Quick start¶
IgPhyML is easiest to use when run indirectly through the Change-O program
BuildTrees
by specifying the --igphyml
option.
Most of these instructions require Change-O 0.4.6 or higher, Alakazam 0.3.0 or higher,
and IgPhyML to be installed, with the executable in your PATH
variable.
If these aren’t possible, see IgPhyML standalone operation
To view all options for BuildTrees , run the command:
BuildTrees.py --help
The following commands should work as a first pass on many reasonably sized datasets, but if you really want to understand what’s going on or make sure what you’re doing makes sense, please check out the rest of the website.
Build trees and estimate model parameters¶
Move to the examples
subfolder and run:
BuildTrees.py -d example.tsv --outname ex --log ex.log --collapse \
--sample 3000 --igphyml --clean all --nproc 1
This command processes an AIRR-formatted dataset of BCR sequences that have been
clonally clustered
with germlines reconstructed.
It then quickly builds trees using the GY94 model and, using these
fixed topologies, estimates HLP19 model parameters. This can be sped up by
increasing the --nproc
option. Subsampling using the --sample
option in isn’t
strictly necessary, but IgPhyML will run slowly when applied to large datasets.
Here, the --collapse
flag is used to collapse identical sequences. This is
highly recommended because identical sequences slow down calculations without
affecting likelihood values in IgPhyML.
Visualize results¶
The output file of the above command can be read using the
readIgphyml
function of
Alakazam.
After opening an R
session, enter the following commands. Note that
when using the Docker container, you’ll need to run dev.off()
after
plotting the tree to create a pdf plot in the examples
directory:
library(alakazam)
library(igraph)
db = readIgphyml("ex_igphyml-pass.tab")
#plot largest lineage tree
plot(db$trees[[1]],layout=layout_as_tree)
#show HLP10 parameters
print(t(db$param[1,]))
CLONE "REPERTOIRE"
NSEQ "4"
NSITE "107"
TREE_LENGTH "0.286"
LHOOD "-290.7928"
KAPPA_MLE "2.266"
OMEGA_FWR_MLE "0.5284"
OMEGA_CDR_MLE "2.3324"
WRC_2_MLE "4.8019"
GYW_0_MLE "3.4464"
WA_1_MLE "5.972"
TW_0_MLE "0.8131"
SYC_2_MLE "-0.99"
GRS_0_MLE "0.2583"