dadi-cli 0.8.1

A command line interface for dadi

dadi-cli

dadi-cli provides a robust and user-friendly command line interface for dadi¹ to help users to quickly apply dadi to their research. dadi is a flexible python package for inferring demographic history and the distribution of fitness effects (DFE) from population genomic data. However, using dadi requires knowledge of python and patience to tune different models.

Installation

To install dadi-cli, users should clone this repo and use the following command

python setup.py install

To get help information, users can use

dadi-cli -h

There are nine subcommands in dadi-cli:

• GenerateFs
• GenerateCache
• InferDM
• InferDFE
• BestFit
• Stat
• Plot
• Model
• Pdf

To display help information for each subcommand, users can use -h. For example,

dadi-cli GenerateFs -h

The workflow

Usage: An Example

Here we use the data from the 1000 Genomes Project to demonstrate how to apply dadi-cli in research.

Generating allele frequency spectrum from VCF files

dadi-cli only accepts VCF files to generate allele frequency spectra. To generate a spectrum, users can use

dadi-cli GenerateFs --vcf ./examples/data/1KG.YRI.CEU.biallelic.synonymous.snps.withanc.strict.vcf.gz --pop-info ./examples/data/1KG.YRI.CEU.popfile.txt --pop-ids YRI CEU --projections 20 20 --polarized --output ./examples/results/fs/1KG.YRI.CEU.20.synonymous.snps.unfold.fs

dadi-cli GenerateFs --vcf ./examples/data/1KG.YRI.CEU.biallelic.nonsynonymous.snps.withanc.strict.vcf.gz --pop-info ./examples/data/1KG.YRI.CEU.popfile.txt --pop-ids YRI CEU --projections 20 20 --polarized --output ./examples/results/fs/1KG.YRI.CEU.20.nonsynonymous.snps.unfold.fs

Here ./examples/data/1KG.YRI.CEU.popfile.txt is a file providing the population information for each individual. In the population information file, each line contains two fields. The first field is the name of the individual, and the second field is the name of the population that the individual belongs to. For example,

NA12718	CEU
NA12748	CEU
NA12775	CEU
NA19095	YRI
NA19096	YRI
NA19107	YRI

--pop-ids specifies the ID of the population. Here we have two populations YRI and CEU. The population IDs should match those listed in the population information file above.

--projections specifies the sample size of the population. Here we have 108 YRI individuals and 99 CEU individuals. Therefore, we have 216 and 198 haplotypes for YRI and CEU respectively. We use a lower sample size here, because it allows us to speed up examples.

By default, dadi-cli generates folded spectra. To generate unfolded spectra, users should add --polarized and the VCF files should have the AA in the INFO field to specify the ancestral allele for each SNP.

While making the spectrum, users can also mask the singleton calls that are exclusive to the population(s) with --mask-singleton or mask the exclusive and shared singleton calls with --mask-singleton-shared.

Inferring demographic models

In this example, we infer a demographic model from the spectrum for synonymous SNPs. Here, we use the split_mig model. In this model, the ancestral population diverges into two populations, which then have an instantaneous change of population size with migration between the two populations over time. To see descriptions of the four parameters of the split_mig model, use dadi-cli Model --names split_mig. By default, with unfolded data an additional parameter is added, which quantifies the proportion of sites for which the ancestral state was misidentified. (To disable this, use the --nomisid option.) Therefore, we have five parameters in total.

