What is the aim of doing statistical analysis of microarray data?
A primary objective of most microarray studies is to identify individual features associated with a phenotype or an experimental factor.
What is p-value in microarray data analysis?
The P-value is the probability for the experimental outcome as observed or more extreme, if there is no difference in expression between the experimental conditions. A small P-value indicates evidence of differential expression, either overexpression or underexpression.
What can you do with microarray data?
Microarrays can be used in many types of experiments including genotyping, epigenetics, translation profiling and gene expression profiling. Gene expression profiling is by far the most common use of microarray technology. Both one- and two-colour microarrays can be used for this type of experiment.
How do you calculate fold change from microarray data?
Fold change is computed simply as the ratio of the changes between final value and the original value over the initial value. Thus, if the original value is X and final value is Y, the fold change is (Y – X)/X or equivalently Y/X – 1.
What does log2 fold change mean?
The log2(fold-change) is the log-ratio of a gene’s or a transcript’s expression values in two different conditions. While comparing two conditions each feature you analyse gets (normalised) expression values. This value can be zero and thus lead to undefined ratios.
What is adj P value?
The adjusted P value is the smallest familywise significance level at which a particular comparison will be declared statistically significant as part of the multiple comparison testing. A separate adjusted P value is computed for each comparison in a family of comparisons.
What is microarray dataset?
A microarray database is a repository containing microarray gene expression data. The key uses of a microarray database are to store the measurement data, manage a searchable index, and make the data available to other applications for analysis and interpretation (either directly, or via user downloads).
Why is microarray useful?
It helps especially in the identification of single-nucleotide polymorphisms (SNPs) and mutations, classification of tumors, identification of target genes of tumor suppressors, identification of cancer biomarkers, identification of genes associated with chemoresistance, and drug discovery.
How are microarray data used in Biological Studies?
Such procedure can be circular in logic; better procedure is to identify differences between groups defined on biological grounds. Microarray data is often used as a guide to further, more precise studies of gene expression by qt-PCR or other methods.
How to specify the confidence of microarray results?
Perhaps a better way to specify the confidence of microarray results is the ‘false discovery rate’. Often the first step is transforming the values to log scale, and doing all subsequent steps on the log-transformed values.
Why are permutation tests used in microarray studies?
Permutation testing is an approach that is widely applicable and copes with distributions that are far from Normal; this approach is particularly useful for microarray studies because it can be easily adapted to estimate significance levels for many genes in parallel.
Is the log transform too strong in microarray plots?
However the gene abundances on the log scale usually show greater variation near the bottom end, giving rise to the characteristic ‘funnel’ shape of many microarray plots. The log transform is too ‘strong’, and partly reverses the inequality of measurement variances.