Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Markov Random Field model of microarray gridding
Proceedings of the 2003 ACM symposium on Applied computing
A Markov Random Field Approach to Microarray Image Gridding
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A Hill-Climbing Approach for Automatic Gridding of cDNA Microarray Images
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Model-based deconvolution of genome-wide DNA binding
Bioinformatics
An Efficient Algorithm for Optimal Multilevel Thresholding of Irregularly Sampled Histograms
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Sub-grid detection in DNA microarray images
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
A Deformable Grid-Matching Approach for Microarray Images
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
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.