To start the inference, users should specify the lower bounds and upper bounds for these parameters with --lbounds and --ubounds. For demographic models, setting parameter boundaries prevents optimizers from going into parameter spaces that are hard for dadi to calculate, such as low population size, high time, and high migration. In this case, we set the range of relative population sizes to be explored as 1e-3 to 100, the range of divergence time to 0 to 1, the range of migration rates from 0 to 10, and the range of misidentification proportions to 0 to 0.5. (Parameters can be fixed to certain values with --constants. In that case, -1 indicates that a parameter is free to vary.) Because we need to run optimization several times to find a converged result with maximum likelihood, we use --optimizations to specify how many times the optimization will run. dadi-cli can use multiprocessing to run optimizations in parallel and by default the max number of CPUs available will be utilized. If users want fewer CPUs to be used, they can use the --cpus option to pass in the number of CPUs they want utilized for multiprocessing. If GPUs are available, they can be used by passing the --gpus option with the number of GPUs to be used.

dadi-cli InferDM --fs ./examples/results/fs/1KG.YRI.CEU.20.synonymous.snps.unfold.fs --model split_mig --lbounds 1e-3 1e-3 0 0 0 --ubounds 100 100 1 10 0.5  --output ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params --optimizations 10

After the optimization, a file ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params.InferDM.opts.0 will be made. Any subsequent optimzations using the same output argument will be number .1, .2, etc.

Users can use BestFit to obtain the best fit parameters across all optimization runs with a matching prefix.

dadi-cli BestFit --input-prefix ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params.InferDM --lbounds 1e-3 1e-3 0 0 0 --ubounds 100 100 1 10 0.5

The result is in a file ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params.InferDM.bestfits. This file contains the 100 highest likelihood parameter sets found (if at least that many optimizations have been carried out). If optimization converged, then the file also contains the converged results.

The results look like:

# /Users/tjstruck/anaconda3/envs/dadicli/bin/dadi-cli BestFit --input-prefix ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params.InferDM --ubounds 10 10 1 10 1 --lbounds 10e-3 10e-3 10e-3 10e-3 10e-5
# /Users/tjstruck/anaconda3/envs/dadicli/bin/dadi-cli InferDM --fs ./examples/results/fs/1KG.YRI.CEU.20.synonymous.snps.unfold.fs --model split_mig --p0 1 1 .5 1 .5 --ubounds 10 10 1 10 1 --lbounds 10e-3 10e-3 10e-3 10e-3 10e-5 --grids 60 80 100 --output ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params --optimizations 10 --maxeval 200
#
# Converged results
# Log(likelihood)	nu1	nu2	T	m	misid	theta
-1358.656798384051	2.1981613475071864	0.5158391424566413	0.28524739475343264	1.2375756394365451	0.020683288489282133	6771.157432134759
-1358.6880947964542	2.196984200820028	0.5133914664638932	0.28277200202566527	1.2413354976672208	0.020632166672609045	6786.9015918739915
-1358.6985706673254	2.193829701862104	0.5150503581819097	0.28755609071963395	1.2477300697512446	0.020689738141788896	6765.701504724712
-1358.7339671606906	2.215035226792488	0.5170682238957518	0.2855564758596486	1.2327369856712844	0.02071860093359053	6759.1696821323685
#
# Top 100 results
# Log(likelihood)	nu1	nu2	T	m	misid	theta
-1358.656798384051	2.1981613475071864	0.5158391424566413	0.28524739475343264	1.2375756394365451	0.020683288489282133	6771.157432134759
-1358.6880947964542	2.196984200820028	0.5133914664638932	0.28277200202566527	1.2413354976672208	0.020632166672609045	6786.9015918739915
-1358.6985706673254	2.193829701862104	0.5150503581819097	0.28755609071963395	1.2477300697512446	0.020689738141788896	6765.701504724712
-1358.7339671606906	2.215035226792488	0.5170682238957518	0.2855564758596486	1.2327369856712844	0.02071860093359053	6759.1696821323685
-1358.9273502384722	2.2161166322584114	0.5192238541188319	0.2805756463060831	1.211174886229439	0.020776317690376894	6774.695201641862
-1359.391018756372	2.2144196583672064	0.5133883753095932	0.2936398676168146	1.2635603043453991	0.021378007902017316	6734.565951708204
-1370.3128577096236	2.2965153967334837	0.5252579237019416	0.3301972600288801	1.2726527078754761	0.021832076042802986	6531.457050865405
-1437.4626671227088	2.348110308415506	0.6039083548424201	0.456754335717111	1.2524206835381557	0.030106689556776402	5971.036610931144
-1591.7611157189594	1.9723568097803748	0.5074933640133197	0.9819908721498116	1.6576780338511465	0.024964816092429044	5651.0110782323845
-2008.8420038152385	4.223980226535977	0.7590647581822983	0.2679298140727045	0.7488053157919355	0.01757046441838214	5977.616341188507A

