{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T15:58:14Z","timestamp":1769875094939,"version":"3.49.0"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Background: One of the significant obstacles in the development of clinically relevant microarray-derived biomarkers and classifiers is tissue heterogeneity. Physical cell separation techniques, such as cell sorting and laser-capture microdissection, can enrich samples for cell types of interest, but are costly, labor intensive and can limit investigation of important interactions between different cell types.<\/jats:p>\n               <jats:p>Results: We developed a new computational approach, called microarray microdissection with analysis of differences (MMAD), which performs microdissection in silico. Notably, MMAD (i) allows for simultaneous estimation of cell fractions and gene expression profiles of contributing cell types, (ii) adjusts for microarray normalization bias, (iii) uses the corrected Akaike information criterion during model optimization to minimize overfitting and (iv) provides mechanisms for comparing gene expression and cell fractions between samples in different classes. Computational microdissection of simulated and experimental tissue mixture datasets showed tight correlations between predicted and measured gene expression of pure tissues as well as tight correlations between reported and estimated cell fraction for each of the individual cell types. In simulation studies, MMAD showed superior ability to detect differentially expressed genes in mixed tissue samples when compared with standard metrics, including both significance analysis of microarrays and cell type-specific significance analysis of microarrays.<\/jats:p>\n               <jats:p>Conclusions: We have developed a new computational tool called MMAD, which is capable of performing robust tissue microdissection in silico, and which can improve the detection of differentially expressed genes. MMAD software as implemented in MATLAB is publically available for download at http:\/\/sourceforge.net\/projects\/mmad\/.<\/jats:p>\n               <jats:p>Contact: \u00a0david.liebner@gmail.com<\/jats:p>\n               <jats:p>Supplementary Information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt566","type":"journal-article","created":{"date-parts":[[2013,10,2]],"date-time":"2013-10-02T00:44:01Z","timestamp":1380674641000},"page":"682-689","source":"Crossref","is-referenced-by-count":68,"title":["MMAD: microarray microdissection with analysis of differences is a computational tool for deconvoluting cell type-specific contributions from tissue samples"],"prefix":"10.1093","volume":"30","author":[{"given":"David A.","family":"Liebner","sequence":"first","affiliation":[{"name":"1 Division of Medical Oncology, Department of Internal Medicine, 2Department of Biomedical Informatics and 3Comprehensive Cancer Center, Biomedical Informatics Shared Resource, The Ohio State University, Columbus OH 43210, USA"},{"name":"1 Division of Medical Oncology, Department of Internal Medicine, 2Department of Biomedical Informatics and 3Comprehensive Cancer Center, Biomedical Informatics Shared Resource, The Ohio State University, Columbus OH 43210, USA"}]},{"given":"Kun","family":"Huang","sequence":"additional","affiliation":[{"name":"1 Division of Medical Oncology, Department of Internal Medicine, 2Department of Biomedical Informatics and 3Comprehensive Cancer Center, Biomedical Informatics Shared Resource, The Ohio State University, Columbus OH 43210, USA"},{"name":"1 Division of Medical Oncology, Department of Internal Medicine, 2Department of Biomedical Informatics and 3Comprehensive Cancer Center, Biomedical Informatics Shared Resource, The Ohio State University, Columbus OH 43210, USA"}]},{"given":"Jeffrey D.","family":"Parvin","sequence":"additional","affiliation":[{"name":"1 Division of Medical Oncology, Department of Internal Medicine, 2Department of Biomedical Informatics and 3Comprehensive Cancer Center, Biomedical Informatics Shared Resource, The Ohio State University, Columbus OH 43210, USA"},{"name":"1 Division of Medical Oncology, Department of Internal Medicine, 2Department of Biomedical Informatics and 3Comprehensive Cancer Center, Biomedical Informatics Shared Resource, The Ohio State University, Columbus OH 43210, USA"}]}],"member":"286","published-online":{"date-parts":[[2013,10,1]]},"reference":[{"key":"2023012710430393700_btt566-B1","doi-asserted-by":"crossref","first-page":"e6098","DOI":"10.1371\/journal.pone.0006098","article-title":"Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus","volume":"4","author":"Abbas","year":"2009","journal-title":"PLoS One"},{"key":"2023012710430393700_btt566-B2","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1093\/bioinformatics\/btt301","article-title":"Demix: deconvolution for mixed cancer transcriptomes using raw measured data","volume":"29","author":"Ahn","year":"2013","journal-title":"Bioinformatics"},{"key":"2023012710430393700_btt566-B3","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1038\/35000501","article-title":"Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling","volume":"403","author":"Alizadeh","year":"2000","journal-title":"Nature"},{"key":"2023012710430393700_btt566-B4","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1038\/nm733","article-title":"gene-expression profiles predict survival of patients with lung adenocarcinoma","volume":"8","author":"Beer","year":"2002","journal-title":"Nat. Med."