ADMIXTOOLS is a widely used software package for calculating admixture statistics and testing population admixture hypotheses.

A typical ADMIXTOOLS workflow generally involves a combination of sed/awk/shell scripting and manual editing to create text configuration files. These are then passed as command-line arguments to one of ADMIXTOOLS commands, and control how to run a particular analysis. The results are then redirected to another file, which has to be parsed by the user to extract values of interest, often using command-line utilities again or (worse) by manual copy-pasting. Finally, the processed results are analysed in R, Excel or another program.

This workflow can be a little cumbersome, especially if one wants to explore many hypotheses involving different combinations of populations. Most importantly, however, it makes it difficult to coduct reproducible research, as it is nearly impossible to construct fully automated “pipelines” that don't require user intervention.

This R package makes it possible to perform all stages of ADMIXTOOLS analyses entirely from R, completely removing the need for “low level” configuration of individual ADMIXTOOLS programs.


Note that in order to use the admixr package, you need a working installation of ADMIXTOOLS! You can find installation instructions here. The software runs on Linux and macOS and these are the two systems that admixr is tested on.

Furthermore, you need to make sure that R can find ADMIXTOOLS binaries on the $PATH. If this is not the case, running library(admixr) will show a warning message with instructions on how to fix this.

To install admixr from GitHub you need to install the package devtools first. To do this, you can simply run (in R):


Furthermore, if you want to follow the examples in this vignette, you will need the tidyverse collection of packages for data manipulation manipulation and plotting, which you can install with:


You definitely don't need tidyverse for working with admixr but it really makes data manipulation and plotting things much easier. I recommend at least giving it a shot.

When everything is ready, you can run the following code to load both packages:


A note about EIGENSTRAT format

ADMIXTOOLS software uses a peculiar set of genetic file formats, which may seem strange if you are used to working with VCF files. However, the basic idea remains the same: we want to store and access SNP data (REF/ALT alleles) of a set of individuals at a defined set of genomic positions.

EIGENSTRAT datasets always contain three kinds of files:

Therefore, a VCF file is essentially a combination of all three files in a single package.

Let's first download a small testing SNP dataset using a built-in admixr function download_data(). This function downloads the data into a temporary directory (you can specify the destination using its dirname argument, in case you want to place it elsewhere). In addition to this, the function returns a shared path/prefix of the whole dataset.

(prefix <- download_data(dirname = tempdir()))
[1] "/var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps"

We can verify that there are indeed three files with this prefix:

list.files(path = dirname(prefix), pattern = basename(prefix), full.names = TRUE)
[1] "/var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.geno"
[2] "/var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.ind" 
[3] "/var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.snp" 

Let's look at their contents:

ind file

Chimp        U  Chimp
Mbuti        U  Mbuti
Yoruba       U  Yoruba
Khomani_San  U  Khomani_San
Han          U  Han
Dinka        U  Dinka
Sardinian    U  Sardinian
Papuan       U  Papuan
French       U  French
Vindija      U  Vindija
Altai        U  Altai
Denisova     U  Denisova

The first column (sample name) and the third column (population label) are generally not the same (sample names often have numerical suffixes to make them unique, etc.), but were kept the same here for simplicity. Importantly, when specifying population/sample names in admixr functions, the information in the third column is what is used. For example, if you have individuals such as “French1”, “French2”, “French3” in the first column of an ind file, all three sharing a “French” population label in the third column, specifying “French” in an admixr function will combine all three samples in a single population, instead of working with each individual separately.

snp file (first 3 lines)

1_832756    1   0.008328    832756  T   G
1_838931    1   0.008389    838931  A   C
1_843249    1   0.008432    843249  A   T

The columns of this file are, in order:

  1. SNP string ID
  2. chromosome
  3. genetic distance
  4. position along a chromosome
  5. reference allele
  6. alternative allele

geno file (first 3 lines)


Each row is one genomic site, each column is a genotype in one individual.

Philosophy of admixr

The goal of admixr is to make ADMIXTOOLS analyses as trivial to run as possible, without having to worry about par/pop/left/right configuration files (as they are known in the jargon of ADMIXTOOLS) and other low-level details.

The only interface between you and ADMIXTOOLS is the following set of R functions:

Anything that would normally require dozens of lines of shell scripts can be often accomplished by running a single line of R code.

Internal representation of EIGENSTRAT data

As we saw above, each EIGENSTRAT dataset has three components. The way this data is internally represented in admixr is using a small S3 R object created using the eigenstrat constructor function. This function accepts the path and prefix of a trio of EIGENSTRAT snp/ind/geno files and returns an R object of the class EIGENSTRAT:

snps <- eigenstrat(prefix)
#> EIGENSTRAT object
#> =================
#> components:
#>   ind file: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.ind
#>   snp file: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.snp
#>   geno file: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.geno

This object encapsulates the paths to all three EIGENSTRAT components and makes it easy to pass the data to different admixr functions.