Because there is randomness built into dadi-cli for where the starting parameters are for each optimization, it is possible the results could have not converged. Some things that can be done when using InferDM are increasing the max number of parameter sets each optimization will attempt with the --maxeval option. Users can also try to use a global optimization before moving onto the local optimization with the --global-optimization option. 25% of the number of optimizations the user passes in will be used for the global and the remaining will be used for the local optimization.

Using BestFit, users can adjust the criteria for convergence. By default optimizations are considered convergent if there are two other optimizations with a log-likelihood within 0.01% units of the optimization with the best log-likelihood. This criteria can be adjusted using the --delta-ll option and passing in the percentage difference in decimal form (so the default is 0.0001, rather than 0.01). Generally a higher --delta-ll can result in a false positive convergence, but this is dependent on the data being used (especially the sample size can effect the size of the log-likelihood). Optimizations in the bestfit file will be ordered by log-likelihood and should be examined closely for similarity of parameter values in convergent fits.

Finally, if you have experience with the data you are using, you can use the --check-convergence or --force-convergence option in InferDM. The --check-convergence option will run BestFit after each optimization to check for convergence and stop running optimizations once convergence is reached. The --force-convergence option will constantly add new optimization runs until convergence is reached. When using --check-convergence or --force-convergence you can pass in a value with --delta-ll as well to change the convergence criteria.

Sometimes parameters may be close to the boundaries. Users should be cautious and test increasing the boundaries to examine whether these boundaries would affect the results significantly. The best fit parameters are shown below mirroring the bestfits file. The first column is the log-likelihood, then the corresponding to these parameters, and the last column is the population-scaled mutation rate of the synonymous SNPs.

log-likelihood	nu1	nu2	T	m	misid	theta
-1358.66	2.2	0.52	0.29	1.24	0.021	6772

Generating caches for DFE inference

After inferring a best fit demographic model, users may also infer distributions of fitness effects (DFEs) from data. To perform DFE inference, users need to first generate of cache of frequency spectra. Because we use the split_mig model in the demographic inference, we need to use the same demographic model plus selection, the split_mig_sel model or the split_mig_sel_single_gamma model. The split_mig_sel model is used for inferring the DFE from two populations by assuming the population-scaled selection coefficients are different in the two populations, while the split_mig_sel_single_gamma model assumes the population-scaled selection coefficients are the same in the two populations.

Here, --model specifies the demographic model plus selection used in the inference. --demo-popt specifies the demographic parameters, which are stored in ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params.InferDM.bestfits. --sample-size defines the population size of each population. --cpus 2 indicates that the computation will use 2 CPUs. The output is pickled and can access through the pickle module in Python. By default GenerateCache will make the cache for the situation where the selection coefficients are the same in the two populations. If you want to to make the cache for the situation where the selection coefficients are independent from one another, use the --dimensionality 2 option. You can use the --gamma-bounds option to choose the range of the gamma distribution and the --gamma-pts option can be used to specify the number of selection coefficients that will be selected in that range to generate your cache. Note that the higher (more negative) you make the --gamma-bounds, the bigger the grid points you will want to use.

dadi-cli GenerateCache --model split_mig_sel_single_gamma --demo-popt ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params.InferDM.bestfits --sample-size 20 20 --grids 60 80 100 --gamma-pts 10 --gamma-bounds 1e-4 200 --output ./examples/results/caches/1KG.YRI.CEU.20.split_mig.sel.single.gamma.spectra.bpkl --cpus 4

dadi-cli GenerateCache --model split_mig_sel --dimensionality 2 --demo-popt ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params.InferDM.bestfits --sample-size 20 20 --grids 60 80 100 --gamma-pts 10 --gamma-bounds 1e-4 200 --output ./examples/results/caches/1KG.YRI.CEU.20.split_mig.sel.spectra.bpkl --cpus 4

