.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/ASW/ASW_two_pulses.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_ASW_ASW_two_pulses.py: ASW inference - Two pulses model ================================ This example implements inference for the ASW population under a two pulses model of admixture, using the tracts package. Inference is performed using autosomal and X chromosome data, allowing for the specification of sex-biased admixture. To implement this example, we use the following driver file: .. code-block:: yaml samples: directory: ./TrioPhased/ individual_names: [ "NA19625","NA19700","NA19701","NA19703","NA19704","NA19707","NA19711","NA19712","NA19713","NA19818","NA19819", "NA19834","NA19835","NA19900","NA19901","NA19904","NA19908","NA19909","NA19913","NA19914","NA19916","NA19917", "NA19920","NA19921","NA19922","NA19923","NA19982","NA19984","NA20126","NA20127","NA20274","NA20276","NA20278", "NA20281","NA20282","NA20287","NA20289","NA20291","NA20294","NA20296","NA20298","NA20299","NA20314","NA20317", "NA20318","NA20320","NA20321","NA20332","NA20334","NA20339","NA20340","NA20342","NA20346","NA20348","NA20351", "NA20355","NA20356","NA20357","NA20359","NA20362","NA20412"] male_names : [ "NA19700","NA19703","NA19711","NA19818","NA19834","NA19900","NA19904","NA19908","NA19916","NA19920", "NA19922","NA19982","NA19984","NA20126","NA20278","NA20281","NA20291","NA20298","NA20318","NA20340", "NA20342","NA20346","NA20348","NA20351","NA20356","NA20362"] #see Readme_dataprocessing.md for how this was generated filename_format: "{name}_{label}_final.bed" labels: [A, B] #If this field is omitted, 'A' and 'B' will be used by default chromosomes: 1-22 #The chromosomes to use for analysis. Can be specified as a list or a range allosomes: [X] output_filename_format: "ASW_test_output_{label}" log_filename: 'ASW_two_pulses.log' output_directory: ./output_two_pulses/ verbose_log: 1 verbose_screen: 30 log_scale : True start_params: t1: 10 REUR: 0.1 t2: 5 REUR_sex_bias: 0.01 repetitions: 3 seed: 100 maximum_iterations: 1000 unknown_labels_for_smoothing: ["UNK", "centromere","miscall"] # segments with these labels will be smoother over, that is, will be filled with neighbouring ancestries up to their midpoints. exclude_tracts_below_cm: 2 npts : 50 #fix_parameters_from_ancestry_proportions: ['REUR2', 'RNAT', 'REUR2_sex_bias', 'RNAT_sex_bias'] output_directory: ./output_two_pulses/ ad_model_autosomes: M ad_model_allosomes: DC Complete results from this analysis are saved in the output directory specified in the driver file. Below, we display the optimal parameters estimated from this analysis, as well as the plots illustrating the inferred tract length distributions, compared to the observed histograms, for every source population and chromosome type (autosomes and X chromosome). Optimal parameters ------------------ .. csv-table:: Estimated optimal parameters :file: output_two_pulses/ASW_test_output_optimal_parameters.txt :header-rows: 1 :delim: tab Tract length histograms ----------------------- Autosomal admixture ^^^^^^^^^^^^^^^^^^^ .. image:: output_two_pulses/ASW_test_output_autosomes_all_populations.png :width: 700px :alt: African ancestry tract histogram X chromosome admixture in females ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. image:: output_two_pulses/ASW_test_output_female_allosomes_all_populations.png :width: 700px :alt: European ancestry tract histogram X chromosome admixture in males ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. image:: output_two_pulses/ASW_test_output_male_allosomes_all_populations.png :width: 700px :alt: Native American ancestry tract histogram .. GENERATED FROM PYTHON SOURCE LINES 91-110 .. rst-class:: sphx-glr-script-out .. code-block:: none ------------------------------------------------------------------------------------------------ Running tracts 2.0 with driver file: ASW_two_pulses.yaml Reading data, demographic model and driver specifications... ------------------------------------------------------------------------------------------------ excluding_tracts_below set to 2.0 cM. Ancestries: ['EUR', 'NAT', 'AFR'] Data autosome proportions: [0.19578862 0.03825495 0.76595643] Data allosome proportions: [0.16839124 0.03818939 