getSignalVars(counts, bgcounts)counts. Rows represent the estimated variance of true signal for each gene.ExampleData.
By assuming that the true signal and background noise are independent, the variance of the underneath signal ($\sigma_s^2$)
can be estimated by applying variance summation law:
$$\sigma_s^2 = \sigma_x^2 + \sigma_b^2 - 2{\rho}{\sigma_x}{\sigma_b}$$
where $\sigma_x^2$ and $\sigma_b^2$ are variance for observed signal and background noise respectively.estimateSCVconditions <- factor(c(rep('C1', 3), rep('C2', 3)))
data(ExampleData)
data_var <- getSignalVars(Observed, Background)Run the code above in your browser using DataLab