User defined demographic models

Users can also import their own models into dadi-cli. In the examples/data folder, there is a file split_mig_fix_T_models.py which has a custom demographic model and demographic models with selection. The file imports various functions from dadi that are used to build demographic models.

from dadi import Numerics, Integration, PhiManip, Spectrum

Then defines the demographic model:

def split_mig_fix_T(params, ns, pts):
    """
    Instantaneous split into two populations of specified size, with symmetric migration and a fixed time point.
    """
    nu1,nu2,m = params

    xx = Numerics.default_grid(pts)

    phi = PhiManip.phi_1D(xx)
    phi = PhiManip.phi_1D_to_2D(xx, phi)

    phi = Integration.two_pops(phi, xx, 0.3, nu1, nu2, m12=m, m21=m)

    fs = Spectrum.from_phi(phi, ns, (xx,xx))
    return fs

dadi-cli checks models for the names and number of parameters, so after defining the demographic model we add an attribute for a list of the parameter names:

split_mig_fix_T.__param_names__ = ['nu1', 'nu2', 'm']

If you want to preform a DFE inference, you will need to add gamma parameters for gamma arguments when initializing $\phi$, ex:

dadi.PhiManip.phi_1D(xx, gamma=gamma_Pop1)

And for integration steps, ex:

dadi.Integration.two_pops(phi, xx, T, nu1, nu2, m12=m, m21=m, gamma1=gamma_Pop1, gamma2=gamma_Pop2)

When making your demographic models with selection and setting the inital $\phi$, take care to consider which population is is the ancestral population for the gamma argument in dadi.PhiManip.phi_1D.

Because custom model files can have multiple models in them, users will still want to use --model to pass in the model for demographic inference and cache generation. Here are some quick examples for users to run:

dadi-cli InferDM --model split_mig_fix_T --model-file examples/data/split_mig_fix_T_models --fs ./examples/results/fs/1KG.YRI.CEU.20.synonymous.snps.unfold.fs --p0 2 0.5 1.2 .02 --ubounds 3 1 2 0.03 --lbounds 1 1e-1 1e-1 1e-3 --grids 60 80 100 --output ./examples/results/demo/1KG.YRI.CEU.20.split_mig_fix_T.demo.params --optimizations 20 --maxeval 300 --check-convergence

dadi-cli GenerateCache --model split_mig_fix_T_one_s --model-file examples/data/split_mig_fix_T_models --demo-popt ./examples/results/demo/1KG.YRI.CEU.20.split_mig_fix_T.demo.params.InferDM.bestfits --sample-size 20 20 --grids 160 180 200 --gamma-pts 10 --gamma-bounds 1e-4 20 --output ./examples/results/caches/1KG.YRI.CEU.20.split_mig_one_s_psudo_new_model.spectra.bpkl --cpus 4

dadi-cli GenerateCache --model split_mig_fix_T_sel --model-file examples/data/split_mig_fix_T_models --dimensionality 2 --demo-popt ./examples/results/demo/1KG.YRI.CEU.20.split_mig_fix_T.demo.params.InferDM.bestfits --sample-size 20 20 --grids 160 180 200 --gamma-pts 10 --gamma-bounds 1e-4 20 --output ./examples/results/caches/1KG.YRI.CEU.20.split_mig_sel_psudo_new_model.spectra.bpkl --cpus 4

Inferring DFE

For inferring the DFE, we fit the spectrum from nonsynonymous SNPs. Although our data come from two populations, we will first infer a one-dimensional DFE, which assumes that selection coefficients are equal in the two populations. We define the marginal DFE as a lognormal distribution with --pdf1d. We use --ratio to specify the ratio of the nonsynonymous SNPs to the synonymous SNPs to calculate the population-scaled mutation rate of the nonsynonymous SNPs. Our parameters are log_mu the mean of the lognormal distribution, log_sigma the standard deviation of the lognormal distribution, and misid.