The following couple of sections describe how to use the admixr package using simple example analyses.

D statistic

Let's say we are interested in the following question: “Which populations today show evidence of Neanderthal admixture?

One way of looking at this is using the following D statistic: \[D(\textrm{present-day human W}, \textrm{African}, \textrm{Neanderthal}, \textrm{Chimp}).\]

\(D\) statistics are based on comparing the proportions of BABA and ABBA sites patterns observed in the data:

\[D = \frac{\textrm{# BABA sites - # ABBA sites}}{\textrm{# BABA sites + # ABBA sites}}.\]

Significant departure of \(D\) from zero indicates an excess of allele sharing between the first and the third population (positive \(D\)), or an excess of allele sharing between the second and the third population (negative \(D\)). If we get \(D\) that is not significantly different from 0, this suggests that the first and second populations form a clade, and don't differ in the rate of allele sharing with the third population (this is the null hypothesis that the data is compared against).

Therefore, our \(D\) statistic above tests whether some modern humans today admixed with Neanderthals, which would increase their genetic affinity to this archaic group compared to Africans (whose ancestors never met Neanderthals).

Let's save some population names first to make our code more concise:

pops <- c("French", "Sardinian", "Han", "Papuan", "Khomani_San", "Mbuti", "Dinka")

Using the admixr package we can then calculate our \(D\) statistic simply by running:

result <- d(W = pops, X = "Yoruba", Y = "Vindija", Z = "Chimp", data = snps)

The result is a following data frame:

W X Y Z D stderr Zscore BABA ABBA nsnps
French Yoruba Vindija Chimp 0.0313 0.006933 4.510 15802 14844 487753
Sardinian Yoruba Vindija Chimp 0.0287 0.006792 4.222 15729 14852 487646
Han Yoruba Vindija Chimp 0.0278 0.006609 4.199 15780 14928 487925
Papuan Yoruba Vindija Chimp 0.0457 0.006571 6.953 16131 14721 487694
Khomani_San Yoruba Vindija Chimp 0.0066 0.006292 1.051 16168 15955 487564
Mbuti Yoruba Vindija Chimp -0.0005 0.006345 -0.074 15751 15766 487642

We can see that in addition to the specified population names, the output table contains additional columns:

While we could certainly make inferences by looking at the \(Z\)-scores, tables in general are not the best representation of this kind of data, especially as the number of samples increases. Instead, we can use the ggplot2 package to plot the results:

ggplot(result, aes(fct_reorder(W, D), D, color = abs(Zscore) > 2)) +
  geom_point() +
  geom_hline(yintercept = 0, linetype = 2) +
  geom_errorbar(aes(ymin = D - 2 * stderr, ymax = D + 2 * stderr))

plot of chunk d_plot

(If you want to more know about data analysis using R, including plotting with ggplot2, I highly recommend this free book.)

We can see that the \(D\) values for Africans are not significantly different from 0, meaning that the data is consistent with the null hypothesis of no Neanderthal ancestry in Africans. On the other hand, the test rejects the null hypothesis for all non-Africans today, suggesting that Neanderthals admixed with the ancestors of present-day non-Africans.

f4 statistic

An alternative way of addressing the previous question is to use the \(f_4\) statistic, which is very similar to \(D\) statistic and can be calculated as:

\[ f_4 = \frac{\textrm{# BABA sites - # ABBA sites}}{\textrm{# sites}}\]

Again, significant departure of \(f_4\) from 0 can be interpreted as evidence of gene flow.

To repeat the previous analysis using \(f_4\) statistic, we can run the function f4():

result <- f4(W = pops, X = "Yoruba", Y = "Vindija", Z = "Chimp", data = snps)
W X Y Z f4 stderr Zscore BABA ABBA nsnps
French Yoruba Vindija Chimp 0.001965 0.000437 4.501 15802 14844 487753
Sardinian Yoruba Vindija Chimp 0.001798 0.000427 4.209 15729 14852 487646
Han Yoruba Vindija Chimp 0.001746 0.000418 4.178 15780 14928 487925
Papuan Yoruba Vindija Chimp 0.002890 0.000417 6.924 16131 14721 487694
Khomani_San Yoruba Vindija Chimp 0.000436 0.000415 1.051 16168 15955 487564
Mbuti Yoruba Vindija Chimp -0.000030 0.000410 -0.074 15751 15766 487642

By comparing this result to the \(D\) statistic analysis above, we can make the same conclusions.