0.79341937] Model parameters : ['REUR', 'REUR_sex_bias', 'RNAT', 'RNAT_sex_bias', 't1', 'REUR2', 'REUR2_sex_bias', 't2'] Multiple starting parameters were generated. These will be converted to optimizer units and used for multiple optimization runs. Run | Starting parameters --------------------------------------------------- 1 | [0.1, 0.01, 0.1, 0.1, 13.89, 0.2, 0.1, 7.641] 2 | [0.1, 0.01, 0.1, 0.1, 12.52, 0.2, 0.1, 6.141] 3 | [0.1, 0.01, 0.1, 0.1, 13.99, 0.2, 0.1, 5.288] --------------------------------------------------- Optimization run #1 ----------------------------------------------------------------------------------------- Admixture is modelled with the M model for autosomes and with the DC model for allosomes. Optimization is performed in two steps. Step 1 : Optimizing autosomal likelihood over parameters ['REUR', 'RNAT', 't1', 'REUR2', 't2']. Iter. Log-likelihood Model parameters Transmission ----------------------------------------------------------------------------------------- 30 , -4538.44 , array([ 0.0953582 , 0 , 0.0944209 , 0 , 11.4738 , 0.196736 , 0 , 7.00516 ]), Autosomes 60 , -2341.91 , array([ 0.0904571 , 0 , 0.0871004 , 0 , 9.23133 , 0.188989 , 0 , 6.24366 ]), Autosomes 90 , -1227.09 , array([ 0.0878228 , 0 , 0.0793795 , 0 , 7.36465 , 0.182499 , 0 , 5.68516 ]), Autosomes 120 , -949.427 , array([ 0.0834851 , 0 , 0.0735855 , 0 , 6.61625 , 0.175779 , 0 , 5.61372 ]), Autosomes Step 1 completed. ---------------------------------------------------------------------------------------------------------------------------- Step 2 : Optimizing autosomal + allosomal likelihood over parameters : ['REUR_sex_bias', 'RNAT_sex_bias', 'REUR2_sex_bias']. Non-sex-bias parameters fixed at values from previous optimization step. Iter. Log-likelihood Model parameters Transmission ---------------------------------------------------------------------------------------------------------------------------- 150 , -209.172 , array([ 0.0836293 , 0.00889216 , 0.0735906 , 0.00502924 , 6.60329 , 0.175745 , -0.00921037 , 5.60263 ]), Female allosomes 150 , -101.918 , array([ 0.0836293 , 0.00889216 , 0.0735906 , 0.00502924 , 6.60329 , 0.175745 , -0.00921037 , 5.60263 ]), Male allosomes 150 , -948.464 , array([ 0.0836293 , 0.00889216 , 0.0735906 , 0.00502924 , 6.60329 , 0.175745 , -0.00921037 , 5.60263 ]), Autosomes 180 , -208.801 , array([ 0.0836293 , 0.122409 , 0.0735906 , -0.00045254 , 6.60329 , 0.175745 , -0.0819624 , 5.60263 ]), Female allosomes 180 , -101.896 , array([ 0.0836293 , 0.122409 , 0.0735906 , -0.00045254 , 6.60329 , 0.175745 , -0.0819624 , 5.60263 ]), Male allosomes 180 , -948.597 , array([ 0.0836293 , 0.122409 , 0.0735906 , -0.00045254 , 6.60329 , 0.175745 , -0.0819624 , 5.60263 ]), Autosomes 210 , -208.398 , array([ 0.0836293 , 0.264264 , 0.0735906 , -0.00947602 , 6.60329 , 0.175745 , -0.0879983 , 5.60263 ]), Female allosomes 210 , -102.085 , array([ 0.0836293 , 0.264264 , 0.0735906 , -0.00947602 , 6.60329 , 0.175745 , -0.0879983 , 5.60263 ]), Male allosomes 210 , -948.618 , array([ 0.0836293 , 0.264264 , 0.0735906 , -0.00947602 , 6.60329 , 0.175745 , -0.0879983 , 5.60263 ]), Autosomes 240 , -208.056 , array([ 0.0836293 , 0.393081 , 0.0735906 , -0.0242356 , 6.60329 , 0.175745 , -0.0965688 , 5.60263 ]), Female allosomes 240 , -102.233 , array([ 0.0836293 , 0.393081 , 0.0735906 , -0.0242356 , 6.60329 , 0.175745 , -0.0965688 , 5.60263 ]), Male allosomes 240 , -948.649 , array([ 0.0836293 , 0.393081 , 0.0735906 , -0.0242356 , 6.60329 , 0.175745 , -0.0965688 , 5.60263 ]), Autosomes 270 , -207.785 , array([ 0.0836293 , 0.508019 , 0.0735906 , -0.0461426 , 6.60329 , 0.175745 , -0.103234 , 5.60263 ]), Female allosomes 270 , -102.343 , array([ 0.0836293 , 0.508019 , 0.0735906 , -0.0461426 , 6.60329 , 0.175745 , -0.103234 , 5.60263 ]), Male allosomes 270 , -948.676 , array([ 0.0836293 , 0.508019 , 0.0735906 , -0.0461426 , 6.60329 , 0.175745 , -0.103234 , 5.60263 ]), Autosomes 300 , -207.572 , array([ 0.0836293 , 0.606187 , 0.0735906 , -0.0710081 , 6.60329 , 0.175745 , -0.111039 , 5.60263 ]), Female allosomes 300 , -102.415 , array([ 0.0836293 , 0.606187 , 0.0735906 , -0.0710081 , 6.60329 , 0.175745 , -0.111039 , 5.60263 ]), Male allosomes 300 , -948.709 , array([ 0.0836293 , 0.606187 , 0.0735906 , -0.0710081 , 6.60329 , 0.175745 , -0.111039 , 5.60263 ]), Autosomes 330 , -207.415 , array([ 0.0836293 , 0.68866 , 0.0735906 , -0.0971588 , 6.60329 , 0.175745 , -0.115879 , 5.60263 ]), Female allosomes 330 , -102.465 , array([ 0.0836293 , 0.68866 , 0.0735906 , -0.0971588 , 6.60329 , 0.175745 , -0.115879 , 5.60263 ]), Male allosomes 330 , -948.731 , array([ 0.0836293 , 0.68866 , 0.0735906 , -0.0971588 , 6.60329 , 0.175745 , -0.115879 , 5.60263 ]), Autosomes 360 , -207.286 , array([ 0.0836293 , 0.757871 , 0.0735906 , -0.119428 , 6.60329 , 0.175745 , -0.120097 , 5.60263 ]), Female allosomes 360 , -102.507 , array([ 0.0836293 , 0.757871 , 0.0735906 , -0.119428 , 6.60329 , 0.175745 , -0.120097 , 5.60263 ]), Male allosomes 360 , -948.751 , array([ 0.0836293 , 0.757871 , 0.0735906 , -0.119428 , 6.60329 , 0.175745 , -0.120097 , 5.60263 ]), Autosomes 390 , -207.192 , array([ 0.0836293 , 0.812752 , 0.0735906 , -0.137767 , 6.60329 , 0.175745 , -0.121712 , 5.60263 ]), Female allosomes 390 , -102.543 , array([ 0.0836293 , 0.812752 , 0.0735906 , -0.137767 , 6.60329 , 0.175745 , -0.121712 , 5.60263 ]), Male allosomes 390 , -948.759 , array([ 0.0836293 , 0.812752 , 0.0735906 , -0.137767 , 6.60329 , 0.175745 , -0.121712 , 5.60263 ]), Autosomes 420 , -207.103 , array([ 0.0836293 , 0.852845 , 0.0735906 , -0.146142 , 6.60329 , 0.175745 , -0.12523 , 5.60263 ]), Female allosomes 420 , -102.58 , array([ 0.0836293 , 0.852845 , 0.0735906 , -0.146142 , 6.60329 , 0.175745 , -0.12523 , 5.60263 ]), Male allosomes 420 , -948.777 , array([ 0.0836293 , 0.852845 , 0.0735906 , -0.146142 , 6.60329 , 0.175745 , -0.12523 , 5.60263 ]), Autosomes 450 , -207.062 , array([ 0.0836293 , 0.887528 , 0.0735906 , -0.164385 , 6.60329 , 0.175745 , -0.128078 , 5.60263 ]), Female allosomes 450 , -102.577 , array([ 0.0836293 , 0.887528 , 0.0735906 , -0.164385 , 6.60329 , 0.175745 , -0.128078 , 5.60263 ]), Male allosomes 450 , -948.791 , array([ 0.0836293 , 0.887528 , 0.0735906 , -0.164385 , 6.60329 , 0.175745 , -0.128078 , 5.60263 ]), Autosomes 480 , -207.013 , array([ 0.0836293 , 0.91252 , 0.0735906 , -0.168757 , 6.60329 , 0.175745 , -0.126095 , 5.60263 ]), Female allosomes 480 , -102.615 , array([ 0.0836293 , 0.91252 , 0.0735906 , -0.168757 , 6.60329 , 0.175745 , -0.126095 , 5.60263 ]), Male allosomes 480 , -948.781 , array([ 0.0836293 , 0.91252 , 0.0735906 , -0.168757 , 6.60329 , 0.175745 , -0.126095 , 5.60263 ]), Autosomes 510 , -206.977 , array([ 0.0836293 , 0.922216 , 0.0735906 , -0.166789 , 6.60329 , 0.175745 , -0.127968 , 5.60263 ]), Female allosomes 510 , -102.634 , array([ 0.0836293 , 0.922216 , 0.0735906 , -0.166789 , 6.60329 , 0.175745 , -0.127968 , 5.60263 ]), Male allosomes 510 , -948.791 , array([ 0.0836293 , 0.922216 , 0.0735906 , -0.166789 , 6.60329 , 0.175745 , -0.127968 , 5.60263 ]), Autosomes 540 , -206.969 , array([ 0.0836293 , 0.930955 , 0.0735906 , -0.172651 , 6.60329 , 0.175745 , -0.129474 , 5.60263 ]), Female allosomes 540 , -102.627 , array([ 0.0836293 , 0.930955 , 0.0735906 , -0.172651 , 6.60329 , 0.175745 , -0.129474 , 5.60263 ]), Male allosomes 540 , -948.798 , array([ 0.0836293 , 0.930955 , 0.0735906 , -0.172651 , 6.60329 , 0.175745 , -0.129474 , 5.60263 ]), Autosomes 570 , -206.951 , array([ 0.0836293 , 0.939968 , 0.0735906 , -0.174364 , 6.60329 , 0.175745 , -0.129472 , 5.60263 ]), Female allosomes 570 , -102.638 , array([ 0.0836293 , 0.939968 , 0.0735906 , -0.174364 , 6.60329 , 0.175745 , -0.129472 , 5.60263 ]), Male allosomes 570 , -948.798 , array([ 0.0836293 , 0.939968 , 0.0735906 , -0.174364 , 6.60329 , 0.175745 , -0.129472 , 5.60263 ]), Autosomes 600 , -206.932 , array([ 0.0836293 , 0.947698 , 0.0735906 , -0.175616 , 6.60329 , 0.175745 , -0.13033 , 5.60263 ]), Female allosomes 600 , -102.646 , array([ 0.0836293 , 0.947698 , 0.0735906 , -0.175616 , 6.60329 , 0.175745 , -0.13033 , 5.60263 ]), Male allosomes 600 , -948.803 , array([ 0.0836293 , 0.947698 , 0.0735906 , -0.175616 , 6.60329 , 0.175745 , -0.13033 , 5.60263 ]), Autosomes 630 , -206.92 , array([ 0.0836293 , 0.953898 , 0.0735906 , -0.175276 , 6.60329 , 0.175745 , -0.128182 , 5.60263 ]), Female allosomes 630 , -102.665 , array([ 0.0836293 , 0.953898 , 0.0735906 , -0.175276 , 6.60329 , 0.175745 , -0.128182 , 5.60263 ]), Male allosomes 630 , -948.792 , array([ 0.0836293 , 0.953898 , 0.0735906 , -0.175276 , 6.60329 , 0.175745 , -0.128182 , 5.60263 ]), Autosomes 660 , -206.915 , array([ 0.0836293 , 0.956591 , 0.0735906 , -0.177643 , 6.60329 , 0.175745 , -0.130624 , 5.60263 ]), Female allosomes 660 , -102.655 , array([ 0.0836293 , 0.956591 , 0.0735906 , -0.177643 , 6.60329 , 0.175745 , -0.130624 , 5.60263 ]), Male allosomes 660 , -948.804 , array([ 0.0836293 , 0.956591 , 0.0735906 , -0.177643 , 6.60329 , 0.175745 , -0.130624 , 5.60263 ]), Autosomes 690 , -206.908 , array([ 0.0836293 , 0.959566 , 0.0735906 , -0.178605 , 6.60329 , 0.175745 , -0.131397 , 5.60263 ]), Female allosomes 690 , -102.655 , array([ 0.0836293 , 0.959566 , 0.0735906 , -0.178605 , 6.60329 , 0.175745 , -0.131397 , 5.60263 ]), Male allosomes 690 , -948.808 , array([ 0.0836293 , 0.959566 , 0.0735906 , -0.178605 , 6.60329 , 0.175745 , -0.131397 , 5.60263 ]), Autosomes 720 , -206.907 , array([ 0.0836293 , 0.962371 , 0.0735906 , -0.18018 , 6.60329 , 0.175745 , -0.130752 , 5.60263 ]), Female allosomes 720 , -102.658 , array([ 0.0836293 , 0.962371 , 0.0735906 , -0.18018 , 6.60329 , 0.175745 , -0.130752 , 5.60263 ]), Male allosomes 720 , -948.805 , array([ 0.0836293 , 0.962371 , 0.0735906 , -0.18018 , 6.60329 , 0.175745 , -0.130752 , 5.60263 ]), Autosomes 750 , -206.9 , array([ 0.0836293 , 0.964736 , 0.0735906 , -0.180218 , 6.60329 , 0.175745 , -0.131491 , 5.60263 ]), Female allosomes 750 , -102.66 , array([ 0.0836293 , 0.964736 , 0.0735906 , -0.180218 , 6.60329 , 0.175745 , -0.131491 , 5.60263 ]), Male allosomes 750 , -948.809 , array([ 0.0836293 , 0.964736 , 0.0735906 , -0.180218 , 6.60329 , 0.175745 , -0.131491 , 5.60263 ]), Autosomes 780 , -206.893 , array([ 0.0836293 , 0.967162 , 0.0735906 , -0.180606 , 6.60329 , 0.175745 , -0.132309 , 5.60263 ]), Female allosomes 780 , -102.661 , array([ 0.0836293 , 0.967162 , 0.0735906 , -0.180606 , 6.60329 , 0.175745 , -0.132309 , 5.60263 ]), Male allosomes 780 , -948.813 , array([ 0.0836293 , 0.967162 , 0.0735906 , -0.180606 , 6.60329 , 0.175745 , -0.132309 , 5.60263 ]), Autosomes 810 , -206.886 , array([ 0.0836293 , 0.969232 , 0.0735906 , -0.179942 , 6.60329 , 0.175745 , -0.132065 , 5.60263 ]), Female allosomes 810 , -102.667 , array([ 0.0836293 , 0.969232 , 0.0735906 , -0.179942 , 6.60329 , 0.175745 , -0.132065 , 5.60263 ]), Male allosomes 810 , -948.812 , array([ 0.0836293 , 0.969232 , 0.0735906 , -0.179942 , 6.60329 , 0.175745 , -0.132065 , 5.60263 ]), Autosomes 840 , -206.886 , array([ 0.0836293 , 0.971135 , 0.0735906 , -0.181802 , 6.60329 , 0.175745 , -0.132432 , 5.60263 ]), Female allosomes 840 , -102.664 , array([ 0.0836293 , 0.971135 , 0.0735906 , -0.181802 , 6.60329 , 0.175745 , -0.132432 , 5.60263 ]), Male allosomes 840 , -948.814 , array([ 0.0836293 , 0.971135 , 0.0735906 , -0.181802 , 6.60329 , 0.175745 , -0.132432 , 5.60263 ]), Autosomes 870 , -206.877 , array([ 0.0836293 , 0.973018 , 0.0735906 , -0.180914 , 6.60329 , 0.175745 , -0.132599 , 5.60263 ]), Female allosomes 870 , -102.67 , array([ 0.0836293 , 0.973018 , 0.0735906 , -0.180914 , 6.60329 , 0.175745 , -0.132599 , 5.60263 ]), Male allosomes 870 , -948.815 , array([ 0.0836293 , 0.973018 , 0.0735906 , -0.180914 , 6.60329 , 0.175745 , -0.132599 , 5.60263 ]), Autosomes 900 , -206.886 , array([ 0.0836293 , 0.974598 , 0.0735906 , -0.183984 , 6.60329 , 0.175745 , -0.13134 , 5.60263 ]), Female allosomes 900 , -102.667 , array([ 0.0836293 , 0.974598 , 0.0735906 , -0.183984 , 6.60329 , 0.175745 , -0.13134 , 5.60263 ]), Male allosomes 900 , -948.808 , array([ 0.0836293 , 0.974598 , 0.0735906 , -0.183984 , 6.60329 , 0.175745 , -0.13134 , 5.60263 ]), Autosomes 930 , -206.877 , array([ 0.0836293 , 0.976011 , 0.0735906 , -0.182376 , 6.60329 , 0.175745 , -0.131481 , 5.60263 ]), Female allosomes 930 , -102.674 , array([ 0.0836293 , 0.976011 , 0.0735906 , -0.182376 , 6.60329 , 0.175745 , -0.131481 , 5.60263 ]), Male allosomes 930 , -948.809 , array([ 0.0836293 , 0.976011 , 0.0735906 , -0.182376 , 6.60329 , 0.175745 , -0.131481 , 5.60263 ]), Autosomes 960 , -206.878 , array([ 0.0836293 , 0.976948 , 0.0735906 , -0.184157 , 6.60329 , 0.175745 , -0.132346 , 5.60263 ]), Female allosomes 960 , -102.667 , array([ 0.0836293 , 0.976948 , 0.0735906 , -0.184157 , 6.60329 , 0.175745 , -0.132346 , 5.60263 ]), Male allosomes 960 , -948.813 , array([ 0.0836293 , 0.976948 , 0.0735906 , -0.184157 , 6.60329 , 0.175745 , -0.132346 , 5.60263 ]), Autosomes 990 , -206.873 , array([ 0.0836293 , 0.977772 , 0.0735906 , -0.183305 , 6.60329 , 0.175745 , -0.132297 , 5.60263 ]), Female allosomes 990 , -102.672 , array([ 0.0836293 , 0.977772 , 0.0735906 , -0.183305 , 6.60329 , 0.175745 , -0.132297 , 5.60263 ]), Male allosomes 990 , -948.813 , array([ 0.0836293 , 0.977772 , 0.0735906 , -0.183305 , 6.60329 , 0.175745 , -0.132297 , 5.60263 ]), Autosomes 1020 , -206.873 , array([ 0.0836293 , 0.978575 , 0.0735906 , -0.18389 , 6.60329 , 0.175745 , -0.132344 , 5.60263 ]), Female allosomes 1020 , -102.671 , array([ 0.0836293 , 0.978575 , 0.0735906 , -0.18389 , 6.60329 , 0.175745 , -0.132344 , 5.60263 ]), Male allosomes 1020 , -948.813 , array([ 0.0836293 , 0.978575 , 0.0735906 , -0.18389 , 6.60329 , 0.175745 , -0.132344 , 5.60263 ]), Autosomes 1050 , -206.871 , array([ 0.0836293 , 0.97925 , 0.0735906 , -0.183976 , 6.60329 , 0.175745 , -0.132513 , 5.60263 ]), Female allosomes 1050 , -102.672 , array([ 0.0836293 , 0.97925 , 0.0735906 , -0.183976 , 6.60329 , 0.175745 , -0.132513 , 5.60263 ]), Male allosomes 1050 , -948.814 , array([ 0.0836293 , 0.97925 , 0.0735906 , -0.183976 , 6.60329 , 0.175745 , -0.132513 , 5.60263 ]), Autosomes 1080 , -206.869 , array([ 0.0836293 , 0.979892 , 0.0735906 , -0.18401 , 6.60329 , 0.175745 , -0.132866 , 5.60263 ]), Female allosomes 1080 , -102.672 , array([ 0.0836293 , 0.979892 , 0.0735906 , -0.18401 , 6.60329 , 0.175745 , -0.132866 , 5.60263 ]), Male allosomes 1080 , -948.816 , array([ 0.0836293 , 0.979892 , 0.0735906 , -0.18401 , 6.60329 , 0.175745 , -0.132866 , 5.60263 ]), Autosomes 1110 , -206.865 , array([ 0.0836293 , 0.980437 , 0.0735906 , -0.183067 , 6.60329 , 0.175745 , -0.132607 , 5.60263 ]), Female allosomes 1110 , -102.677 , array([ 0.0836293 , 0.980437 , 0.0735906 , -0.183067 , 6.60329 , 0.175745 , -0.132607 , 5.60263 ]), Male allosomes 1110 , -948.815 , array([ 0.0836293 , 0.980437 , 0.0735906 , -0.183067 , 6.60329 , 0.175745 , -0.132607 , 5.60263 ]), Autosomes 1140 , -206.865 , array([ 0.0836293 , 0.980947 , 0.0735906 , -0.183683 , 6.60329 , 0.175745 , -0.132873 , 5.60263 ]), Female allosomes 1140 , -102.675 , array([ 0.0836293 , 0.980947 , 0.0735906 , -0.183683 , 6.60329 , 0.175745 , -0.132873 , 5.60263 ]), Male allosomes 1140 , -948.816 , array([ 0.0836293 , 0.980947 , 0.0735906 , -0.183683 , 6.60329 , 0.175745 , -0.132873 , 5.60263 ]), Autosomes Step 2 completed. ---------------------------------------------------------------------------------------------------------------------------- Optimization run #2 ----------------------------------------------------------------------------------------- Admixture is modelled with the M model for autosomes and with the DC model for allosomes. Optimization is performed in two steps. Step 1 : Optimizing autosomal likelihood over parameters ['REUR', 'RNAT', 't1', 'REUR2', 't2']. Iter. Log-likelihood Model parameters Transmission ----------------------------------------------------------------------------------------- 30 , -3076.31 , array([ 0.0948264 , 0 , 0.0930836 , 0 , 10.392 , 0.19423 , 0 , 5.70553 ]), Autosomes 60 , -1587.74 , array([ 0.089653 , 0 , 0.0855664 , 0 , 8.26067 , 0.188404 , 0 , 5.20647 ]), Autosomes 90 , -1024.42 , array([ 0.0857413 , 0 , 0.0741787 , 0 , 6.78731 , 0.17934 , 0 , 5.12973 ]), Autosomes 120 , -919.197 , array([ 0.0848331 , 0 , 0.0702176 , 0 , 6.36681 , 0.176561 , 0 , 5.36338 ]), Autosomes 150 , 4.15817e+27 , array([ 0.084797 , 0 , 0.0701599 , 0 , 6.36627 , 0.176477 , 0 , 5.36631 ]), OOB (oob=-4.1581650789268565e-05) Step 1 completed. ---------------------------------------------------------------------------------------------------------------------------- Step 2 : Optimizing autosomal + allosomal likelihood over parameters : ['REUR_sex_bias', 'RNAT_sex_bias', 'REUR2_sex_bias']. Non-sex-bias parameters fixed at values from previous optimization step. Iter. Log-likelihood Model parameters Transmission ---------------------------------------------------------------------------------------------------------------------------- Step 2 