dadi-cli InferDFE --fs ./examples/results/fs/1KG.YRI.CEU.20.nonsynonymous.snps.unfold.fs --cache1d ./examples/results/caches/1KG.YRI.CEU.20.split_mig.sel.single.gamma.spectra.bpkl --pdf1d lognormal --p0 1 1 .5 --lbounds -10 0.01 0 --ubounds 10 10 0.5 --demo-popt ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params.InferDM.bestfits --ratio 2.31 --output ./examples/results/dfe/1KG.YRI.CEU.20.split_mig.dfe.1D_lognormal.params --optimizations 10 --maxeval 400 --check-convergence

The result is

# /home/u25/tjstruck/miniconda3/bin/dadi-cli InferDFE --fs ./examples/results/fs/1KG.YRI.CEU.20.nonsynonymous.snps.unfold.fs --cache1d ./examples/results/caches/1KG.YRI.CEU.20.split_mig.sel.single.gamma.spectra.bpkl --pdf1d lognormal --p0 1 1 .5 --lbounds 0 0.01 0 --ubounds 10 10 1 --demo-popt ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params.InferDM.bestfits --ratio 2.31 --output ./examples/results/dfe/1KG.YRI.CEU.20.split_mig.dfe.1D_lognormal.params --optimizations 10 --maxeval 400 --check-convergence
# /home/u25/tjstruck/miniconda3/bin/dadi-cli InferDFE --fs ./examples/results/fs/1KG.YRI.CEU.20.nonsynonymous.snps.unfold.fs --cache1d ./examples/results/caches/1KG.YRI.CEU.20.split_mig.sel.single.gamma.spectra.bpkl --pdf1d lognormal --p0 1 1 .5 --lbounds 0 0.01 0 --ubounds 10 10 1 --demo-popt ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params.InferDM.bestfits --ratio 2.31 --output ./examples/results/dfe/1KG.YRI.CEU.20.split_mig.dfe.1D_lognormal.params --optimizations 10 --maxeval 400 --check-convergence
#
# Converged results
# Log(likelihood)   log_mu  log_sigma   misid   theta
-1388.7168092990519 5.490553018509483   7.617358688984522   0.016580353311106688    15644.214296668904
-1388.7182268917072 5.48833179537645    7.612505954083041   0.016574649919878663    15644.214296668904
-1388.7511876804606 5.465373979901826   7.565453530395414   0.01664401774234844 15644.214296668904
#
# Top 100 results
# Log(likelihood)   log_mu  log_sigma   misid   theta
-1388.7168092990519 5.490553018509483   7.617358688984522   0.016580353311106688    15644.214296668904
-1388.7182268917072 5.48833179537645    7.612505954083041   0.016574649919878663    15644.214296668904
-1388.7511876804606 5.465373979901826   7.565453530395414   0.01664401774234844 15644.214296668904

Similar to the best fit parameters in ./examples/results/demo/1KG.YRI.CEU.split_mig.bestfit.demo.params, the first column is the log-likelihood followed by the parameters.

likelihood	mu	sigma	misidentification
-1389	5.5	7.6	0.017

Inferring a bivariate lognormal joint DFE

Here we will infer a joint DFE with selection potentially being different in the two populations. We define the DFE as a bivariate lognormal distribution with --pdf2d and pass in a cache that assumes the population-scaled selection coefficients are different in the two populations through --cache2d. The bivariate lognormal has an extra parameter rho, the correlation of the DFE between the populations. We can allow mu_log and sigma_log be different or the same in our populations. dadi-cli will run either the symmetric (shared mu_log and sigma_log) or asymmetric (independent mu_log and sigma_log) bivariate lognormal based on the number of parameters. For the symmetric bivariate lognormal the parameters are log_mu, log_sigma, and rho, the asymmetric bivariate lognormal the parameters are log_mu1, log_mu2, log_sigma1, log_sigma2, and rho, where 1 denotes the first population and 2 denotes the second population.

