title: A regression system for estimation of errors introduced by confocal imaging into gene expression data in situ.
abstract: BACKGROUND
  Accuracy of the data extracted from two-dimensional confocal images is limited due to experimental errors that arise in course of confocal scanning. The common way to reduce the noise in images is sequential scanning of the same specimen several times with the subsequent averaging of multiple frames. Attempts to increase the dynamical range of an image by setting too high values of microscope PMT parameters may cause clipping of single frames and introduce errors into the data extracted from the averaged images. For the estimation and correction of this kind of errors a method based on censoring technique (Myasnikova et al., 2009) is used. However, the method requires the availability of all the confocal scans along with the averaged image, which is normally not provided by the standard scanning procedure.
  
  
  RESULTS
  To predict error size in the data extracted from the averaged image we developed a regression system. The system is trained on the learning sample composed of images obtained from three different microscopes at different combinations of PMT parameters, and for each image all the scans are saved. The system demonstrates high prediction accuracy and was applied for correction of errors in the data on segmentation gene expression in Drosophila blastoderm stored in the FlyEx database (http://urchin.spbcas.ru/flyex/, http://flyex.uchicago.edu/flyex/). The prediction method is realized as a software tool CorrectPattern freely available at http://urchin.spbcas.ru/asp/2011/emm/.
  
  
  CONCLUSIONS
  We created a regression system and software to predict the magnitude of errors in the data obtained from a confocal image based on information about microscope parameters used for the image acquisition. An important advantage of the developed prediction system is the possibility to accurately correct the errors in data obtained from strongly clipped images, thereby allowing to obtain images of the higher dynamical range and thus to extract more detailed quantitative information from them.
authors.affiliation: Department of Computational Biology, Center for Advanced Studies, St.Petersburg State Polytechnical University, St.Petersburg, 195251, Russia. myasnikova@spbcas.ru
authors.forename: Ekaterina
authors.initials: E
authors.lastname: Myasnikova
authors.forename: Svetlana
authors.initials: S
authors.lastname: Surkova
authors.forename: Grigory
authors.initials: G
authors.lastname: Stein
authors.forename: Andrei
authors.initials: A
authors.lastname: Pisarev
authors.forename: Maria
authors.initials: M
authors.lastname: Samsonova
mesh_terms: D000818:Animals; D001756:Blastoderm / Q000378:metabolism*; D029721:Drosophila Proteins / Q000235:genetics / Q000378:metabolism; D004331:Drosophila melanogaster / Q000235:genetics* / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods; D018613:Microscopy, Confocal / Q000379:methods*; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D012984:Software
chemical_list: D029721:Drosophila Proteins