mahoney, D. W., Asmann, Y. W., & Liu, P. (2010) · Heart Failure Clinics · doi:10.1016/j.hfc.2009.08.012 · Cited by 30
Metadata
- Authors
- Wolfinger, R. D., Gibson, G., Wolfinger, E. D., Bennett, L., Hamadeh, H., Bushel, P., Afshari, C., & Paules, R. S.
- Venue
- Journal of Computational Biology
- Publisher
- SAGE Publications
- Published
- 2001-11
- Volume
- 8
- Issue
- 6
- Pages
- 625-637
- ISSN
- 1066-5277 (ISSN-L)
- 1557-8666
- OpenAlex
- W2002810210
- Cited by
- 1,117
- References
- 2
- FWCI
- 26.71
- Type
- journal article
- Language
- en
- Open access
- green
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
