Validating microarray data
If a gene is used to produce m RNA, it is considered "on", otherwise "off".Many factors determine whether a gene is on or off, such as the time of day, whether or not the cell is actively dividing, its local environment, and chemical signals from other cells.
This is because altered levels of a specific sequence of m RNA suggest a changed need for the protein coded by the m RNA, perhaps indicating a homeostatic response or a pathological condition.In the field of molecular biology, gene expression profiling is the measurement of the activity (the expression) of thousands of genes at once, to create a global picture of cellular function.These profiles can, for example, distinguish between cells that are actively dividing, or show how the cells react to a particular treatment.Best case is something like q PCR in a sample not in the original cohort, second to that is using a publicly available dataset as an independent validation.Heat maps of gene expression values show how experimental conditions influenced production (expression) of m RNA for a set of genes. Cluster analysis has placed a group of down regulated genes in the upper left corner.For instance, skin cells, liver cells and nerve cells turn on (express) somewhat different genes and that is in large part what makes them different.
Therefore, an expression profile allows one to deduce a cell's type, state, environment, and so forth.
To validate an observation, then simply, you need to repeat the experiment with an independent cohort (often not feasible), or use q PCR.
A combined "meta-analysis data set" again, is a dangerous thing to do if you don't understand the caveats of what you're trying to achieve from a statistical perspective. This also isn't validation, as much as an experimental power boost in most cases.
Dear everybody, Could you please provide me main principles for validation in microarray-based gene expression meta-analysis studies?
First of all, I personally think that meta-analysis itself is a validation study.
DNA microarrays Expression profiling is a logical next step after sequencing a genome: the sequence tells us what the cell could possibly do, while the expression profile tells us what it is actually doing at a point in time.