Advanced usage
Running sub-models
By default, the run_model
function uses the full HAMdetector model that includes the horseshoe prior, phylogeny information and epitope prediction. Reduced versions of the full model can be run by supplying an additional positional argument:
using Escape
data = HLAData(
alignment_file = "/home/user/Desktop/alignment.fasta",
tree_file = "/home/user/Desktop/phylogeny.tree"
)
result = Escape.run_model(HLAModel{4}(), data)
The different versions are:
- 1: A simple logistic regression model
- 2: Like model 1, but additionally with horseshoe prior
- 3: Like model 2, but additionally with phylogeny information included
- 4: Full HAMdetector model, like model 3 but with epitope prediction included.
Note that the full HAMdetector model includes all the previous models as a special case.
Modifying sampler parameters
By default, 4 chains with 1000 iterations each and a warmup of 300 samples are drawn. This should be plenty for all use-cases, but can be changed as shown below:
using Escape
data = HLAData(
alignment_file = "/home/user/Desktop/alignment.fasta",
tree_file = "/home/user/Desktop/phylogeny.tree"
)
result = Escape.run_model(
data, chains = 4, iter = 1000, warmup = 300, stan_args = "adapt delta=0.95"
)
For a full list of the CmdStan options, please refer to the CmdStan sampling parameters. It should not be necessary to modify the stan_args
argument, it is just listed here for completeness.
Using 4-digit HLA alleles
By default, HAMdetector uses HLA alleles with 2-digit accuracy. To use HLA alleles with 4-digit accuracy (if supplied), set the allele_depth
keyword argument to 2 (default is 1).
using Escape
data = HLAData(
alignment_file = "/home/user/Desktop/alignment_with_4_digit_alleles.fasta",
tree_file = "/home/user/Desktop/phylogeny.tree",
allele_depth = 2
)