completed. ---------------------------------------------------------------------------------------------------------------------------- Optimization run #3 ----------------------------------------------------------------------------------------- Admixture is modelled with the M model for autosomes and with the DC model for allosomes. Optimization is performed in two steps. Step 1 : Optimizing autosomal likelihood over parameters ['REUR', 'RNAT', 't1', 'REUR2', 't2']. Iter. Log-likelihood Model parameters Transmission ----------------------------------------------------------------------------------------- 30 , -3694.51 , array([ 0.0943971 , 0 , 0.0919076 , 0 , 11.9332 , 0.197646 , 0 , 4.97037 ]), Autosomes 60 , -1895 , array([ 0.0898342 , 0 , 0.0830427 , 0 , 9.41982 , 0.193181 , 0 , 4.7267 ]), Autosomes 90 , -1207.18 , array([ 0.08665 , 0 , 0.0747253 , 0 , 7.80178 , 0.18735 , 0 , 4.51408 ]), Autosomes 120 , -973.891 , array([ 0.0856579 , 0 , 0.0655613 , 0 , 6.99577 , 0.176278 , 0 , 4.78787 ]), Autosomes 150 , -882.714 , array([ 0.0853844 , 0 , 0.0617342 , 0 , 6.79096 , 0.169927 , 0 , 5.13186 ]), Autosomes 180 , -783.752 , array([ 0.0849452 , 0 , 0.0573191 , 0 , 6.55847 , 0.164013 , 0 , 5.55773 ]), Autosomes 210 , -780.22 , array([ 0.0846849 , 0 , 0.0570419 , 0 , 6.57659 , 0.163945 , 0 , 5.57606 ]), Autosomes Step 1 completed. ---------------------------------------------------------------------------------------------------------------------------- Step 2 : Optimizing autosomal + allosomal likelihood over parameters : ['REUR_sex_bias', 'RNAT_sex_bias', 'REUR2_sex_bias']. Non-sex-bias parameters fixed at values from previous optimization step. Iter. Log-likelihood Model parameters Transmission ---------------------------------------------------------------------------------------------------------------------------- 240 , -214.303 , array([ 0.0846895 , 0.0707172 , 0.0570457 , 0.0705609 , 6.57657 , 0.163937 , -0.032293 , 5.57594 ]), Female allosomes 240 , -99.3913 , array([ 0.0846895 , 0.0707172 , 0.0570457 , 0.0705609 , 6.57657 , 0.163937 , -0.032293 , 5.57594 ]), Male allosomes 240 , -780.261 , array([ 0.0846895 , 0.0707172 , 0.0570457 , 0.0705609 , 6.57657 , 0.163937 , -0.032293 , 5.57594 ]), Autosomes 270 , -213.412 , array([ 0.0846895 , 0.175729 , 0.0570457 , 0.167023 , 6.57657 , 0.163937 , -0.0601379 , 5.57594 ]), Female allosomes 270 , -99.6837 , array([ 0.0846895 , 0.175729 , 0.0570457 , 0.167023 , 6.57657 , 0.163937 , -0.0601379 , 5.57594 ]), Male allosomes 270 , -780.285 , array([ 0.0846895 , 0.175729 , 0.0570457 , 0.167023 , 6.57657 , 0.163937 , -0.0601379 , 5.57594 ]), Autosomes 300 , -212.563 , array([ 0.0846895 , 0.28478 , 0.0570457 , 0.255654 , 6.57657 , 0.163937 , -0.0766907 , 5.57594 ]), Female allosomes 300 , -99.9996 , array([ 0.0846895 , 0.28478 , 0.0570457 , 0.255654 , 6.57657 , 0.163937 , -0.0766907 , 5.57594 ]), Male allosomes 300 , -780.307 , array([ 0.0846895 , 0.28478 , 0.0570457 , 0.255654 , 6.57657 , 0.163937 , -0.0766907 , 5.57594 ]), Autosomes 330 , -211.792 , array([ 0.0846895 , 0.386504 , 0.0570457 , 0.340195 , 6.57657 , 0.163937 , -0.0940155 , 5.57594 ]), Female allosomes 330 , -100.301 , array([ 0.0846895 , 0.386504 , 0.0570457 , 0.340195 , 6.57657 , 0.163937 , -0.0940155 , 5.57594 ]), Male allosomes 330 , -780.335 , array([ 0.0846895 , 0.386504 , 0.0570457 , 0.340195 , 6.57657 , 0.163937 , -0.0940155 , 5.57594 ]), Autosomes 360 , -211.112 , array([ 0.0846895 , 0.483655 , 0.0570457 , 0.412521 , 6.57657 , 0.163937 , -0.107498 , 5.57594 ]), Female allosomes 360 , -100.585 , array([ 0.0846895 , 0.483655 , 0.0570457 , 0.412521 , 6.57657 , 0.163937 , -0.107498 , 5.57594 ]), Male allosomes 360 , -780.36 , array([ 0.0846895 , 0.483655 , 0.0570457 , 0.412521 , 6.57657 , 0.163937 , -0.107498 , 5.57594 ]), Autosomes 390 , -210.528 , array([ 0.0846895 , 0.572495 , 0.0570457 , 0.473771 , 6.57657 , 0.163937 , -0.125475 , 5.57594 ]), Female allosomes 390 , -100.823 , array([ 0.0846895 , 0.572495 , 0.0570457 , 0.473771 , 6.57657 , 0.163937 , -0.125475 , 5.57594 ]), Male allosomes 390 , -780.4 , array([ 0.0846895 , 0.572495 , 0.0570457 , 0.473771 , 6.57657 , 0.163937 , -0.125475 , 5.57594 ]), Autosomes 420 , -210.006 , array([ 0.0846895 , 0.648387 , 0.0570457 , 0.535519 , 6.57657 , 0.163937 , -0.139369 , 5.57594 ]), Female allosomes 