You might be wondering why we have both \(f_4\) and \(D\) if they are so similar. The truth is that \(f_4\) is, among other things, directly informative about the amount of shared genetic drift ("branch length”) between pairs of populations, which is a very useful theoretical property. Other than that, it's often a matter of personal preference and so admixr provides functions for calculating both.

f4-ratio statistic

Now we know that non-Africans today carry some Neanderthal ancestry. But what if we want to know how much Neanderthal ancestry they have? What proportion of their genomes is of Neanderthal origin? To answer questions like this, we can use the \(f_4\)-ratio statistic, which can be formulated in the following way (using a notation of Patterson et al., 2012, who formally described its properties).

\[f_4\textrm{-ratio} = \frac{f_4(A, O; X, C)}{f_4(A, O; B, C)}.\]

Using amidxr, we can calculate \(f_4\)-ratios using the following code (X being a vector of samples which we want to estimate the Neanderthal ancestry in):

result <- f4ratio(X = pops, A = "Altai", B = "Vindija", C = "Yoruba", O = "Chimp", data = snps)

The ancestry proportion (a number between 0 and 1) is given in the alpha column:

A B X C O alpha stderr Zscore
Altai Vindija French Yoruba Chimp 0.023774 0.006176 3.850
Altai Vindija Sardinian Yoruba Chimp 0.024468 0.006071 4.031
Altai Vindija Han Yoruba Chimp 0.022117 0.005892 3.754
Altai Vindija Papuan Yoruba Chimp 0.037311 0.005812 6.420
Altai Vindija Khomani_San Yoruba Chimp 0.003909 0.005913 0.661
Altai Vindija Mbuti Yoruba Chimp 0.000319 0.005717 0.056
ggplot(result, aes(fct_reorder(X, alpha), alpha, color = abs(Zscore) > 2)) +
  geom_point() +
  geom_errorbar(aes(ymin = alpha - 2 * stderr, ymax = alpha + 2 * stderr)) +
  geom_hline(yintercept = 0, linetype = 2) +
  labs(y = "Neandertal ancestry proportion", x = "present-day individual")

plot of chunk f4ratio_plot

We can make several observations:

f3 statistic

The \(f_3\) statistic, also known as the 3-population statistic, is useful whenever we want to:

  1. Estimate the branch length (shared genetic drift) between a pair of populations \(A\) and \(B\) with respect to a common outgroup \(C\). In this case, the higher the \(f_3\) value, the longer the shared evolutionary time between \(A\) and \(B\).
  2. Test whether population \(C\) is a mixture of two populations \(A\) and \(B\). Significantly negative values of the \(f_3\) statistic are then a statistical evidence of this admixture.

As an example, imagine we are interested in relative divergence times between pairs of present-day human populations, and want to know in which approximate order they split of from each other. To address this problem, we could use \(f_3\) statistic by fixing the \(C\) outgroup as San, and calculating pairwise \(f_3\) statistics between all present-day modern humans.

pops <- c("French", "Sardinian", "Han", "Papuan", "Mbuti", "Dinka", "Yoruba")

result <- f3(A = pops, B = pops, C = "Khomani_San", data = snps)
A B C f3 stderr Zscore nsnps
French French Khomani_San 0.000000 -1.000000 0.000 -1
French Sardinian Khomani_San 0.353447 0.012527 28.215 249760
French Han Khomani_San 0.316964 0.011914 26.604 253158
French Papuan Khomani_San 0.306962 0.011708 26.218 251648
French Mbuti Khomani_San 0.119283 0.008448 14.119 271501
French Dinka Khomani_San 0.190141 0.010049 18.922 276964
# sort the population labels according to an increasing f3 value relative to French
ordered <- filter(result, A == "Mbuti", B != "Mbuti") %>% arrange(f3) %>% .[["B"]] %>% c("Mbuti")

# plot heatmap of pairwise f3 values
result %>%
  filter(A != B) %>%
  mutate(A = factor(A, levels = ordered),
         B = factor(B, levels = ordered)) %>%
  ggplot(aes(A, B)) + geom_tile(aes(fill = f3))

plot of chunk f3_plot

We can see that when we order the heatmap labels based on values of pairwise \(f_3\) statistics, the (already known) order of population splits pops up nicely (i.e. San separated first, followed by Mbuti, etc.).

qpWave and qpAdm

Both qpWave and qpAdm can be though of as more complex and powerful extensions of the basic ideas behind a simple \(f_4\) statistic. Building upon the \(f_4\) theory and generalizing it, qpWave makes it possible to find the lowest number of “streams of ancestry” between two groups of populations that is consistent with the data. Extending the concept of \(f_4\) statistics even further, qpAdm allows to find the proportions of ancestry from a set of ancestral populations that contributed ancestry to our population of interest.

