ipADMIXTURE: Iterative Pruning Population Admixture Inference Framework

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A data clustering package based on admixture ratios (Q matrix) of population structure.

The framework is based on iterative Pruning procedure that performs data clustering by splitting a given population into subclusters until meeting the condition of stopping criteria the same as ipPCA, iNJclust, and IPCAPS frameworks.

The package also provides a function to retrieve phylogeny tree that construct a neighbor-joining tree based on a similar matrix between clusters.

By given multiple Q matrices with varying a number of ancestors (K), the framework define a similar value between clusters i,j as a minimum number K* that makes majority of members of two clusters are in the different clusters. This K* reflexes a minimum number of ancestors we need to splitting cluster i,j into different clusters if we assign K* clusters based on maximum admixture ratio of individuals.


For the newest version on github, please call the following command in R terminal.


This requires a user to install the “remotes” package before installing ipADMIXTURE.


In this example, we have data set of human 27 population data published by Xing, J., et al. (2009). The dataset consists of 544 individuals from 27 populations. The Q matrices from this data are provided in this package. The following steps are the simple way to use our package.

Step1: running the ipADMIXTURE using Human 27 population dataset where the number of ancestors K =12.

# # running area: ipADMIXTURE::human27pop_Qmat[[i]] is a Q matrix with K=i+1
h27pop_obj<-ipADMIXTURE(Qmat=ipADMIXTURE::human27pop_Qmat[[11]], admixRatioThs =0.15)

Step2: printing all cluster information in text mode.


Then, the text looks like this

[1] "Overall labels"
[1] "==============="
[1] "Alur(10)Hema(15)Pygmy(25)Brahmin(25)Utah_N._European(25)Cambodian(5)Chinese(10)Tamil_LC(13)Irula(24)JPN2(13)Madiga(10)Mala(11)CEU(60)YRI(60)CHB(45)JPT(45)Luhya(24)Tuscan(25)Kung(13)Pedi(10)Sotho/Tswana(8)Stalskoe(5)Iban(25)TBrahmin(14)Urkarah(18)VN(7)Nguni(9)"
[1] "==============="
[1] "ID1, md0.05, N25"
[1] "Pygmy(25/25)"
[1] "==============="
[1] "ID2, md0.13, N56"
[1] "JPN2(12/13)JPT(44/45)"
[1] "==============="
[1] "ID3, md0.00, N12"
[1] "Kung(12/13)"
[1] "==============="
[1] "ID4, md0.00, N25"
[1] "Iban(25/25)"
[1] "==============="
[1] "ID5, md0.00, N69"
[1] "Cambodian(5/5)Chinese(10/10)JPN2(1/13)CHB(45/45)JPT(1/45)VN(7/7)"
[1] "==============="
[1] "ID6, md0.06, N25"
[1] "Utah_N._European(1/25)Tuscan(24/25)"
[1] "==============="
[1] "ID7, md0.09, N85"
[1] "Utah_N._European(24/25)CEU(60/60)Tuscan(1/25)"
[1] "==============="
[1] "ID8, md0.00, N17"
[1] "Urkarah(17/18)"
[1] "==============="
[1] "ID9, md0.00, N6"
[1] "Stalskoe(5/5)Urkarah(1/18)"
[1] "==============="
[1] "ID10, md0.00, N4"
[1] "Irula(4/24)"
[1] "==============="
[1] "ID11, md0.00, N10"
[1] "Irula(10/24)"
[1] "==============="
[1] "ID12, md0.00, N9"
[1] "Irula(9/24)"
[1] "==============="
[1] "ID13, md0.00, N33"
[1] "Tamil_LC(13/13)Madiga(9/10)Mala(11/11)"
[1] "==============="
[1] "ID14, md0.08, N41"
[1] "Brahmin(25/25)Irula(1/24)Madiga(1/10)TBrahmin(14/14)"
[1] "==============="
[1] "ID15, md0.00, N4"
[1] "Pedi(2/10)Sotho/Tswana(2/8)"
[1] "==============="
[1] "ID16, md0.00, N20"
[1] "Pedi(5/10)Sotho/Tswana(6/8)Nguni(9/9)"
[1] "==============="
[1] "ID17, md0.00, N4"
[1] "Kung(1/13)Pedi(3/10)"
[1] "==============="
[1] "ID18, md0.04, N60"
[1] "YRI(60/60)"
[1] "==============="
[1] "ID19, md0.00, N4"
[1] "Hema(2/15)Luhya(2/24)"
[1] "==============="
[1] "ID20, md0.00, N2"
[1] "Luhya(2/24)"
[1] "==============="
[1] "ID21, md0.07, N20"
[1] "Luhya(20/24)"
[1] "==============="
[1] "ID22, md0.12, N23"
[1] "Alur(10/10)Hema(13/15)"

For any cluster, it is separated from other cluster by “===============”. The first line of cluster details is “IDx, md0.xx, Nx” and the second line is a detail of populations from the ground truth.

For example, [1] “ID19, md0.00, N4” [1] “Hema(2/15)Luhya(2/24)”.

This is a cluster ID19 that has a maximum of manitude-difference of admixture ratios (md) as 0.00 and there are 4 individuals in this cluster. For a second line, there are 2 individuals from Hema population where the total number of Hema members is 15. There are also 2 individuals out of 24 from Luhya population.

Step3: plotting admixture ratios and clustering assignment.


Step4: plotting clustering information in treemap plot


Step5: Inferring phylogenetic tree of clusters based on a list of Q matrices that varies K using neighbor-joining (NJ) method.

plot(out$tree,type = "unrooted")

The leave nodes are cluster IDs.

Creating Q matrix from .geno file using R

There are two well-known software to get Q matrix: ADMIXTURE and STRUCTURE. However, if you want to have everything in R, then here’s the solution.

We can use LEA package to convert .geno file into Q matrix. If you never install bioconductor, then you should run the following code.

if (!requireNamespace("BiocManager", quietly = TRUE))

You can install LEA package by the BiocManager below.


Suppose we have “yourfile.geno” and we want to get the Q matrix with 4 ancestors, then we can run the following code.

obj.snmf = LEA::snmf(input.file="yourfile.geno", K = 4, project = project, iterations= iterations)
 Qmat = LEA::Q(obj.snmf, K = K)