An example of running a symmetrical bivariate lognormal is:

dadi-cli InferDFE --fs ./examples/results/fs/1KG.YRI.CEU.20.nonsynonymous.snps.unfold.fs --cache2d ./examples/results/caches/1KG.YRI.CEU.20.split_mig.sel.spectra.bpkl --pdf2d biv_lognormal --p0 1 1 .5 .5 --lbounds -10 0.01 0.001 0 --ubounds 10 10 0.999 0.5 --demo-popt ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params.InferDM.bestfits --ratio 2.31 --output ./examples/results/dfe/1KG.YRI.CEU.20.split_mig.dfe.bivariate_sym_lognormal.params --optimizations 15 --maxeval 400 --check-convergence

An example of running an asymmetrical bivariate lognormal is:

dadi-cli InferDFE --fs ./examples/results/fs/1KG.YRI.CEU.20.nonsynonymous.snps.unfold.fs --cache2d ./examples/results/caches/1KG.YRI.CEU.20.split_mig.sel.spectra.bpkl --pdf2d biv_lognormal --p0 1 1 1 1 .5 .5 --lbounds -10 -10 0.01 0.01 0.001 0 --ubounds 10 10 10 10 0.999 0.5 --demo-popt ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params.InferDM.bestfits --ratio 2.31 --output ./examples/results/dfe/1KG.YRI.CEU.20.split_mig.dfe.bivariate_asym_lognormal.params --optimizations 10 --maxeval 400 --check-convergence

Inferring mixture model joint DFE

Here, we will use a mixture of a univariate lognormal and a bivariate lognormal distribution. To make the mixture we pass in options for both 1D and 2D: --pdf1d, --pdf2d, --cache1d, and --cache2d. Because the mixture model is assuming some proportion of the DFE is lognormal and the other is bivariate, the bivariate is symmeteric. The parameters for the mixture lognormal DFE are log_mu, log_sigma, rho,and w, the proportional weight of the bivariate lognormal DFE (1-w would be the weight of the univariate lognormal distribution). In this example we fix rho of the bivariate component to 0 with the --constants option.

dadi-cli InferDFE --fs ./examples/results/fs/1KG.YRI.CEU.20.nonsynonymous.snps.unfold.fs --cache1d ./examples/results/caches/1KG.YRI.CEU.20.split_mig.sel.single.gamma.spectra.bpkl --cache2d ./examples/results/caches/1KG.YRI.CEU.20.split_mig.sel.spectra.bpkl --pdf1d lognormal --pdf2d biv_lognormal --mix-pdf mixture_lognormal --p0 1 1 0 .5 .5 --lbounds -10 0.01 -1 0.001 0 --ubounds 10 10 -1 0.999 0.5 --constants -1 -1 0 -1 -1 --demo-popt ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params.InferDM.bestfits --ratio 2.31 --output ./examples/results/dfe/1KG.YRI.CEU.20.split_mig.dfe.lognormal_mixture.params --optimizations 1 --maxeval 400 --check-convergence

Similar to the best fit parameters in ./examples/results/demo/1KG.YRI.CEU.split_mig.bestfit.demo.params, the first column is the log-likelihood.

likelihood	mu	sigma	rho	w	misidentification
-1389	5.51	7.65	0	0	0.017

Performing statistical testing

To performing statistical testing with the Godambe Information Matrix (GIM), users should first use GenerateFs to generate bootstrapping data from VCF files. In this example we generate 20 bootstraps to save on time, but we recommend users do 100. --chunk-size is the max length of chunks the chromosomes will be broken up into and used to randomly draw from with replacement to make our bootstrapped chromosomes.

