natsel_sitehet – a test of site heterogeneity

Note

These docs now use the new_type core objects via the following setting.

import os

# using new types without requiring an explicit argument
os.environ["COGENT3_NEW_TYPE"] = "1"

This app evaluates evidence for whether sites differ in their mode of natural selection (Nielsen and Yang 1998).

from cogent3 import get_app

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

sites_differ = get_app("natsel_sitehet",
    "GNC", tree="data/primate_brca1.tree", optimise_motif_probs=False
)

result = sites_differ(aln)
result

Statistics

LR	df	pvalue
1.4048	2	0.4954

hypothesis	key	lnL	nfp	DLC	unique_Q
null	'GNC-null'	-6708.3129	24	True	True
alt	'GNC-alt'	-6707.6104	26	True	True

The models have been constructed such that site-class bins have names indicating the mode of natural selection: -ve is purifying (oomega<1); neutral (omega=1); and +ve is positive natural selection (omega>1). The two parameters of interest relating to these are the bprobs (the maximum likelihood estimate of the frequency of the site-class) and the corresponding value of omega.

result.alt.lf

GNC-alt

log-likelihood = -6707.6104

number of free parameters = 26

Global params

A>C	A>G	A>T	C>A	C>G	C>T	G>A	G>C	G>T	T>A	T>C
0.85	3.56	0.97	1.64	2.18	6.32	8.08	1.23	0.78	1.28	3.03

Bin params

bin	bprobs	omega
-ve	0.10	0.00
neutral	0.81	1.00
+ve	0.09	20.00

Edge params

edge	parent	length
Galago	root	0.55
HowlerMon	root	0.14
Rhesus	edge.3	0.06
Orangutan	edge.2	0.02
Gorilla	edge.1	0.01
Human	edge.0	0.02
Chimpanzee	edge.0	0.01
edge.0	edge.1	0.00
edge.1	edge.2	0.01
edge.2	edge.3	0.04
edge.3	root	0.02

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

Getting the individual site posterior probabilities

I’m just displaying the posterior-probabilities from the first 20 positions only.

bprobs = result.alt.lf.get_bin_probs()
bprobs[:, :20]

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
-ve	0.1491	0.0839	0.0000	0.1313	0.1146	0.1569	0.0843	0.1191	0.1020	0.0798	0.0760	0.1569	0.0937	0.0000	0.1563	0.5173	0.0798	0.0695	0.1146	0.1216
neutral	0.7725	0.8127	0.8643	0.7851	0.7961	0.7668	0.8125	0.7927	0.8032	0.8141	0.8164	0.7668	0.8070	0.8655	0.7670	0.4824	0.8141	0.8197	0.7961	0.7909
+ve	0.0784	0.1034	0.1357	0.0837	0.0893	0.0764	0.1032	0.0882	0.0948	0.1061	0.1076	0.0764	0.0993	0.1345	0.0766	0.0003	0.1061	0.1108	0.0893	0.0875