},{"key":"2023012710430393700_btt566-B5","first-page":"1184","article-title":"Molecular classification of head and neck squamous cell carcinoma using cDNA microarrays","volume":"62","author":"Belbin","year":"2002","journal-title":"Cancer Res."},{"key":"2023012710430393700_btt566-B6","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1038\/35020115","article-title":"Molecular classification of cutaneous malignant melanoma by gene expression profiling","volume":"406","author":"Bittner","year":"2000","journal-title":"Nature"},{"key":"2023012710430393700_btt566-B7","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1186\/1471-2105-12-258","article-title":"Cell subset prediction for blood genomic studies","volume":"12","author":"Bolen","year":"2011","journal-title":"BMC Bioinformatics"},{"key":"2023012710430393700_btt566-B8","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1093\/bioinformatics\/19.2.185","article-title":"A comparison of normalization methods for high density oligonucleotide array data based on variance and bias","volume":"19","author":"Bolstad","year":"2003","journal-title":"Bioinformatics"},{"key":"2023012710430393700_btt566-B9","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1186\/1471-2105-10-61","article-title":"A comparison on effects of normalisations in the detection of differentially expressed genes","volume":"10","author":"Chiogna","year":"2009","journal-title":"BMC Bioinformatics"},{"key":"2023012710430393700_btt566-B10","doi-asserted-by":"crossref","first-page":"r32","DOI":"10.1186\/bcr1506","article-title":"The effect of the stromal component of breast tumours on prediction of clinical outcome using gene expression microarray analysis","volume":"8","author":"Cleator","year":"2006","journal-title":"Breast Cancer Res."},{"key":"2023012710430393700_btt566-B11","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1038\/sj.tpj.6500240","article-title":"Comparison of different isolation techniques prior gene expression profiling of blood derived cells: impact on physiological responses, on overall expression and the role of different cell types","volume":"4","author":"Debey","year":"2004","journal-title":"Pharmacogenomics J."},{"key":"2023012710430393700_btt566-B12","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1038\/35090585","article-title":"Delineation of prognostic biomarkers in prostate cancer","volume":"412","author":"Dhanasekaran","year":"2001","journal-title":"Nature"},{"key":"2023012710430393700_btt566-B13","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/1755-8794-4-54","article-title":"Systematic bias in genomic classification due to contaminating non-neoplastic tissue in breast tumor samples","volume":"4","author":"Elloumi","year":"2011","journal-title":"BMC Med. Genomics"},{"key":"2023012710430393700_btt566-B14","doi-asserted-by":"crossref","first-page":"2571","DOI":"10.1093\/bioinformatics\/btq406","article-title":"Probabilistic analysis of gene expression measurements from heterogeneous tissues","volume":"26","author":"Erkkila","year":"2010","journal-title":"Bioinformatics"},{"key":"2023012710430393700_btt566-B15","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1152\/physiolgenomics.00020.2004","article-title":"Whole blood and leukocyte rna isolation for gene expression analyses","volume":"19","author":"Feezor","year":"2004","journal-title":"Physiol. Genomics"},{"key":"2023012710430393700_btt566-B16","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1016\/j.meegid.2011.08.014","article-title":"Semi-supervised nonnegative matrix factorization for gene expression deconvolution: a case study","volume":"12","author":"Gaujoux","year":"2012","journal-title":"Infect. Genet. Evol."},{"key":"2023012710430393700_btt566-B17","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1093\/bioinformatics\/btt351","article-title":"Cellmix: a comprehensive toolbox for gene expression deconvolution","volume":"29","author":"Gaujoux","year":"2013","journal-title":"Bioinformatics"},{"key":"2023012710430393700_btt566-B18","doi-asserted-by":"crossref","first-page":"e27156","DOI":"10.1371\/journal.pone.0027156","article-title":"Optimal deconvolution of transcriptional profiling data using quadratic programming with application to complex clinical blood samples","volume":"6","author":"Gong","year":"2011","journal-title":"PLoS One"},{"key":"2023012710430393700_btt566-B19","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1093\/bioinformatics\/btt090","article-title":"Deconrnaseq: a statistical framework for deconvolution of heterogeneous tissue samples based on mrna-seq data","volume":"29","author":"Gong","year":"2013","journal-title":"Bioinformatics"},{"key":"2023012710430393700_btt566-B20","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1093\/bioinformatics\/18.12.1585","article-title":"Robust estimators for expression analysis","volume":"18","author":"Hubbell","year":"2002","journal-title":"Bioinformatics"},{"key":"2023012710430393700_btt566-B21","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1093\/biomet\/76.2.297","article-title":"Regression and time series model selection in small samples","volume":"76","author":"Hurvich","year":"1989","journal-title":"Biometrika"},{"key":"2023012710430393700_btt566-B22","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/1471-2105-6-54","article-title":"In silico microdissection of microarray data from heterogeneous cell populations","volume":"6","author":"Lahdesmaki","year":"2005","journal-title":"BMC Bioinformatics"},{"key":"2023012710430393700_btt566-B23","doi-asserted-by":"crossref","first-page":"10370","DOI":"10.1073\/pnas.1832361100","article-title":"Expression