A large amount of effort has focused on developing mathematical models to normalize and identify differentially expressed genes [1].
To choose these 40 samples, we first used RMA to normalize all samples identified for the cell type.
The array data was normalized for identifying differentially expressed genes, clustering and annotation.
To examine acylcarnitine profiles in individuals with hypo- or hyperthyroidism, and determine whether any atypical acylcarnitine species identified would normalize with correction of thyroid status.
We used FPKM (Fragments Per Kilobase per Million) method to normalize the expression of identified transcripts across different conditions.
Once named entities have been identified, we normalize them, so that, e.g. both 'diencephalon' and 'interbrain' resolve to the same entity.
Specific differences in gene expression, however, are identified after normalizing for global differences in the level of gene expression, thus, global differences may not be visualized in these analyses.
After normalizing the data, we identified genes that exhibited a log-fold change of greater than 2 between control E2 and control E2 cells, or between control E2 and siJ2B E2 cells.
Next, the effects of inhibiting synaptic activity on: i) DCC density – the number of identified DCCs normalized to total dendritic length in each field and ii) DC clustering - the proportion of DCs that participate in DCCs, as a fraction of the total number of DCs in a given network, were considered.
Using parallel oligonucleotide array platforms, shared orthologues between species were identified and normalized.