dadi-cli GenerateFs --vcf ./examples/data/1KG.YRI.CEU.biallelic.synonymous.snps.withanc.strict.vcf.gz --pop-info ./examples/data/1KG.YRI.CEU.popfile.txt --pop-ids YRI CEU --projections 20 20 --polarized --bootstrap 20 --chunk-size 1000000 --output ./examples/results/fs/bootstrapping_syn/1KG.YRI.CEU.20.synonymous.snps.unfold

dadi-cli GenerateFs --vcf ./examples/data/1KG.YRI.CEU.biallelic.nonsynonymous.snps.withanc.strict.vcf.gz --pop-info ./examples/data/1KG.YRI.CEU.popfile.txt --pop-ids YRI CEU --projections 20 20 --polarized --bootstrap 20 --chunk-size 1000000 --output ./examples/results/fs/bootstrapping_non/1KG.YRI.CEU.20.nonsynonymous.snps.unfold

To estimate the confidence intervals for the demographic parameters, users can use

dadi-cli StatDM --fs ./examples/results/fs/1KG.YRI.CEU.20.synonymous.snps.unfold.fs --model split_mig --demo-popt ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params.InferDM.bestfits --grids 60 80 100 --bootstrapping-dir ./examples/results/fs/bootstrapping_syn/ --output ./examples/results/stat/1KG.YRI.CEU.20.split_mig.bestfit.demo.params.godambe.ci

To estimate the confidence intervals for the joint DFE parameters, users can use

dadi-cli StatDFE --fs ./examples/results/fs/1KG.YRI.CEU.20.nonsynonymous.snps.unfold.fs --dfe-popt ./examples/results/dfe/1KG.YRI.CEU.20.split_mig.dfe.1D_lognormal.params.InferDFE.bestfits --cache1d ./examples/results/caches/1KG.YRI.CEU.20.split_mig.sel.single.gamma.spectra.bpkl --pdf1d lognormal --bootstrapping-nonsynonymous-dir ./examples/results/fs/bootstrapping_non/ --bootstrapping-synonymous-dir ./examples/results/fs/bootstrapping_non/ --output ./examples/results/stat/1KG.YRI.CEU.20.split_mig.bestfit.dfe.1D_lognormal.params.godambe.ci

Three different step sizes are tested when using the GIM. Ideally 95% confidence intervals will be consistent between step sizes.

Plotting

dadi-cli can plot allele frequency spectrum from data or compare the spectra between model and data.

To plot frequency spectrum from data, users can use

dadi-cli Plot --fs ./examples/results/fs/1KG.YRI.CEU.20.synonymous.snps.unfold.fs --output ./examples/results/plots/1KG.YRI.CEU.20.synonymous.snps.unfold.fs.pdf --model split_mig

dadi-cli Plot --fs ./examples/results/fs/1KG.YRI.CEU.20.nonsynonymous.snps.unfold.fs --output ./examples/results/plots/1KG.YRI.CEU.20.nonsynonymous.snps.unfold.fs.pdf --model split_mig

To compare two frequency spectra from data, users can use

dadi-cli Plot --fs ./examples/results/fs/1KG.YRI.CEU.20.synonymous.snps.unfold.fs --fs2 ./examples/results/fs/1KG.YRI.CEU.20.nonsynonymous.snps.unfold.fs --output ./examples/results/plots/1KG.YRI.CEU.20.synonymous.vs.nonsynonymous.snps.unfold.fs.pdf --model None

To compare frequency spectra between a demographic model without selection and data, users can use

dadi-cli Plot --fs ./examples/results/fs/1KG.YRI.CEU.20.synonymous.snps.unfold.fs --demo-popt ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.params.InferDM.bestfits --output ./examples/results/plots/1KG.YRI.CEU.20.synonymous.snps.vs.split_mig.pdf --model split_mig

To compare frequency spectra between a demographic model with selection and data, users can use

dadi-cli Plot --fs ./examples/results/fs/1KG.YRI.CEU.20.nonsynonymous.snps.unfold.fs --model split_mig --pdf1d lognormal --pdf2d biv_lognormal --dfe-popt ./examples/results/dfe/1KG.YRI.CEU.20.split_mig.dfe.lognormal_mixture.params.InferDFE.bestfits --cache1d ./examples/results/caches/1KG.YRI.CEU.20.split_mig.sel.single.gamma.spectra.bpkl --cache2d ./examples/results/caches/1KG.YRI.CEU.20.split_mig.sel.spectra.bpkl --output ./examples/results/plots/1KG.YRI.CEU.20.nonsynonymous.snps.vs.lognormal_mixture.pdf

By default, dadi-cli projects the sample size down to 20 for each population. Users can use --projections to lower the sample size for visualization purposes.