deconvolution: a reinterpretation of DNA microarray data reveals dynamic changes in cell populations","volume":"100","author":"Lu","year":"2003","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012710430393700_btt566-B24","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1186\/1471-2407-10-222","article-title":"A genomic and transcriptomic approach for a differential diagnosis between primary and secondary ovarian carcinomas in patients with a previous history of breast cancer","volume":"10","author":"Meyniel","year":"2010","journal-title":"BMC Cancer"},{"key":"2023012710430393700_btt566-B25","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1038\/35021093","article-title":"Molecular portraits of human breast tumours","volume":"406","author":"Perou","year":"2000","journal-title":"Nature"},{"key":"2023012710430393700_btt566-B26","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/1471-2105-11-27","article-title":"Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach","volume":"11","author":"Repsilber","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"2023012710430393700_btt566-B27","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1038\/nmeth.1439","article-title":"Cell type-specific gene expression differences in complex tissues","volume":"7","author":"Shen-Orr","year":"2010","journal-title":"Nat. Methods"},{"key":"2023012710430393700_btt566-B28","doi-asserted-by":"crossref","first-page":"10869","DOI":"10.1073\/pnas.191367098","article-title":"Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications","volume":"98","author":"Sorlie","year":"2001","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012710430393700_btt566-B29","doi-asserted-by":"crossref","first-page":"e1000307","DOI":"10.1371\/journal.pmed.1000307","article-title":"A six-gene signature predicts survival of patients with localized pancreatic ductal adenocarcinoma","volume":"7","author":"Stratford","year":"2010","journal-title":"PLoS Med."},{"key":"2023012710430393700_btt566-B30","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1073\/pnas.2536479100","article-title":"In silico dissection of cell-type-associated patterns of gene expression in prostate cancer","volume":"101","author":"Stuart","year":"2004","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012710430393700_btt566-B31","doi-asserted-by":"crossref","first-page":"6062","DOI":"10.1073\/pnas.0400782101","article-title":"A gene atlas of the mouse and human protein-encoding transcriptomes","volume":"101","author":"Su","year":"2004","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012710430393700_btt566-B32","doi-asserted-by":"crossref","first-page":"e46331","DOI":"10.1371\/journal.pone.0046331","article-title":"A framework for regularized non-negative matrix factorization, with application to the analysis of gene expression data","volume":"7","author":"Taslaman","year":"2012","journal-title":"PLoS One"},{"key":"2023012710430393700_btt566-B33","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1038\/nature08486","article-title":"Pten in stromal fibroblasts suppresses mammary epithelial tumours","volume":"461","author":"Trimboli","year":"2009","journal-title":"Nature"},{"key":"2023012710430393700_btt566-B34","doi-asserted-by":"crossref","first-page":"5116","DOI":"10.1073\/pnas.091062498","article-title":"Significance analysis of microarrays applied to the ionizing radiation response","volume":"98","author":"Tusher","year":"2001","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012710430393700_btt566-B35","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1038\/415530a","article-title":"Gene expression profiling predicts clinical outcome of breast cancer","volume":"415","author":"van \u2018t Veer","year":"2002","journal-title":"Nature"},{"key":"2023012710430393700_btt566-B36","doi-asserted-by":"crossref","first-page":"S279","DOI":"10.1093\/bioinformatics\/17.suppl_1.S279","article-title":"Separation of samples into their constituents using gene expression data","volume":"17","author":"Venet","year":"2001","journal-title":"Bioinformatics"},{"key":"2023012710430393700_btt566-B37","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1186\/1471-2105-7-328","article-title":"Computational expression deconvolution in a complex mammalian organ","volume":"7","author":"Wang","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2023012710430393700_btt566-B38","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1038\/nmeth.1830","article-title":"Gene expression deconvolution in linear space","volume":"9","author":"Zhong","year":"2012","journal-title":"Nat. Methods"},{"key":"2023012710430393700_btt566-B39","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1186\/1471-2105-14-89","article-title":"Digital sorting of complex tissues for cell type-specific gene expression profiles","volume":"14","author":"Zhong","year":"2013","journal-title":"BMC Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/30\/5\/682\/48918142\/bioinformatics_30_5_682.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/30\/5\/682\/48918142\/bioinformatics_30_5_682.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T11:03:04Z","timestamp":1674817384000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/30\/5\/682\/244931"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,10,1]]},"references-count":39,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2014,3,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btt566","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2014,3,1]]},"published":{"date-parts":[[2013,10,1]]}}}