I am looking at a signal from an exogenous protein that also includes the signal from the endogenous homologue. To get a rough idea about the signal from my exogenous protein I subtract an averaged endogenous background signal. This endogenous background signal comes from five experiments and has a certain variation. If I do for example six experiments where I get a combined signal from both endogenous and exogenous proteins, and from each of these experiments subtract the averaged endogenous background signal, I get six datasets for my exogenous protein and can plot it with for example SD or SEM, but what about the error I already know to have from my averaged endogenous background signal, how do I take this into account?
Thank you,
-Michael
There may be specialist methods for this, but from first principles one can add in the error using the variance sum law.
So SD[data]^2 + SD[background]^2 = SD[total]^2
Assuming independence (but one can generalise this to correlated samples).
Thank you Thom,
just to further highlight how bad I am with statistics; I wouldn't be able to translate this error into SEM right? or is there a way to do so?
I don't see why not. I think one would just use the new SD in the SEM calculation based on sample n as this SD captures the total error you expect.
There may be more sophisticated procedures but this seems reasonably conservative to me.
I agree with Thom,
if CorrData = Data - Bgnd
the law of propagation of variances gives you
var(corrData) = var(Data) + var(Bgnd)
Instead of subtracting average endogenous background, from each exogenous protein, you have to subtract the individual background for each protein and take the average which is more appropriate.
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