If you have a vector of bootstrap contributions for a sample, there are a couple of different approaches often used to decide if your sample passes/fails bootstrap support.
One of the most common is to ask
What is the minimum proportion of bootstraps (B) that signature X contribution must be greater than some threshold value (T) before we decide its too unstable to include in the final model?
Out in he wild I've seen default of T = 5%, and B = 95%. I.e. To be included in a model the signature must contribute to >5% of the optimal model in at >95% of the bootstrapped experiments.
We should add a function sig_compute_pval_from_bootstraps that takes
A user-defined threshold of T
A numeric vector describing bootstrap contributions of a single signature
computes the 'experimental p value' as above (i.e. what % of bootstraps the sample is >= T).
If you have a vector of bootstrap contributions for a sample, there are a couple of different approaches often used to decide if your sample passes/fails bootstrap support.
One of the most common is to ask
What is the minimum proportion of bootstraps (B) that signature X contribution must be greater than some threshold value (T) before we decide its too unstable to include in the final model?
Out in he wild I've seen default of T = 5%, and B = 95%. I.e. To be included in a model the signature must contribute to >5% of the optimal model in at >95% of the bootstrapped experiments.
We should add a function sig_compute_pval_from_bootstraps that takes
We can leave it up to the user to filter on 'B'