Unfortunately, both methods represent a rather advanced topic that still lacks proper documentation and beginner-friendly tutorials, and explaining them in detail is beyond the scope of this vignette. If you want to use them, it's crucial that you read the official documentation decribing the basic ideas of both methods (distributed with ADMIXTOOLS), and that you read the relevant supplementary sections of papers published by David Reich's group. At the very least, I recommend reading:

In the remainder of this section, I will assume that you are familiar with both methods, and will only explain how to use admixr for running them from R.


To run qpWave, you must provide a list of left and right populations (using the terminology of Haak et al. 2015 above). The aim of the method is to get an idea about the number of migration waves from right to left (with no back-migration from left to right!). This is done by estimating the rank of a matrix of all possible \(f_4\) statistics

\[f_4(\textrm{left}_1, \textrm{left}_i; \textrm{right}_1, \textrm{right}_i),\]

where \(\textrm{left}_1\) and \(\textrm{right}_1\) are some fixed populations and the \(i\) and \(j\) indices run over all other possible choices of populations.

As an example, let's try to find the number of admixture waves from right = {Yoruba, Mbuti, Alta} into left = {French, Sardinian, Han} populations. We can do this using the function qpWave(), setting its arguments appropriately:

result <- qpWave(
 left = c("French", "Sardinian", "Han"),
 right = c("Altai", "Yoruba", "Mbuti"),
 data = snps

The qpWave() function returns a data frame which shows the results of a series of matrix rank tests. The rank column is the matrix rank tested, df, chisq and tail give the degrees of freedom, \(\chi^2\) value and \(p\)-value for the comparison with the saturated model (the \(p\)-value then indicates which matrix rank is consistent with the data - see example below), and dfdiff, chisqdiff and taildiff give the same, but always comparing a model to the model with one rank less.

rank df chisq tail dfdiff chisqdiff taildiff
0 4 1.756 0.7805106 0 0.000 1.0000000
1 1 0.192 0.6614968 3 1.564 0.6674998
2 0 0.000 1.0000000 1 0.192 0.6614968

In this example, we see that matrix \(r = 0\) cannot be rejected (tail \(p\)-value = 0.78). Because Reich et al. 2012 showed that \(r + 1 \le n\), where \(n\) is the number of admixture waves, we can interpret this as left populations having at least \(n = 1\) streams of ancestry from the set of right populations. In this case, the most likely explanation is Neandertal admixture into non-Africans today.

Now, what happens if we add Papuans to the left group?

result <- qpWave(
 left = c("Papuan", "French", "Sardinian", "Han"),
 right = c("Altai", "Yoruba", "Mbuti"),
 data = snps
rank df chisq tail dfdiff chisqdiff taildiff
0 6 29.143 0.0000572 0 0.000 1.0000000
1 2 0.601 0.7403643 4 28.542 0.0000097
2 0 0.000 1.0000000 2 0.601 0.7403643

We can now clearly reject rank \(r = 0\), but we see that the data is consistent with rank \(r = 1\), meaning that there must have been at least \(n = 2\) streams of ancestry from right to left populations (\(r + 1 \le n\)). Because this happened after we introduced Papuans to the left set, this could indicate a separate pulse of archaic introgression into Papuans, which is not surprising given what we know about significantly more archaic ancestry in Papuans than in any other present-day population.


The qpAdm method can be used to find, for a given target population, the proportions of ancestry coming from a set of source populations. Importantly, since we often lack accurate representatives of the true ancestral populations, we can use a set of reference populations instead, under a crucial assumption that the references set is phylogenetically closer to true source populations than to a set of specified outgroups. For example, coming back to our example of estimating the proportions of Neandertal ancestry in people today, we could define:

Having defined all three population sets, we can run qpAdm with:

result <- qpAdm(
  target = c("Sardinian", "Han", "French"),
  sources = c("Vindija", "Yoruba"),
  outgroups = c("Chimp", "Denisova", "Altai"),
  data = snps

The qpAdm() function has an argument details (default TRUE) which makes the function return a list of three elements:

If details is set to FALSE, only the proportions components is returned by the qpAdm() function.

Let's start with the ranks element:

target rank df chisq tail dfdiff chisqdiff taildiff
Sardinian 1 1 0.005 0.9447256 3 -0.005 1.0000000
Sardinian 2 0 0.000 1.0000000 1 0.005 0.9447256
Han 1 1 2.096 0.1476639 3 -2.096 1.0000000
Han 2 0 0.000 1.0000000 1 2.096 0.1476639
French 1 1 3.745 0.0529780 3 -3.745 1.0000000
French 2 0 0.000 1.0000000 1 3.745 0.0529780

The row with rank = 1 represents a qpWave test with all \(n\) source populations set as the left set and all outgroups as the right set. This test evaluates whether the ancestral populations are descended from \(n\) independent streams of ancestry. In our case, \(n = 2\) (Mbuti and Vindija), which means that the data would have to be consistent with rank \(r = 1\) to satisfy the inequality \(r + 1 \le n\) proved by Reich et al., 2012. We see that this is true for all three target populations (\(p\)-value > 0.05 for all targets), and the simple model of Neandertal admixture thus seems to be reasonably consistent with the data.

