`natsel_timehet` – a test of branch heterogeneity

We employ codon models to test whether the mode of natural selection affecting human and chimpanzee lineages is distinctive. This is done by specifying the edges of interest (Yang 1998).

Warning

I’m setting optimise_motif_probs=False to speed up execution of the examples, not because it’s a good idea!

from cogent3 import get_app

loader = get_app("load_aligned", format_name="fasta", moltype="dna")
aln = loader("data/primate_brca1.fasta")

hc_differ = get_app("natsel_timehet",
    "GNC",
    tree="data/primate_brca1.tree",
    optimise_motif_probs=False,
    tip1="Human",
    tip2="Chimpanzee",
)
result = hc_differ(aln)
result

Statistics
LR	df	pvalue
4.9248	1	0.0265

hypothesis	key	lnL	nfp	DLC	unique_Q
null	'GNC-null'	-6713.2743	23	True	True
alt	'GNC-alt'	-6710.8118	24	True	True

result.alt.lf

GNC-alt

log-likelihood = -6710.8118

number of free parameters = 24

Global params
A>C	A>G	A>T	C>A	C>G	C>T	G>A	G>C	G>T	T>A	T>C
0.86	3.54	0.98	1.67	2.21	6.26	7.92	1.23	0.80	1.29	3.07

Edge params
edge	parent	length	omega
Galago	root	0.52	0.79
HowlerMon	root	0.13	0.79
Rhesus	edge.3	0.06	0.79
Orangutan	edge.2	0.02	0.79
Gorilla	edge.1	0.01	0.79
Human	edge.0	0.02	2.64
Chimpanzee	edge.0	0.01	2.64
edge.0	edge.1	0.00	0.79
edge.1	edge.2	0.01	0.79
edge.2	edge.3	0.04	0.79
edge.3	root	0.02	0.79

Motif params
AAA	AAC	AAG	AAT	ACA	ACC	ACG	ACT	AGA	AGC	AGG	AGT	ATA
0.06	0.02	0.03	0.06	0.02	0.00	0.00	0.03	0.02	0.03	0.01	0.04	0.02

continuation
ATC	ATG	ATT	CAA	CAC	CAG	CAT	CCA	CCC	CCG	CCT	CGA	CGC
0.01	0.01	0.02	0.02	0.01	0.02	0.02	0.02	0.01	0.00	0.03	0.00	0.00

continuation
CGG	CGT	CTA	CTC	CTG	CTT	GAA	GAC	GAG	GAT	GCA	GCC	GCG
0.00	0.00	0.01	0.01	0.01	0.01	0.08	0.01	0.03	0.03	0.02	0.01	0.00

continuation
GCT	GGA	GGC	GGG	GGT	GTA	GTC	GTG	GTT	TAC	TAT	TCA	TCC
0.01	0.02	0.01	0.01	0.01	0.01	0.01	0.01	0.02	0.00	0.01	0.02	0.01

continuation
TCG	TCT	TGC	TGG	TGT	TTA	TTC	TTG	TTT
0.00	0.03	0.00	0.00	0.02	0.02	0.01	0.01	0.02