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2013 | 60 | 4 |

Tytuł artykułu

Microarray Inspector: tissue cross contamination detection tool for microarray data

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
Microarray technology changed the landscape of contemporary life sciences by providing vast amounts of expression data. Researchers are building up repositories of experiment results with various conditions and samples which serve the scientific community as a precious resource. Ensuring that the sample is of high quality is of utmost importance to this effort. The task is complicated by the fact that in many cases datasets lack information concerning pre-experimental quality assessment. Transcription profiling of tissue samples may be invalidated by an error caused by heterogeneity of the material. The risk of tissue cross contamination is especially high in oncological studies, where it is often difficult to extract the sample. Therefore, there is a need of developing a method detecting tissue contamination in a post-experimental phase. We propose Microarray Inspector: customizable, user-friendly software that enables easy detection of samples containing mixed tissue types. The advantage of the tool is that it uses raw expression data files and analyses each array independently. In addition, the system allows the user to adjust the criteria of the analysis to conform to individual needs and research requirements. The final output of the program contains comfortable to read reports about tissue contamination assessment with detailed information about the test parameters and results. Microarray Inspector provides a list of contaminant biomarkers needed in the analysis of adipose tissue contamination. Using real data (datasets from public repositories) and our tool, we confirmed high specificity of the software in detecting contamination. The results indicated the presence of adipose tissue admixture in a range from approximately 4% to 13% in several tested surgical samples.

Słowa kluczowe

Wydawca

-

Rocznik

Tom

60

Numer

4

Opis fizyczny

p.647-655,fig.,ref.

Twórcy

autor
  • Transition Technologies, Warszawa, Poland
autor
  • Transition Technologies, Warszawa, Poland
autor
  • Transition Technologies, Warszawa, Poland
  • Institute of Heat Engineering, Warsaw University of Technology, Warszawa, Poland
autor
  • Transition Technologies, Warszawa, Poland
  • Department of Gastroenterology and Hepatology, Medical Center for Postgraduate Education, Warsaw, Poland
autor
  • Transition Technologies, Warszawa, Poland
  • Institute of Physics PAS, Warszawa, Poland
  • Laboratory of Bioinformatics and Biostatistics, Maria Skłodowska-Curie Memorial Cancer Center and Institute of Oncology, Warszawa, Poland
  • Laboratory of Bioinformatics and Biostatistics, Maria Skłodowska-Curie Memorial Cancer Center and Institute of Oncology, Warszawa, Poland
autor
  • Transition Technologies, Warszawa, Poland
  • Institute of Heat Engineering, Warsaw University of Technology, Warszawa, Poland

Bibliografia

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  • Bolstad BM, Collin F, Simpson KM, Irizarry RA, Speed TP (2004) Experimental design and low-level analysis of microarray data. Int Rev Neurobiol 60: 25-58. 
  • Chen JJ, Hsueh HM, Delongchamp RR, Lin CJ, Tsai CA (2007) Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data. BMC Bioinformatics 8: 412. 
  • Chunlei Wu, Orozco C, Boyer J, Leglise M, Goodale J, Batalov S, Hodge CL, Haase J, Janes J, Huss JW 3rd, Su AI (2009) BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol 10: R130. 
  • Clarke J, Seo P, Clarke B (2010) Statistical expression deconvolution from mixed tissue samples. Bioinformatics (Oxford, England) 26: 1043-1049. 
  • Dupuy A, Simon RM (2007) Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Nat Cancer Institute 99: 147-157. 
  • Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30: 207-210. 
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  • Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP (2003) Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 31: e15. 
  • Ji H, Davis RW (2006) Data quality in genomics and microarrays. Nat Biotechnol 24: 1112-1113. 
  • Lähdesmäki H, Shmulevich L, Dunmire V, Yli-Harja O, Zhang W (2005) In silico microdissection of microarray data from heterogeneous cell populations. BMC Bioinformatics 6: 54. 
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  • McCall MN, Murakami PN, Lukk M, Huber W, Irizarry R a (2011) Assessing affymetrix GeneChip microarray quality. BMC Bioinformatics 12: 137. 
  • Michiels S, Koscielny S, Hill C (2005) Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 365: 488-492. 
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  • She X, Rohl C a, Castle JC, Kulkarni A V, Johnson JM, Chen R (2009) Definition, conservation and epigenetics of housekeeping and tissue-enriched genes. BMC Genomics 10: 269. 
  • Shi L, Tong W, Goodsaid F, Frueh FW, Fang H, Han T, Fuscoe JC, Casciano DA (2004) QA/QC: challenges and pitfalls facing the microarray community and regulatory agencies. Expert Rev Mol Diagnostics 4: 761-777. 
  • Shi L, Reid LH, Jones WD et al. (2006) The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 24: 1151-1161. 
  • Shi L, Campbell G, Jones W, Campagne F (2010) The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat Biotechnol 28: 827-838. 
  • Skrzypczak M, Goryca K, Rubel T, Paziewska A, Mikula M, Jarosz D, Pachlewski J, Oledzki J, Ostrowski J, Ostrowsk J (2010) Modeling oncogenic signaling in colon tumors by multidirectional analyses of microarray data directed for maximization of analytical reliability. PloS One 5: e13091. 
  • Tan PK (2003) Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res 31: 5676-5684. 
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  • Wang M, Master SR, Chodosh L a (2006) Computational expression deconvolution in a complex mammalian organ. BMC Bioinformatics 7: 328. 
  • Wang Y, Xia XQ, Jia Z, Sawyers A, Yao H, Wang-Rodriquez J, Mercola D, McClelland M (2010) In silico estimates of tissue components in surgical samples based on expression profiling data. Cancer Res 70: 6448-6455. 
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Typ dokumentu

Bibliografia

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