natsel_zhang – a branch-site test

This is the hypothesis test presented in Zhang et al. This test evaluates the hypothesis that a set of sites have undergone positive natural selection on a pre-specified set of lineages.

For this model class, there are groups of branches for which all positions are evolving neutrally but some proportion of those neutrally evolving sites change to adaptively evolving on so-called foreground edges. For the current example, we’ll define the Chimpanzee and Human branches as foreground and everything else as background. The following table defines the parameter scopes.

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Note

Our implementation is not as parametrically succinct as that of Zhang et al, we have 1 additional bin probability.

from cogent3.app import io, evo

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

zhang_test = evo.natsel_zhang(
    "GNC",
    tree="data/primate_brca1.tree",
    optimise_motif_probs=False,
    tip1="Human",
    tip2="Chimpanzee",
)

result = zhang_test(aln)
result
Statistics
LRdfpvalue
4.957030.1750
hypothesiskeylnLnfpDLCunique_Q
null'GNC-null'-6708.311924TrueTrue
alt'GNC-alt'-6705.833427TrueTrue
result.alt.lf

GNC-alt

log-likelihood = -6705.8334

number of free parameters = 27

Global params
A>CA>GA>TC>AC>GC>TG>AG>CG>TT>A
0.85543.53440.97451.65872.19376.25878.01061.24190.79421.2668
continuation
T>C
2.9646
Bin params
binbprobs
00.0513
10.0711
2a0.0422
2b0.8354
Edge params
edgeparentlength
Galagoroot0.5419
HowlerMonroot0.1359
Rhesusedge.30.0648
Orangutanedge.20.0235
Gorillaedge.10.0075
Humanedge.00.0182
Chimpanzeeedge.00.0085
edge.0edge.10.0000
edge.1edge.20.0099
edge.2edge.30.0365
edge.3root0.0234
Edge bin params
edgebinomega
Galago01.0000002366422153e-06
Galago11.0
Galago2a1.0000002366422153e-06
Galago2b1.0
HowlerMon01.0000002366422153e-06
HowlerMon11.0
HowlerMon2a1.0000002366422153e-06
HowlerMon2b1.0
Rhesus01.0000002366422153e-06
Rhesus11.0
Rhesus2a1.0000002366422153e-06
Rhesus2b1.0
Orangutan01.0000002366422153e-06
Orangutan11.0
Orangutan2a1.0000002366422153e-06
Orangutan2b1.0
Gorilla01.0000002366422153e-06
Gorilla11.0
Gorilla2a1.0000002366422153e-06
Gorilla2b1.0
Human01.0000002366422153e-06
Human11.0
Human2a4.460261328868928
Human2b4.460261328868928
Chimpanzee01.0000002366422153e-06
Chimpanzee11.0
Chimpanzee2a4.460261328868928
Chimpanzee2b4.460261328868928
edge.001.0000002366422153e-06
edge.011.0
edge.02a1.0000002366422153e-06
edge.02b1.0
edge.101.0000002366422153e-06
edge.111.0
edge.12a1.0000002366422153e-06
edge.12b1.0
edge.201.0000002366422153e-06
edge.211.0
edge.22a1.0000002366422153e-06
edge.22b1.0
edge.301.0000002366422153e-06
edge.311.0
edge.32a1.0000002366422153e-06
edge.32b1.0
Motif params
AAAAACAAGAATACAACCACGACTAGAAGC
0.05560.02350.03440.05560.02280.00460.00080.02890.02310.0286
continuation
AGGAGTATAATCATGATTCAACACCAGCAT
0.01400.03810.01860.00700.01280.01920.01960.00520.02380.0221
continuation
CCACCCCCGCCTCGACGCCGGCGTCTACTC
0.01950.00620.00060.02630.00110.00090.00230.00320.01370.0078
continuation
CTGCTTGAAGACGAGGATGCAGCCGCGGCT
0.01250.01050.07550.01050.03030.03150.01580.00960.00140.0137
continuation
GGAGGCGGGGGTGTAGTCGTGGTTTACTAT
0.01610.00900.00670.01330.01480.00700.00690.02130.00230.0101
continuation
TCATCCTCGTCTTGCTGGTGTTTATTCTTG
0.02210.00820.00150.02510.00180.00400.02010.02120.00780.0108
continuation
TTT
0.0187

Getting the posterior probabilities of site-class membership

bprobs = result.alt.lf.get_bin_probs()
bprobs[:, :20]
012345678910111213141516171819
00.07330.04130.00000.06470.05660.07730.04150.05870.05010.03960.03780.07730.04630.00000.07700.25280.03960.03430.05660.0598
10.06820.07230.07840.06930.07040.06760.07230.07010.07120.07250.07270.06760.07160.07830.06770.04190.07250.07320.07040.0699
2a0.05940.03430.00000.05270.04640.06250.03450.04800.04130.03310.03160.06250.03840.00000.06220.21130.03310.02880.04640.0490
2b0.79920.85210.92160.81330.82670.79260.85170.82320.83740.85480.85780.79260.84360.92170.79310.49400.85480.86380.82670.8213

Getting all the statistics in tabular form

tab = evo.tabulate_stats()
stats = tab(result.alt)
stats
5x tabular_result('global params': Table, 'bin params': Table, 'edge params': Table, 'edge bin params': Table)
stats["edge bin params"][:10]  # truncating the table
edge bin params
edgebinomega
Galago01.0000002366422153e-06
Galago11.0
Galago2a1.0000002366422153e-06
Galago2b1.0
HowlerMon01.0000002366422153e-06
HowlerMon11.0
HowlerMon2a1.0000002366422153e-06
HowlerMon2b1.0
Rhesus01.0000002366422153e-06
Rhesus11.0

10 rows x 3 columns