Normalisation and statistics method

Normalisation and statistics method
Per spot and per chip normalisation by Intensity dependant (LOWESS) normalisation.
A Lowess curve was fit to the log-intensity versus log-ratio plot. 20% of the data was
used to calculate the Lowess fit at each point. This curve was used to adjust the
control value for each measurement. If the control channel was lower than 10, than 10
was used instead. The 50th percentile of all measurements in that sample divided each
measurement. For samples where the bottom tenth percentile was less than the
negative of the 50th percentile, it was used as a background and subtracted from all the
other genes first.
Statistical analysis was carried out based on the normalised data. Two-group,
unpaired Welch t-test (variances not equal) was used for 2 groups comparison.
Estimate experimental variance, including hybridisation of pairs of cDNAs on
different batch of microarray slides on different days, indicated that our measurements
fell within 95 percent confidence interval of 0.49 to 0.88 for the mean of 0.68 by
paired T-test (Minitab).
Hierarchical agglomerative clustering was performed by J-Express pro. The distance
score is based on the Cosine correlation between two vectors containing values of
similarities across multiple fields. A small value in the distance matrix implies that
these two clusters/objects are more similar than clusters/objects with greater value.
Then the average linkage WPGMA(weighted pair group method with arithmetic
mean) is used to define the distance from each of the elements already in the matrix to
the new cluster.
The formulas:
Cosine correlation:
d ( x. y) 
 ( xiyi)
 ( xi)2 ( yi)2
d (C (ij ), Ck ) 
D(ci, ck )  d (cj , ck )