420 , -101.056 , array([ 0.0846895 , 0.648387 , 0.0570457 , 0.535519 , 6.57657 , 0.163937 , -0.139369 , 5.57594 ]), Male allosomes 420 , -780.435 , array([ 0.0846895 , 0.648387 , 0.0570457 , 0.535519 , 6.57657 , 0.163937 , -0.139369 , 5.57594 ]), Autosomes 450 , -209.573 , array([ 0.0846895 , 0.715157 , 0.0570457 , 0.585008 , 6.57657 , 0.163937 , -0.157388 , 5.57594 ]), Female allosomes 450 , -101.236 , array([ 0.0846895 , 0.715157 , 0.0570457 , 0.585008 , 6.57657 , 0.163937 , -0.157388 , 5.57594 ]), Male allosomes 450 , -780.486 , array([ 0.0846895 , 0.715157 , 0.0570457 , 0.585008 , 6.57657 , 0.163937 , -0.157388 , 5.57594 ]), Autosomes 480 , -209.205 , array([ 0.0846895 , 0.769041 , 0.0570457 , 0.633083 , 6.57657 , 0.163937 , -0.160142 , 5.57594 ]), Female allosomes 480 , -101.437 , array([ 0.0846895 , 0.769041 , 0.0570457 , 0.633083 , 6.57657 , 0.163937 , -0.160142 , 5.57594 ]), Male allosomes 480 , -780.494 , array([ 0.0846895 , 0.769041 , 0.0570457 , 0.633083 , 6.57657 , 0.163937 , -0.160142 , 5.57594 ]), Autosomes 510 , -208.889 , array([ 0.0846895 , 0.814919 , 0.0570457 , 0.676882 , 6.57657 , 0.163937 , -0.164882 , 5.57594 ]), Female allosomes 510 , -101.61 , array([ 0.0846895 , 0.814919 , 0.0570457 , 0.676882 , 6.57657 , 0.163937 , -0.164882 , 5.57594 ]), Male allosomes 510 , -780.509 , array([ 0.0846895 , 0.814919 , 0.0570457 , 0.676882 , 6.57657 , 0.163937 , -0.164882 , 5.57594 ]), Autosomes 540 , -208.623 , array([ 0.0846895 , 0.852881 , 0.0570457 , 0.714605 , 6.57657 , 0.163937 , -0.177911 , 5.57594 ]), Female allosomes 540 , -101.731 , array([ 0.0846895 , 0.852881 , 0.0570457 , 0.714605 , 6.57657 , 0.163937 , -0.177911 , 5.57594 ]), Male allosomes 540 , -780.552 , array([ 0.0846895 , 0.852881 , 0.0570457 , 0.714605 , 6.57657 , 0.163937 , -0.177911 , 5.57594 ]), Autosomes 570 , -208.405 , array([ 0.0846895 , 0.884278 , 0.0570457 , 0.747366 , 6.57657 , 0.163937 , -0.181784 , 5.57594 ]), Female allosomes 570 , -101.856 , array([ 0.0846895 , 0.884278 , 0.0570457 , 0.747366 , 6.57657 , 0.163937 , -0.181784 , 5.57594 ]), Male allosomes 570 , -780.565 , array([ 0.0846895 , 0.884278 , 0.0570457 , 0.747366 , 6.57657 , 0.163937 , -0.181784 , 5.57594 ]), Autosomes 600 , -208.216 , array([ 0.0846895 , 0.909245 , 0.0570457 , 0.778928 , 6.57657 , 0.163937 , -0.187074 , 5.57594 ]), Female allosomes 600 , -101.964 , array([ 0.0846895 , 0.909245 , 0.0570457 , 0.778928 , 6.57657 , 0.163937 , -0.187074 , 5.57594 ]), Male allosomes 600 , -780.584 , array([ 0.0846895 , 0.909245 , 0.0570457 , 0.778928 , 6.57657 , 0.163937 , -0.187074 , 5.57594 ]), Autosomes 630 , -208.067 , array([ 0.0846895 , 0.929162 , 0.0570457 , 0.804401 , 6.57657 , 0.163937 , -0.190229 , 5.57594 ]), Female allosomes 630 , -102.054 , array([ 0.0846895 , 0.929162 , 0.0570457 , 0.804401 , 6.57657 , 0.163937 , -0.190229 , 5.57594 ]), Male allosomes 630 , -780.595 , array([ 0.0846895 , 0.929162 , 0.0570457 , 0.804401 , 6.57657 , 0.163937 , -0.190229 , 5.57594 ]), Autosomes 660 , -207.939 , array([ 0.0846895 , 0.944604 , 0.0570457 , 0.829119 , 6.57657 , 0.163937 , -0.191877 , 5.57594 ]), Female allosomes 660 , -102.139 , array([ 0.0846895 , 0.944604 , 0.0570457 , 0.829119 , 6.57657 , 0.163937 , -0.191877 , 5.57594 ]), Male allosomes 660 , -780.601 , array([ 0.0846895 , 0.944604 , 0.0570457 , 0.829119 , 6.57657 , 0.163937 , -0.191877 , 5.57594 ]), Autosomes 690 , -207.837 , array([ 0.0846895 , 0.956634 , 0.0570457 , 0.849882 , 6.57657 , 0.163937 , -0.191326 , 5.57594 ]), Female allosomes 690 , -102.215 , array([ 0.0846895 , 0.956634 , 0.0570457 , 0.849882 , 6.57657 , 0.163937 , -0.191326 , 5.57594 ]), Male allosomes 690 , -780.599 , array([ 0.0846895 , 0.956634 , 0.0570457 , 0.849882 , 6.57657 , 0.163937 , -0.191326 , 5.57594 ]), Autosomes 720 , -207.765 , array([ 0.0846895 , 0.964487 , 0.0570457 , 0.863896 , 6.57657 , 0.163937 , -0.197405 , 5.57594 ]), Female allosomes 720 , -102.246 , array([ 0.0846895 , 0.964487 , 0.0570457 , 0.863896 , 6.57657 , 0.163937 , -0.197405 , 5.57594 ]), Male allosomes 720 , -780.622 , array([ 0.0846895 , 0.964487 , 0.0570457 , 0.863896 , 6.57657 , 0.163937 , -0.197405 , 5.57594 ]), Autosomes 750 , -207.692 , array([ 0.0846895 , 0.972429 , 0.0570457 , 0.879454 , 6.57657 , 0.163937 , -0.200294 , 