The rank = 2 row represents a qpWave test after adding a target population to the left group together with the sources. This test makes sure that including the target population does not increase the rank of the \(f_4\) matrix, meaning that the target can be really modelled as a mixture of ancestries from the sources. If the \(p\)-values turn out to be very low, this indicates that the assumed model does not fit the data and that a part of the ancestry in a target possibly cannot be traced to any of the sources. In our case, however, all rank = 2 test \(p\)-values are not significant, and we can be reasonably sure that the target samples can be fully modelled as a mixtures of all specified references.

The most important element of a qpAdm output is in the $proportions component. This contains admixture proportion estimates from all specified sources, the p-values of each model (remember, low p-values/significance means the model is rejected!) as well as standard errors for those proportions using a block jackknife:

target Vindija Yoruba stderr_Vindija stderr_Yoruba nsnps_used nsnps_target pvalue
Sardinian 0.024 0.976 0.006 0.006 5e+05 499314 0.944726
Han 0.020 0.980 0.006 0.006 5e+05 499654 0.147664
French 0.022 0.978 0.006 0.006 5e+05 499434 0.052978

If we compare this result to the \(f_4\)-ratio values calculated above, we see that the qpAdm estimates are very close to what we got earlier.

The third element in the list of results shows the outcome of an “all subsets” analysis, which involves testing all subsets of potential source populations. Each 1 in the “pattern” column means that the proportion of ancestry from that particular source population (in the order as specified by the user) was forced to 0.0.

target pattern wt dof chisq tail Vindija Yoruba
Sardinian 00 0 1 0.005 0.9447260 0.024 0.976
Sardinian 01 1 2 15795.353 0.0000000 1.000 0.000
Sardinian 10 1 2 16.295 0.0002895 0.000 1.000
Han 00 0 1 2.096 0.1476640 0.020 0.980
Han 01 1 2 14809.047 0.0000000 1.000 0.000
Han 10 1 2 14.239 0.0008092 0.000 1.000
French 00 0 1 3.745 0.0529780 0.022 0.978
French 01 1 2 15326.662 0.0000000 1.000 0.000
French 10 1 2 16.102 0.0003188 0.000 1.000

New feature: There is a new function called qpAdm_rotation() which allows exhaustive exploration of many qpAdm models in paralle. For more information please see the vignette “Fitting qpAdm models with a 'rotation' strategy”.

Grouping samples

What we've been doing so far was calculating statistics for individual samples. However, it is often useful to treat multiple samples as a single group or population. admixr provides a function called relabel() that does just that.

Here is an example: let's say we want to run a similar analysis to the one described in the \(D\) statistic section, but we want to treat Europeans, Africans and archaics as combined populations, and not as separate individuals. But the ind file that we have does not contain grouped labels - each sample stands on its own:

Chimp        U  Chimp
Mbuti        U  Mbuti
Yoruba       U  Yoruba
Khomani_San  U  Khomani_San
Han          U  Han
Dinka        U  Dinka
Sardinian    U  Sardinian
Papuan       U  Papuan
French       U  French
Vindija      U  Vindija
Altai        U  Altai
Denisova     U  Denisova

To merge several individual samples under a combined label we can call relabel() like this:

# paths to the original ind file and a new modified ind file, which will
# contain merged population labels
modif_snps <- relabel(
  European = c("French", "Sardinian"),
  African = c("Dinka", "Yoruba", "Mbuti", "Khomani_San"),
  Archaic = c("Vindija", "Altai", "Denisova")
#> EIGENSTRAT object
#> =================
#> components:
#>   ind file: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.ind
#>   snp file: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.snp
#>   geno file: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.geno
#> modifiers:
#>   groups:  /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/file1860b66c6f44e.ind

We can see that the function relabel returned a modified EIGENSTRAT object, which contains a new item in the “modifiers” section - the path to a new ind file. Let's look at its contents:

Chimp        U  Chimp
Mbuti        U  African
Yoruba       U  African
Khomani_San  U  African
Han          U  Han
Dinka        U  African
Sardinian    U  European
Papuan       U  Papuan
French       U  European
Vindija      U  Archaic
Altai        U  Archaic
Denisova     U  Archaic

Having the modified EIGENSTRAT object ready, we can then use “European”, “African” and “Archaic” names in any of the admixr wrapper functions described above. For example:

result <- d(W = "European", X = "African", Y = "Archaic", Z = "Chimp", data = modif_snps)

Here is the result, showing again Europeans show genetic affinity to archaic humans compared to Africans today:

W X Y Z D stderr Zscore BABA ABBA nsnps
European African Archaic Chimp 0.0225 0.004404 5.117 15487 14805 489003

Note that the d() function correctly picks up the “group modifier” ind file from the provided EIGENSTRAT object and uses it in place of the original ind file.

