Statistical Design and Analysis of Microarray Experiments Peng Liu 6/15/2010. Microarray Technology. Microarray technology allows measuring expression levels (abundance of mRNA transcripts) of thousands of genes simultaneously. Two types of platforms: Affymetrix (single-color)
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Statistical Design and Analysis of Microarray Experiments
cattle have a
mutation in the
Design of Affymetrix experiment: one sample one chip
From Churchill, 2002, nature genetics
bundle sheath strands
mesophyll protoplastsExample I: Sawers et al, 2007, BMC Bioinformatics
Isolate cells and perform a microarray experiments to compare the gene expression between the two cells (treatments).
The procedure for extracting mRNA for the two cells are different. The one to extract mRNA from M cells introduces stress.
Add two more treatment groups: samples with both M and B cells going through extraction of mRNA with and without stress.
B, M, Stress and Total (4 treatment groups)
MDesigning experiment for example I
With 6 biological replicates
Affymetrix Gene Chip image
2-color microarray image
Log Red-Log Green = M
(Log Green+Log Red)/2 = A
Log Red-Log Green
(Log Green+Log Red)/2
Yijk for each gene (g)
i: treatment index
j: dye index
k: slide index
v1 - v2 0 means differential expression.
2536 p-values below 0.05.
We would expect around 0.05*40000=2000
p-values to be less than 0.05 by chance
if no genes were differentially expressed.
Outcomes when testing m genes
(Benjamini and Hochberg, 1995)
Family-wise error rate, FWER= Pr(V >0)
False Discovery Rate,
FDR = E(V/R |R>0) * Pr(R>0)