Using WorkQueue for distributed inference with dadi-cli

dadi-cli InferDM and InferDFE has built in options to work with Cooperative Computing Tools (CCTools)'s Work Queue for launching independent optimizations across multiple machines. This example will be for submitting jobs to a Slurm Workload Manager. First we want to submit a factory.

work_queue_factory -T local -M dm-inference -P ./tests/mypwfile --workers-per-cycle=0 --cores=1 &

dm-inference is the project name and mypwfile is a file containing a password, both of which are needed for dadi-cli use. Next you'll want to submit jobs from dadi-cli.

dadi-cli InferDM --fs ./examples/results/fs/1KG.YRI.CEU.20.synonymous.snps.unfold.fs --model split_mig --p0 1 1 .5 1 .5 --ubounds 10 10 1 10 1 --lbounds 10e-3 10e-3 10e-3 10e-3 10e-5 --grids 60 80 100 --output ./examples/results/demo/1KG.YRI.CEU.20.split_mig.demo.work_queue.params --optimizations 5 --maxeval 200 --check-convergence --work-queue dm-inference ./tests/mypwfile

dadi-cli will send the number of workers as the number of optimizations you request. The check-convergence and force-convergence options work with Work Queue as well.

Available demographic models

dadi-cli provides a subcommand Model to help users finding available demographic models in dadi. To find out available demographic models, users can use

dadi-cli Model --names

Then the available demographic models will be displayed in the screen:

Available 1D demographic models:
- bottlegrowth_1d
- growth
- snm_1d
- three_epoch
- two_epoch

Available 2D demographic models:
- bottlegrowth_2d
- bottlegrowth_split
- bottlegrowth_split_mig
- IM
- IM_pre
- split_mig
- split_asym_mig
- snm_2d

Available demographic models with selection:
- equil
- equil_X
- IM_sel
- IM_sel_single_gamma
- IM_pre_sel
- IM_pre_sel_single_gamma
- split_mig_sel
- split_mig_sel_single_gamma
- split_asym_mig_sel
- split_asym_mig_sel_single_gamma
- two_epoch_sel
- three_epoch_sel

To find out the parameters and detail of a specific model, users can use the name of the demograpic model as the parameter after --names. For example,

dadi-cli Model --names split_mig

Then the detail of the model will be displayed in the screen:

- split_mig:

        Split into two populations of specifed size, with symmetric migration.
        Two populations in this model.

        params = [nu1,nu2,T,m]

            nu1: Size of population 1 after split (in units of Na)
            nu2: Size of population 2 after split (in units of Na)
              T: Time in the past of split (in units of 2*Na generations)
              m: Migration rate between populations (2*Na*m)

Available DFE distributions

dadi-cli provides a subcommand Pdf to help users finding available probability density functions for DFE inference in dadi.

To find out available probability density functions, users can use

dadi-cli Pdf --names

Then the availalbe functions will be displayed in the screen:

Available probability density functions:
- beta
- biv_ind_gamma
- biv_lognormal
- exponential
- gamma
- lognormal
- normal
- mixture

To find out the parameters and the detail of a specific function, users can use the name of the function as the parameter after --names. For example,

dadi-cli Pdf --names lognormal

Then the detail of the function will be displayed in the screen:

- lognormal:

        Lognormal probability density function.

        params = [log_mu, log_sigma]

Dependencies

• dadi >2.1.0

References

1. Gutenkunst et al., PLoS Genet, 2009.
2. Huang et al., Mol Biol Evol, 2021.