Applications of Multilevel Thresholding Algorithms to Transcriptomics Data
Microarrays are one of the methods for analyzing the expression levels of genes in a massive and parallel way. Since any errors in early stages of the analysis affect subsequent stages, leading to possibly erroneous biological conclusions, finding the correct location of the spots in the images is extremely important for subsequent steps that include segmentation, quantification, normalization and clustering. On the other hand, genome-wide profiling of DNA-binding proteins using ChIP-seq and RNA-seq has emerged as an alternative to ChIP-chip methods. Due to the large amounts of data produced by next generation sequencing technology, ChIPseq and RNA-seq offer much higher resolution, less noise and greater coverage than its predecessor, the ChIPchip array.
Multilevel thresholding algorithms have been applied to many problems in image and signal processing. We show that these algorithms can be used for transcriptomics and genomics data analysis such as sub-grid and spot detection in DNA microarrays, and also for detecting significant regions based on next generation sequencing data. We show the advantages and disadvantages of using multilevel thresholding and other algorithms in these two applications, as well as an overview of numerical and visual results used to validate the power of the thresholding methods based on previously published data.
Keywordsmicroarray image gridding image analysis multi level thresholding transcriptomics
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