scholarly journals Microarray data analysis to identify differentially expressed genes and biological pathways associated with asthma

2018 ◽  
Author(s):  
Shanshan Qi ◽  
Guanghui Liu ◽  
Xiang Dong ◽  
Nan Huang ◽  
Wenjing Li ◽  
...  
Keyword(s):  
Data Analysis ◽  
Differentially Expressed Genes ◽  
Microarray Data ◽  
Biological Pathways ◽  
Differentially Expressed ◽  
Microarray Data Analysis

scholarly journals Impact of DNA microarray data transformation on gene expression analysis - comparison of two normalization methods.

2011 ◽  
Vol 58 (4) ◽  
Author(s):  
Marcin T Schmidt ◽  
Luiza Handschuh ◽  
Joanna Zyprych ◽  
Alicja Szabelska ◽  
Agnieszka K Olejnik-Schmidt ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Dna Microarray ◽  
Microarray Data ◽  
Normalization Method ◽  
Differentially Expressed ◽  
Microarray Data Analysis ◽  
Data Set ◽  
Normalization Methods ◽  
The Impact

Two-color DNA microarrays are commonly used for the analysis of global gene expression. They provide information on relative abundance of thousands of mRNAs. However, the generated data need to be normalized to minimize systematic variations so that biologically significant differences can be more easily identified. A large number of normalization procedures have been proposed and many softwares for microarray data analysis are available. Here, we have applied two normalization methods (median and loess) from two packages of microarray data analysis softwares. They were examined using a sample data set. We found that the number of genes identified as differentially expressed varied significantly depending on the method applied. The obtained results, i.e. lists of differentially expressed genes, were consistent only when we used median normalization methods. Loess normalization implemented in the two software packages provided less coherent and for some probes even contradictory results. In general, our results provide an additional piece of evidence that the normalization method can profoundly influence final results of DNA microarray-based analysis. The impact of the normalization method depends greatly on the algorithm employed. Consequently, the normalization procedure must be carefully considered and optimized for each individual data set.

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