As the field moves from simpler analyses (e.g., differential expression of single genes and clustering) to more complex analyses such as developing multivariate molecular signatures in supervised fashion, the interpretation of microarray data involves multifaceted analysis protocols with many sophisticated and interacting analytic steps [4].
One common filter that is applied to microarray data involves an expression filter that compares mRNA abundance of specific gene probes in one cohort versus a reference cohort.
Defining the boundaries of these regions from high-density microarray data involves scoring each probe as indicative of either a conserved or variable sequence and grouping probes with consistent scores into larger segments with defined boundaries.
Another approach often applied to microarray data involves creation of a co-expression network within which each 'node' represents a gene, and 'edges' are created between genes when their expression patterns are significantly correlated.
Fifthly, there are differences in the statistical models used to detect differentially expressed genes for the two platforms (for example, analysis of the microarray data involve moderated t-tests; analysis of the RNA-seq data using edgeR involved a negative binomial distribution) [ 28, 68- 71].
Contact: [email protected] or [email protected] Typical workflows for the analysis of microarray data involve several steps, namely, the preparation of samples and arrays, their hybridization to arrays, scanning the array and processing the image to read out the raw probe intensities.
The microarray quantitative data involves over 10 different oligonucleotides arrayed for each gene and the hybridization must be consistent to allow a statistically significant quantitative measure of gene expression and regulation.
Also, the microarray expression data involved in human diseases will be semiannually collected from Gene Expression Omnibus (GEO).
The microarray quantitative data involves over 10 different oligonucleotides arrayed for each gene and the hybridization must be consistent to allow for a statistically significant quantitative measure of gene expression and regulation.
The microarray quantitative data involves signals from an average of 28 different oligonucleotides (probes) arrayed for each transcript and many genes are represented on the chip by several transcripts.