Counting present/missing SNPs

The count_snps function can be useful for quality control, weighting of admixture statistics (\(D\), \(f_4\), etc.) in regression analyses etc. There are two optional arguments:

For each sample, count the SNPs present in that sample:

id sex label present
Chimp U Chimp 491273
Mbuti U Mbuti 499334
Yoruba U Yoruba 499246
Khomani_San U Khomani_San 499250
Han U Han 499654
Dinka U Dinka 499362
Sardinian U Sardinian 499314
Papuan U Papuan 499377
French U French 499434
Vindija U Vindija 497544
Altai U Altai 497729
Denisova U Denisova 497398

Data filtering

Filtering based on a BED file

It is quite common to repeat a particular analysis only on a subset of the genome (such as intergenic sites, etc). However, EIGENSTRAT is a rather obscure file format which is generally not supported by standard bioinformatics tools. Luckily, admixr includes a function filter_bed() that takes an EIGENSTRAT object and a BED file as its inputs and produces a new object that contains a modifier called “excluded”, linking to a snp file with coordinates of sites that did not pass the filtering and will be excluded from later analyses.

bed <- file.path(dirname(prefix), "regions.bed")
# BED file contains regions to keep in an analysis
new_snps <- filter_bed(snps, bed)

# BED file contains regions to remove from an analysis
new_snps <- filter_bed(snps, bed, remove = TRUE)
#> EIGENSTRAT object
#> =================
#> components:
#>   ind file: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.ind
#>   snp file: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.snp
#>   geno file: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.geno
#> modifiers:
#>   excluded sites:  /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/file1860b13b8946b.snp 
#>       (SNPs excluded: 100000, SNPs remaining: 400000)

If we want to run the whole analysis in a single pipeline, we can use the %>% pipe operator and do the following:

(The %>% operator takes what is on its left side and puts it as a first argument of a function on the right side. While it takes some time to get used to, it is very useful in longer multi-step “pipelines” because it makes more pipelines much more readable. In fact, the resulting code often reads almost like English! The %>% pipe is automatically imported when you load the tidyverse library, and you can read about it more here.)

snps %>%
  filter_bed("regions.bed") %>%
  d(W = "French", X = "Mbuti", Y = "Vindija", Z = "Chimp")

This is because in the formal definitions of admixr function, data = is always the argument, so we don't have to specify it manually.

Important: The filter_bed() function makes it very easy to do filtering without worrying about locations of intermediate files, but it is important to keep in mind that the function still creates temporary files under the hood. If you plan to run many independent calculations on a filtered subset of the data, it's better to save the new EIGENSTRAT object to a variable first and re-use the same object multiple times, rather than running the whole pipeline for each analysis separately (which would create new copies of intermediate files for each iteration).

Filtering out potential ancient DNA damage SNPs

In the field of ancient DNA, we often need to repeat an analysis on a subset of data that is less likely to be influenced by ancient DNA damage, to verify that our results are not caused by artifacts in the data (due to biochemical properties of DNA degradation, ancient DNA damage will lead to an increase in C→T and G→A substitutions). Using a similar method described in the BED filtering section above, we can use the transversions_only() function to generate a snp file with positions that carry transitions (C→T and G→A sites):

new_snps <- transversions_only(snps)

# perform the calculation only on transversions
d(W = "French", X = "Dinka", Y = "Altai", Z = "Chimp", data = new_snps)

Again, we could combine several filtering steps into one pipeline:

snps %>%                                    # take the original data
  filter_bed("regions.bed", remove = TRUE) %>%  # remove sites not in specified regions
  transversions_only() %>%                      # remove potential false SNPs due to aDNA damage
  d(W = "French", X = "Dinka", Y = "Altai", Z = "Chimp") # calculate D on the filtered dataset

Merging EIGENSTRAT datasets

Another useful data processing function is merge_eigenstrat(). This function takes two EIGENSTRAT datasets and merges them, producing a union of samples and intersection of SNPs from both of them and returning a new EIGENSTRAT object.

# this is just an example code - it will not run unless you specify the paths
merged <- merge_eigenstrat(
    merged = <"prefix of the merged dataset">
    a = first_EIGENSTRAT_object,
    b = second_EIGENSTRAT_object

Examining log information

The goal of admixr is to abstract away all the low-level technical workings of ADMIXTOOLS. As we saw in the examples above, it achieves this by doing all the dirty work of parsing the output files generated by ADMIXTOOLS, presenting the user with convenient R data structures.

