Quantitatively monitor expression level for thousands of genes at a time.
All the methods and applications are based on Nylon membrane microarrays and can be extended to other DNA microarray analysis using other platforms.
A number of systematic variations can occur during experiments. For example, different samples being compared are hybridized on different nylon membranes. Need normalization to remove these sources of variation.
Well normalized data are the foundation of good analysis results.
Alignment : each gene is represented by two spots. Match these two spots to a schematic representation of an array. Final intensity for this gene will be the average value of the intensities of these two spots.
external(global):median intensity of the black space between different panels.
user-defined external:median intensity of user-defined area
local:median intensity of the space surrounding the gene spot
Adjusted intensity = raw intensity - background value
Each Clontech Stress array contains 234 sequences expressed in response to stress.
Each insert cDNA is denatured and UV cross-linked to a positively charged membrane
Samples are treated with DMSO and BaP (Benzo(a)pyrene) dissolved in DMSO. So DMSO is the control and BaP is the treatment.
DMSO and BaP treated samples are hybridized under the same condition each time. Two membranes are used three times for DMSO and BaP treated samples, respectively.
Three biological replicates done with the same membrane(s) (correlation occurs)
Use Phosphor Imager laser scanner to obtain densities of each spot on filter. Control RNA Sample Test RNA Sample Hybridization to microarray filters radio-labelled cDNA probes Reverse-Transcription 33 P - dCTP 33 P - dCTP Compare densities at each spot to determine if treatment changes gene expression. Compile subset of differentially expressed genes. Gene Control Test A 1X 3X : : : Z 1X 0.5X
Scatter plots of adjusted log intensities for paired experiments of D MSO vs BaP
Hundreds of genes tested at the same time. Assume 1000 genes are not differentially expressed. P-value of 0.01(false positive rate) means that around 10 genes will nevertheless be significant.
Bonferroni correction: want to make sure that P[ 1 gene significant from 1000] 0.05. Consequently, p-value for a single gene to be announced as significant is: P [single gene] 0.05/1000 = 0.00005
Conservative and lower power.
keep FWR manageable and try some p-value, say 0.001 as the significant level.
Cutoff point determination: set up critical point to eliminate genes whose intensity is less than this point.
Statistically significant? No unique method to analyze data. Some methods are better for one data set, but may not be good for other data sets. In practice, we have to try different ways to see which methods work well.
Biologically significant? For those genes picked up by statistics, we have to be careful to draw conclusions. Some genes shown to be significant may not be functionally meaningful. Conversely, genes that do not show up significant may be significant, especially for those genes at the boarder line in the statistical test.