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 evo, io

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
4.964730.1744
hypothesiskeylnLnfpDLCunique_Q
null'GNC-null'-6708.311924TrueTrue
alt'GNC-alt'-6705.829627TrueTrue
result.alt.lf

GNC-alt

log-likelihood = -6705.8296

number of free parameters = 27

Global params
A>CA>GA>TC>AC>GC>TG>AG>CG>TT>A
0.85563.53530.97471.65912.19436.26048.01281.24220.79441.2671
continuation
T>C
2.9654
Bin params
binbprobs
00.0528
10.2664
2a0.0407
2b0.6401
Edge params
edgeparentlength
Galagoroot0.5420
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
Galago01.0000001376971116e-06
Galago11.0
Galago2a1.0000001376971116e-06
Galago2b1.0
HowlerMon01.0000001376971116e-06
HowlerMon11.0
HowlerMon2a1.0000001376971116e-06
HowlerMon2b1.0
Rhesus01.0000001376971116e-06
Rhesus11.0
Rhesus2a1.0000001376971116e-06
Rhesus2b1.0
Orangutan01.0000001376971116e-06
Orangutan11.0
Orangutan2a1.0000001376971116e-06
Orangutan2b1.0
Gorilla01.0000001376971116e-06
Gorilla11.0
Gorilla2a1.0000001376971116e-06
Gorilla2b1.0
Human01.0000001376971116e-06
Human11.0
Human2a19.99999997623967
Human2b19.99999997623967
Chimpanzee01.0000001376971116e-06
Chimpanzee11.0
Chimpanzee2a19.99999997623967
Chimpanzee2b19.99999997623967
edge.001.0000001376971116e-06
edge.011.0
edge.02a1.0000001376971116e-06
edge.02b1.0
edge.101.0000001376971116e-06
edge.111.0
edge.12a1.0000001376971116e-06
edge.12b1.0
edge.201.0000001376971116e-06
edge.211.0
edge.22a1.0000001376971116e-06
edge.22b1.0
edge.301.0000001376971116e-06
edge.311.0
edge.32a1.0000001376971116e-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
00.07540.04240.00000.06660.05820.07950.04270.06040.05150.04080.03890.07950.04770.00000.07920.26000.04080.03530.05820.0615
10.25550.27100.29390.25970.26370.25350.27090.26260.26670.27160.27250.25350.26850.29360.25370.15690.27160.27430.26370.2620
2a0.05730.03320.00000.05090.04480.06020.03330.04640.03990.03200.03060.06020.03710.00000.06000.20410.03200.02780.04480.0473
2b0.61180.65340.70610.62280.63340.60670.65310.63060.64190.65560.65800.60670.64680.70640.60710.37890.65560.66270.63340.6292

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, 'motif params': Table)
stats["edge bin params"][:10]  # truncating the table
edge bin params
edgebinomega
Galago01.0000001376971116e-06
Galago11.0
Galago2a1.0000001376971116e-06
Galago2b1.0
HowlerMon01.0000001376971116e-06
HowlerMon11.0
HowlerMon2a1.0000001376971116e-06
HowlerMon2b1.0
Rhesus01.0000001376971116e-06
Rhesus11.0

10 rows x 3 columns