Nevertheless, admixr cannot (yet) parse all the information generated by ADMIXTOOLS utilities. For calculating D statistics, \(f_4\) statistics and doing other simple analyses, a single data frame is usually all the user needs. However, sometimes things go wrong - it turns out data was in a wrong format, or ADMIXTOOLS crashes with an error or the results simply look suspicious. Furthermore, some complex commands (such as qpAdm) are not yet completely implemented in admixr. In any of these cases, it is still useful (and important!) to examine the log outputs.

Each admixr result, such as the data frame object below, contains a (hidden!) attribute which carries the complete log file associate with that particular analysis:

dres <- d(W = c("French", "Han", "Dinka"), X = "Yoruba", Y = "Vindija", Z = "Chimp", data = snps)

#> # A tibble: 3 x 10
#>   W      X      Y       Z           D  stderr Zscore  BABA  ABBA  nsnps
#>   <chr>  <chr>  <chr>   <chr>   <dbl>   <dbl>  <dbl> <dbl> <dbl>  <dbl>
#> 1 French Yoruba Vindija Chimp  0.0313 0.00693  4.51  15802 14844 487753
#> 2 Han    Yoruba Vindija Chimp  0.0278 0.00661  4.20  15780 14928 487925
#> 3 Dinka  Yoruba Vindija Chimp -0.0009 0.00612 -0.151 15131 15159 487667

We can examine the full log of this D statistic run with the loginfo function. Without any further arguments, this function writes the complete log on the screen. Note that we run this function on the data frame object although it might appear that it does not contain any other information (it does but it's normally hidden as most of the time it's not useful):

#> Full output log of qpDstat:
#> ===================================================
#> qpDstat: parameter file: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/qpDstat__470148940.par
#> genotypename: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.geno
#> snpname: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.snp
#> indivname: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.ind
#> popfilename: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/qpDstat__470148940.pop
#> printsd: YES
#> ## qpDstat version: 970
#> number of quadruples 3
#>   0               French    1
#>   1                  Han    1
#>   2                Dinka    1
#>   3               Yoruba    1
#>   4              Vindija    1
#>   5                Chimp    1
#> jackknife block size:     0.050
#> snps: 500000  indivs: 6
#> number of blocks for block jackknife: 547
#> nrows, ncols: 6 500000
#> result:     French     Yoruba    Vindija      Chimp      0.0313      0.006933     4.510   15802  14844 487753 
#> result:        Han     Yoruba    Vindija      Chimp      0.0278      0.006609     4.199   15780  14928 487925 
#> result:      Dinka     Yoruba    Vindija      Chimp     -0.0009      0.006124    -0.151   15131  15159 487667 
#> ##end of qpDstat:        3.746 seconds cpu        0.000 Mbytes in use

The situation is a bit more complex for qpAdm which can be evaluated for multiple target populations/samples which are then analyzed separately, with each analysis having its own log file:

qpadm_res <- qpAdm(
  target = c("Sardinian", "Han"),
  sources = c("Vindija", "Yoruba"),
  outgroups = c("Chimp", "Denisova", "Altai"),
  data = snps

#> $proportions
#> # A tibble: 2 x 8
#>   target Vindija Yoruba stderr_Vindija stderr_Yoruba nsnps_used nsnps_target
#>   <chr>    <dbl>  <dbl>          <dbl>         <dbl>      <dbl>        <dbl>
#> 1 Sardi…   0.024  0.976          0.006         0.006     500000       499314
#> 2 Han      0.02   0.98           0.006         0.006     500000       499654
#> # … with 1 more variable: pvalue <dbl>
#> $ranks
#> # A tibble: 4 x 8
#>   target     rank    df chisq  tail dfdiff chisqdiff taildiff
#>   <chr>     <dbl> <dbl> <dbl> <dbl>  <dbl>     <dbl>    <dbl>
#> 1 Sardinian     1     1 0.005 0.945      3    -0.005    1    
#> 2 Sardinian     2     0 0     1          1     0.005    0.945
#> 3 Han           1     1 2.10  0.148      3    -2.10     1    
#> 4 Han           2     0 0     1          1     2.10     0.148
#> $subsets
#> # A tibble: 6 x 8
#>   target    pattern    wt   dof     chisq     tail Vindija Yoruba
#>   <chr>     <chr>   <dbl> <dbl>     <dbl>    <dbl>   <dbl>  <dbl>
#> 1 Sardinian 00          0     1     0.005 0.945      0.024  0.976
#> 2 Sardinian 01          1     2 15795.    0          1      0    
#> 3 Sardinian 10          1     2    16.3   0.000289   0      1    
#> 4 Han       00          0     1     2.10  0.148      0.02   0.98 
#> 5 Han       01          1     2 14809.    0          1      0    
#> 6 Han       10          1     2    14.2   0.000809   0      1

