Over the last decade, success in microarray data studies has led to an expansion of large-scale omics data analyses and their data types.
While it is possible after obtaining results from microarray data to study the role of single or multiple genes in inducing the phenotype, it is rather difficult to perform such studies rather quickly in vivo in mice.
To demonstrate the usefulness of the integrated microarray data for studying human gene expression patterns, we have analyzed the dataset to identify potential tissue-selective genes.
Two distinct microarray data sets were studied here.
Here, we describe a novel statistical method for identifying genes with outlier expression in large-scale microarray data integration studies and compare this method with existing algorithms.
The leukemia dataset 12 has frequently been used in previous microarray data analysis studies.
Using publicly available DNA microarray data, this study explores folate cycle interactions at the higher level of mRNA.
Author's response At least for microarray data, previous studies [ 46- 48] have shown that good consistency exists between gene expression measurements produced within the same laboratory using commercial platforms (such as the microarray data used by this study [ 20]).
The main objective of this investigation is to assess the reporting of experimental design and statistical methodologies in recently published microarray data analysis studies.
Clustering-based analysis, the t-test and ANOVA represent the most widely applied techniques in microarray data analysis studies (Table 3).
