RealSpot: software validating results from DNA microarray data analysis with spot images

2005 ◽  
Vol 21 (2) ◽  
pp. 284-291 ◽  
Author(s):  
Zhongming Chen ◽  
Lin Liu

The spot images from DNA microarray highly affect the discovery of biological knowledge from gene expression data. However, results from quality analysis, normalization, differential expression, and cluster analysis are rarely validated with spot images in current data analysis methods or software packages. We designed RealSpot, a software package, to validate the results by directly associating spot quality and data with spot images in a spreadsheet table. RealSpot splits hybridization images into individual spots stored in a spreadsheet table. It subsequently associates microarray data with spot images and performs data validation through the standard table operation such as sorting, searching, and editing. RealSpot has several built-in functions to facilitate data validation, including spot quality analysis, data organization, one-way ANOVA, gene ontology association, verification, import, and export. We used RealSpot to evaluate 77 slides (30,000 features each) from real hybridization experiments and to validate results from each step of data analysis. It took ∼10 min to validate results of spot quality after initial evaluation and correct ∼0.3% of falsely assigned qualities of 10,000 spots. We validated 1,641 of 2,110 differentially expressed genes identified by SAM analysis in ∼1/2 h by comparing each gene with its respective spot image. Furthermore, we found that 6 of 48 genes in one cluster from k-mean clustering method showed inconsistent trends of spot images. RealSpot is efficient for validating microarray results and thus helpful for improving the reliability of the whole microarray experiment for experimentalists.

2013 ◽  
pp. 513-551 ◽  
Author(s):  
Alain B. Tchagang ◽  
Youlian Pan ◽  
Fazel Famili ◽  
Ahmed H. Tewfik ◽  
Panayiotis V. Benos

In this chapter, different methods and applications of biclustering algorithms to DNA microarray data analysis that have been developed in recent years are discussed and compared. Identification of biological significant clusters of genes from microarray experimental data is a very daunting task that emerged, especially with the development of high throughput technologies. Various computational and evaluation methods based on diverse principles were introduced to identify new similarities among genes. Mathematical aspects of the models are highlighted, and applications to solve biological problems are discussed.


2007 ◽  
Vol 131 (1) ◽  
pp. 34-44 ◽  
Author(s):  
Takashi Hirasawa ◽  
Katsunori Yoshikawa ◽  
Yuki Nakakura ◽  
Keisuke Nagahisa ◽  
Chikara Furusawa ◽  
...  

2013 ◽  
Vol 74 (20) ◽  
pp. 9031-9041 ◽  
Author(s):  
Dong Kyun Park ◽  
Eun-Young Jung ◽  
Sang-Hong Lee ◽  
Joon S. Lim

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