Biology Reference
In-Depth Information
Fig. 1
Agilent Bioanalyzer scan of total RNA from eukaryotic cultured cells isolated using the RNeasy mini Kit
from Qiagen. RNA was diluted to a concentration of 100 ng/
μ
L and 1
μ
L aliquots were analyzed using the RNA
6000 LabChip kit
2. Quality control assessment can be performed using different
Bioconductor packages (
www.bioconductor.org
)
such as
R-AffyQC Report, R-Affy-PLM, R-RNA Degradation Plot,
and Partek's GS (
see
Note 2
).
3. The DChip-free software can be also used to perform super-
vised analyses as well as to visualize results.
3.9 Microarray Data
Functional Analysis
1. Microarray data analysis measures the changes of gene expres-
sion providing a gene list as fi nal output (
see
Fig.
2
). However,
for a biological interpretation of the experiment, the gene list
needs to be elaborated into the format of a functional gene
group-based study. Commercial and open source bioinfor-
matic tools are available, that, using public databases (e.g.,
Gene Ontology and KEGG), permit to systematically dissect
large gene lists assembling the transcripts into enriched bio-
logical and molecular functional groups. The enrichment anal-
ysis is based on the concept that, if a biological process is
triggered by a treatment, the related co-functioning genes
should have an enriched and higher potential as a relevant
group compared to what is represented in the microarray as a
whole (gene population background) (
see
Fig.
3
). Such over-
represented categories represent biological “themes” of a given
list. The enrichment can be quantitatively measured by com-
mon statistical methods such as Chi-square, Fisher's exact test,
binomial probability, and Hypergeometric distribution [
18
].
