Preparing concordance factors¶

In this notebook, we will read a table with all bucky concordance factors, translate them from individuals to species and then average over species. Finally, we will export this table as csv so we can use in further analyses.

Let's start by loading packages

using Pkg
pkgs = Pkg.installed()
pkgs["PhyloNetworks"]

┌ Warning: Pkg.installed() is deprecated
└ @ Pkg /scratch/pkrastev/lmod_build/julia-1.5.1/usr/share/julia/stdlib/v1.5/Pkg/src/Pkg.jl:554

v"0.11.0"

import PhyloNetworks
using CSV
using DataFrames

CF = PhyloNetworks.mapAllelesCFtable("pnetworks_pops.txt","test_cf.csv")

┌ Warning: some alleles in the mapping file do not occur in the quartet CF data. Extra allele names will be ignored
└ @ PhyloNetworks /n/home08/souzademedeiros/.julia/packages/PhyloNetworks/zeoh2/src/multipleAlleles.jl:251

Something is wrong with readTableCF!, it is not averaging over species:

dataCF_summarized = PhyloNetworks.readTableCF!(CF)

found 1 4-taxon sets uninformative about between-species relationships, out of 49.
These 4-taxon sets will be deleted from the data frame. 48 informative 4-taxon sets will be used.
between 6.0 and 151.0 gene trees per 4-taxon set

Object DataCF
number of quartets: 48

When we use mergeRows explicitly, it works:

dataCF_summarized = PhyloNetworks.readTableCF!(PhyloNetworks.mergeRows(CF, [1,2,3,4,5,8,11,14]))

10 unique 4-taxon sets were found. CF values of repeated 4-taxon sets will be averaged (ngenes too).
between 32.375 and 151.0 gene trees per 4-taxon set

Object DataCF
number of quartets: 10

	taxon1	taxon2	taxon3	taxon4	CF12_34	CF12_34_lo
	String	String	String	String	Float64	Float64
1	botryophora_I	botryophora_I	botryophora_I	botryophora_I	0.3333	0.0
2	coronata_H	coronata_B	botryophora_I	coronata_A	0.160389	0.0
3	coronata_H	coronata_B	botryophora_I	coronata_A	0.74	0.280702
4	coronata_H	coronata_B	botryophora_I	coronata_A	0.566071	0.0714286
5	coronata_H	coronata_D	botryophora_I	coronata_A	0.1802	0.0
6	coronata_H	coronata_D	botryophora_I	coronata_A	0.188938	0.0
7	coronata_H	coronata_D	botryophora_I	coronata_A	0.173659	0.0
8	coronata_H	coronata_D	botryophora_I	coronata_A	0.32281	0.0
9	coronata_H	coronata_F	botryophora_I	coronata_A	0.105029	0.0
10	coronata_H	coronata_F	botryophora_I	coronata_A	0.242671	0.0273973
11	coronata_H	coronata_F	botryophora_I	coronata_A	0.516583	0.148148
12	coronata_H	coronata_F	botryophora_I	coronata_A	0.863148	0.477273
13	coronata_H	coronata_F	botryophora_I	coronata_A	0.444919	0.116279
14	coronata_H	coronata_F	botryophora_I	coronata_A	0.41586	0.0465116
15	coronata_H	kellyana_C	botryophora_I	coronata_A	0.287558	0.0620155
16	coronata_H	kellyana_C	botryophora_I	coronata_A	0.487256	0.116279
17	coronata_H	cf_vagans	botryophora_I	coronata_A	0.00780233	0.0
18	coronata_H	cf_vagans	botryophora_I	coronata_A	0.00751163	0.0
19	coronata_H	cearensis_E	botryophora_I	coronata_A	0.383045	0.120301
20	coronata_H	cearensis_E	botryophora_I	coronata_A	0.439526	0.142857
21	coronata_H	oleracea_C	botryophora_I	coronata_A	0.0858095	0.0
22	coronata_H	oleracea_C	botryophora_I	coronata_A	0.0865952	0.0
23	coronata_H	coconut_ref	botryophora_I	coronata_A	0.00427815	0.0
24	coronata_H	x_costae_E	botryophora_I	coronata_A	0.667667	0.259259
25	coronata_H	x_costae_E	botryophora_I	coronata_A	0.569533	0.2
26	coronata_H	coronata_G	botryophora_I	coronata_A	0.701831	0.112676
27	coronata_H	coronata_G	botryophora_I	coronata_A	0.730139	0.361111
28	coronata_H	coronata_G	botryophora_I	coronata_A	0.584417	0.152778
29	coronata_H	coronata_G	botryophora_I	coronata_A	0.706727	0.25974
30	coronata_H	coronata_G	botryophora_I	coronata_A	0.670895	0.0877193
⋮	⋮	⋮	⋮	⋮	⋮	⋮