Fully automatic quantification of microarray image data.

By Ajay N. Jain, T. Tokuyasu, Antoine Snijders, R. Segraves, Donna Albertson, Dan Pinkel. Genome Research, 2002

363 citations recorded by the source corpus.

DNA microarrays are now widely used to measure expression levels and DNA copy number in biological samples. Ratios of relative abundance of nucleic acids are derived from images of regular arrays of spots containing target genetic material to which fluorescently labeled samples are hybridized. Whereas there are a number of methods in use for the quantification of images, many of the software systems in wide use either encourage or require extensive human interaction at the level of individual spots on arrays. We present a fully automatic system for microarray image quantification. The system automatically locates both subarray grids and individual spots, requiring no user identification of any image coordinates. Ratios are computed based on explicit segmentation of each spot. On a typical image of 6000 spots, the entire process takes less than 20 sec. We present a quantitative assessment of performance on multiple replicates of genome-wide array-based comparative genomic hybridization experiments. By explicitly identifying the pixels in each spot, the system yields more accurate estimates of ratios than systems assuming spot circularity. The software, called, runs on Windows platforms and is available free of charge for academic use.

Subfields: Genetics, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/2c0107fad46a1e3ac7cc450928a6ca776ef02dd6

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Citations in the corpus

A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. (2006, methodology): “Array CGH data image analyses were performed as described previously (Jain et al., 2002).”
Segmental duplications and copy-number variation in the human genome. (2005, methodology): “Array image analysis and normalization were performed using UCSF Spot and Sproc software (Jain et al. 2002).”
Development of query strategies to identify a histologic lymphoma subtype in a large linked database system. (2006, neutral): “The localization of spots on the array is a semi-automatic process supported by “spot ﬁ nding” functions, available in most microarray scanner software packages and custom packages (Jain et al. 2002).”
Hidden Markov models approach to the analysis of array CGH data (2004, methodology): “[19] and quantified using custom image analysis software Spot [9].”
Fundamentals of cDNA microarray data analysis. (2003, methodology): “phenylindole (DAPI) and the counterstained image used to assist in the segmentation process [ 10 ].”
Analysis of array CGH data: from signal ratio to gain and loss of DNA regions (2004, methodology): “Inthis study, we assume that signal ratios for each BAC, such as those provided by SPOT 2.0 (Jain et al., 2002) or GenePix (Axon Instruments, 2003) software, are available and we focus on the problem of identifying the regions gained and lost from the ratio proﬁle.”
bcp: An R Package for Performing a Bayesian Analysis of Change Point Problems (2007, methodology): “UCSF SPOT software (Jain et al. 2002) was used to calculate the log 2 ratio ratios of the red-green signal intensities for each spot on the arrays, and to perform local background correction.”