PB Consensus tree - mendessoares/BuddySuite GitHub Wiki
--consensus_tree, -ct
Description
Create a consensus tree from two or more trees.
Argument
Minimum frequency threshold (float).
Optional. A minimum frequency threshold controls whether each bipartition is included in the tree or not (i.e., a particular node is included if it is present in a minimum number of input trees). Some common values:
- 1.0 creates a strict consensus tree
- 0.5 creates a majority rule tree (default)
- 0.0 creates a tree where all possible nodes are presented
Examples
input file: trees.nex
#NEXUS
BEGIN TAXA;
DIMENSIONS NTAX=8;
TAXLABELS
A
B
C
D
E
F
G
H
;
END;
BEGIN TREES;
TREE 1 = [&U] (A:3.0,B:1.0,C:1.0,D:1.0,E:1.0,F:2.0):2.0;
TREE 2 = [&U] (((C:3.0,F:2.0):2.0,B:2.0):1.0,((D:1.0,G:1.0):2.0,H:3.0):1.0):3.0;
TREE 3 = [&U] ((E:1.0,D:1.0,A:1.0):1.0,F:1.0,B:1.0,C:1.0):1.0;
TREE 4 = [&U] (((((B:1.0,C:1.0):4.0,A:1.0):1.0,E:2.0):1.0,D:1.0):4.0,F:3.0):1.0;
END;
usage example 1
$: pb trees.nex -dis
output
#NEXUS
BEGIN TAXA;
DIMENSIONS NTAX=8;
TAXLABELS
A
G
H
B
C
D
E
F
;
END;
BEGIN TREES;
TREE 1 = [&U] (A[&support=0.0][&length_mean=0.0,length_median=0.0,length_sd=0.0,length_hpd95=0.0,length_quant_5_95=0.0,length_range=0.0],G[&support=0.25][&length_mean=1.0,length_median=1.0,length_sd=inf,length_hpd95=None,length_quant_5_95={1.0,1.0},length_range={1.0,1.0}],H[&support=0.25][&length_mean=3.0,length_median=3.0,length_sd=inf,length_hpd95=None,length_quant_5_95={3.0,3.0},length_range={3.0,3.0}],(B[&support=0.75][&length_mean=1.0,length_median=1.0,length_sd=0.0,length_hpd95=None,length_quant_5_95={1.0,1.0},length_range={1.0,1.0}],C[&support=1.0][&length_mean=1.5,length_median=1.0,length_sd=1.0,length_hpd95=None,length_quant_5_95={3.0,3.0},length_range={1.0,3.0}],D[&support=1.0][&length_mean=1.0,length_median=1.0,length_sd=0.0,length_hpd95=None,length_quant_5_95={1.0,1.0},length_range={1.0,1.0}],E[&support=0.75][&length_mean=1.3333333333333333,length_median=1.0,length_sd=0.5773502691896258,length_hpd95=None,length_quant_5_95={2.0,2.0},length_range={1.0,2.0}],F[&support=1.0][&length_mean=3.0,length_median=2.0,length_sd=2.70801280154532,length_hpd95=None,length_quant_5_95={7.0,7.0},length_range={1.0,7.0}])[&support=0.75][&length_mean=1.6666666666666667,length_median=1.0,length_sd=1.1547005383792515,length_hpd95=None,length_quant_5_95={3.0,3.0},length_range={1.0,3.0}])[&support=1.0][&length_mean=1.75,length_median=1.5,length_sd=0.9574271077563381,length_hpd95=None,length_quant_5_95={3.0,3.0},length_range={1.0,3.0}];
END;
usage example 2
$: pb trees.nex -ct 1.0
output
#NEXUS
BEGIN TAXA;
DIMENSIONS NTAX=8;
TAXLABELS
A
B
C
D
E
F
G
H
;
END;
BEGIN TREES;
TREE 1 = [&U] (A[&support=0.0][&length_mean=0.0,length_median=0.0,length_sd=0.0,length_hpd95=0.0,length_quant_5_95=0.0,length_range=0.0],B[&support=0.75][&length_mean=1.0,length_median=1.0,length_sd=0.0,length_hpd95=None,length_quant_5_95={1.0,1.0},length_range={1.0,1.0}],C[&support=1.0][&length_mean=1.5,length_median=1.0,length_sd=1.0,length_hpd95=None,length_quant_5_95={3.0,3.0},length_range={1.0,3.0}],D[&support=1.0][&length_mean=1.0,length_median=1.0,length_sd=0.0,length_hpd95=None,length_quant_5_95={1.0,1.0},length_range={1.0,1.0}],E[&support=0.75][&length_mean=1.3333333333333333,length_median=1.0,length_sd=0.5773502691896258,length_hpd95=None,length_quant_5_95={2.0,2.0},length_range={1.0,2.0}],F[&support=1.0][&length_mean=3.0,length_median=2.0,length_sd=2.70801280154532,length_hpd95=None,length_quant_5_95={7.0,7.0},length_range={1.0,7.0}],G[&support=0.25][&length_mean=1.0,length_median=1.0,length_sd=inf,length_hpd95=None,length_quant_5_95={1.0,1.0},length_range={1.0,1.0}],H[&support=0.25][&length_mean=3.0,length_median=3.0,length_sd=inf,length_hpd95=None,length_quant_5_95={3.0,3.0},length_range={3.0,3.0}])[&support=1.0][&length_mean=1.75,length_median=1.5,length_sd=0.9574271077563381,length_hpd95=None,length_quant_5_95={3.0,3.0},length_range={1.0,3.0}];
END;