In this case, we could either print all log information (one log output for each target we specified in the qpAdm call) by runningloginfo(qpadm_res) or, perhaps more useful, specify which target's log file we want to examine:

loginfo(qpadm_res, target = "Han")
#> Full output log of qpAdm for target 'Han':
#> ===================================================
#> qpAdm: parameter file: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/qpAdm__171069246.par
#> genotypename: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.geno
#> snpname: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.snp
#> indivname: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/snps/snps.ind
#> popleft: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/qpAdm__171069246.popleft
#> popright: /var/folders/t7/9gjtb6m92flbnp930618vt3r0000gn/T//Rtmpxiy5JE/qpAdm__171069246.popright
#> allsnps: YES
#> summary: YES
#> details: YES
#> ## qpAdm version: 1201
#> seed: 410343441
#> left pops:
#> Han
#> Vindija
#> Yoruba
#> right pops:
#> Chimp
#> Denisova
#> Altai
#>   0                  Han    1
#>   1              Vindija    1
#>   2               Yoruba    1
#>   3                Chimp    1
#>   4             Denisova    1
#>   5                Altai    1
#> jackknife block size:     0.050
#> snps: 500000  indivs: 6
#> number of blocks for block jackknife: 547
#> ## ncols: 500000
#> coverage:                  Han 499654
#> coverage:              Vindija 497544
#> coverage:               Yoruba 499246
#> coverage:                Chimp 491273
#> coverage:             Denisova 497398
#> coverage:                Altai 497729
#> Effective number of blocks:   489.823
#> numsnps used: 500000
#> codimension 1
#> f4info: 
#> f4rank: 1 dof:      1 chisq:     2.096 tail:          0.147663931 dofdiff:      3 chisqdiff:    -2.096 taildiff:                    1
#> B:
#>           scale     1.000 
#>        Denisova     0.475 
#>           Altai     1.332 
#> A:
#>           scale    27.655 
#>         Vindija     1.414 
#>          Yoruba    -0.029 
#> full rank
#> f4info: 
#> f4rank: 2 dof:      0 chisq:     0.000 tail:                    1 dofdiff:      1 chisqdiff:     2.096 taildiff:          0.147663931
#> B:
#>           scale    19.608   759.097 
#>        Denisova     0.470    -0.810 
#>           Altai     1.334    -1.160 
#> A:
#>           scale     1.414     1.414 
#>         Vindija     1.414     0.000 
#>          Yoruba     0.000     1.414 
#> best coefficients:     0.020     0.980 
#> Jackknife mean:      0.020445464     0.979554536 
#>       std. errors:     0.006     0.006 
#> error covariance (* 1,000,000)
#>         33        -33 
#>        -33         33 
#> summ: Han    2      0.147664     0.020     0.980         33        -33         33 
#>     fixed pat  wt  dof     chisq       tail prob
#>            00  0     1     2.096        0.147664     0.020     0.980 
#>            01  1     2 14809.047               0     1.000     0.000 
#>            10  1     2    14.239     0.000809235     0.000     1.000 
#> best pat:           00         0.147664              -  -
#> best pat:           10      0.000809235  chi(nested):    12.143 p-value for nested model:     0.000492824
#> coeffs:     0.020     0.980 
#> ## dscore:: f_4(Base, Fit, Rbase, right2)
#> ## genstat:: f_4(Base, Fit, right1, right2)
#> details:              Vindija             Denisova     0.023947   40.866731
#> details:               Yoruba             Denisova    -0.001067   -2.572745
#> dscore:             Denisova f4:    -0.000556 Z:    -1.350781
#> details:              Vindija                Altai     0.068033  120.675791
#> details:               Yoruba                Altai    -0.001528   -3.697347
#> dscore:                Altai f4:    -0.000107 Z:    -0.260309
#> gendstat:                Chimp             Denisova    -1.351
#> gendstat:                Chimp                Altai    -0.260
#> gendstat:             Denisova                Altai     1.118
#> ##end of qpAdm:        3.601 seconds cpu        0.000 Mbytes in use

Finally, we might want to keep some of the log files around for future reference, further debugging or sharing with others. For these purposes, we can run the loginfo function with the argument save = TRUE, potentially also specifying the output directory (dir = "." by default) or a prefix of the output file(s) (default prefix is simply the name of the admixr command which generated the log file).

For example, the following will save the result of our qpAdm analysis of Neandertal ancestry to a file with a prefix “qpAdm_Neandertal_ancestry”, but just the one for the Sardinian individual:

loginfo(qpadm_res, target = "Sardinian", save = TRUE, prefix = "qpAdm_Neandertal_ancestry")

This will create a file in the current directory named qpAdm_Neandertal_ancestry_Sardinian.txt. Note that when you call loginfo() on a qpAdm run, the name of the target is always added to the end of the output log file.

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This work is licensed under a Creative Commons Attribution 4.0 International License.