I have a table like this:
Age5 Age5 Age5 Age22 Age22 Age22 Gene1 1.2 2.3 4.5 3.4 4.5 1.3 Gene2 2.4 -2.3 1.3 1.2 3.4 4.5
i.e two age groups (5 and 22), for multiple genes and the values are log2 transformed gene expression data.
for one gene, for example, I did a linear regression (so the x axis is age, and the y axis is log2 expression values). The statistics from the output from that regression are:
equation type: linear co-efficient = -0.127 intercept = 4.85 data transformation = log2 % change between age group 5 and 22 = 51%
The problem, I do not understand how they calculated the % change as 51% using the information.
For example, I said: y = b0 + b1(x)
For expression data at age 5;
y = 4.85 + (5)(-0.127) y = 4.85-0.635 y = 4.215
but since the y(gene expression) is log2 transformed, I log2 transformed 4.215; so the expression data at age 5 (i.e. y) is 2.075.
Then I did the same for age 22:
y = 4.85 + (22)(-0.127) y = 4.85 -3.74 y = 1.11
and similarly, since y is log2 transformed, I log2 transformed 1.11, so the expression at age 22 (i.e. y) = 0.151.
Then, I cannot seem to combine the two expression values (i.e. 2.075 and 0.151) in a way that will give me a 51% change in gene expression between the two age groups as calculated from a linear regression. Can someone show me how this calculation is done?
In case anyone is interested, this is where I got all the above numbers used in my calculations from.