5.57594 ]), Female allosomes 750 , -102.292 , array([ 0.0846895 , 0.972429 , 0.0570457 , 0.879454 , 6.57657 , 0.163937 , -0.200294 , 5.57594 ]), Male allosomes 750 , -780.633 , array([ 0.0846895 , 0.972429 , 0.0570457 , 0.879454 , 6.57657 , 0.163937 , -0.200294 , 5.57594 ]), Autosomes 780 , -207.636 , array([ 0.0846895 , 0.978218 , 0.0570457 , 0.89222 , 6.57657 , 0.163937 , -0.199919 , 5.57594 ]), Female allosomes 780 , -102.336 , array([ 0.0846895 , 0.978218 , 0.0570457 , 0.89222 , 6.57657 , 0.163937 , -0.199919 , 5.57594 ]), Male allosomes 780 , -780.632 , array([ 0.0846895 , 0.978218 , 0.0570457 , 0.89222 , 6.57657 , 0.163937 , -0.199919 , 5.57594 ]), Autosomes 810 , -207.59 , array([ 0.0846895 , 0.982325 , 0.0570457 , 0.90386 , 6.57657 , 0.163937 , -0.200642 , 5.57594 ]), Female allosomes 810 , -102.371 , array([ 0.0846895 , 0.982325 , 0.0570457 , 0.90386 , 6.57657 , 0.163937 , -0.200642 , 5.57594 ]), Male allosomes 810 , -780.634 , array([ 0.0846895 , 0.982325 , 0.0570457 , 0.90386 , 6.57657 , 0.163937 , -0.200642 , 5.57594 ]), Autosomes 840 , -207.572 , array([ 0.0846895 , 0.984269 , 0.0570457 , 0.908225 , 6.57657 , 0.163937 , -0.19995 , 5.57594 ]), Female allosomes 840 , -102.387 , array([ 0.0846895 , 0.984269 , 0.0570457 , 0.908225 , 6.57657 , 0.163937 , -0.19995 , 5.57594 ]), Male allosomes 840 , -780.632 , array([ 0.0846895 , 0.984269 , 0.0570457 , 0.908225 , 6.57657 , 0.163937 , -0.19995 , 5.57594 ]), Autosomes 870 , -207.553 , array([ 0.0846895 , 0.986088 , 0.0570457 , 0.911986 , 6.57657 , 0.163937 , -0.202329 , 5.57594 ]), Female allosomes 870 , -102.393 , array([ 0.0846895 , 0.986088 , 0.0570457 , 0.911986 , 6.57657 , 0.163937 , -0.202329 , 5.57594 ]), Male allosomes 870 , -780.641 , array([ 0.0846895 , 0.986088 , 0.0570457 , 0.911986 , 6.57657 , 0.163937 , -0.202329 , 5.57594 ]), Autosomes 900 , -207.536 , array([ 0.0846895 , 0.98758 , 0.0570457 , 0.916188 , 6.57657 , 0.163937 , -0.202501 , 5.57594 ]), Female allosomes 900 , -102.406 , array([ 0.0846895 , 0.98758 , 0.0570457 , 0.916188 , 6.57657 , 0.163937 , -0.202501 , 5.57594 ]), Male allosomes 900 , -780.642 , array([ 0.0846895 , 0.98758 , 0.0570457 , 0.916188 , 6.57657 , 0.163937 , -0.202501 , 5.57594 ]), Autosomes 930 , -207.596 , array([ 0.0846895 , 0.988194 , 0.0570457 , 0.918215 , 6.57657 , 0.163937 , -0.202376 , 5.57594 ]), Female allosomes 930 , -102.399 , array([ 0.0846895 , 0.988194 , 0.0570457 , 0.918215 , 6.57657 , 0.163937 , -0.202376 , 5.57594 ]), Male allosomes 930 , -780.641 , array([ 0.0846895 , 0.988194 , 0.0570457 , 0.918215 , 6.57657 , 0.163937 , -0.202376 , 5.57594 ]), Autosomes Step 2 completed. ---------------------------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------- Results from multiple optimization runs with different starting parameters: ------------------------------------- Run | LogLik | Found parameters ------------------------------------- 1 | -1258.36 | [0.08363, 0.981, 0.07359, -0.1838, 6.603, 0.1757, -0.1324, 5.603] 2 | -4.15817e+27 | [0.0848, 0, 0.07016, 0, 6.366, 0.1765, 0, 5.366] 3 | -1090.58 | [0.08469, 0.9882, 0.05705, 0.9182, 6.577, 0.1639, -0.2024, 5.576] ------------------------------------- Final parameters and corresponding likelihood: -------------------------------------------------------------------------------------------------------------------------- LogLik | REUR REUR_sex_bias RNAT RNAT_sex_bias t1 REUR2 REUR2_sex_bias t2 -------------------------------------------------------------------------------------------------------------------------- -1090.58 | 0.08469 0.9882 0.05705 0.9182 6.577 0.1639 -0.2024 5.576 -------------------------------------------------------------------------------------------------------------------------- Results saved to : ./output_two_pulses/ {'destination_dir': PosixPath('/home/runner/work/tracts/tracts/docs/source/auto_examples/ASW/output_two_pulses'), 'table_file': PosixPath('/home/runner/work/tracts/tracts/docs/source/auto_examples/ASW/output_two_pulses/ASW_test_output_optimal_parameters.txt')} | .. code-block:: Python import sys from pathlib import Path sys.path.append('.') from tracts.driver import run_tracts script_path = Path(sys.argv[0]).resolve() driver_filename = "ASW_two_pulses.yaml" run_tracts(driver_filename = driver_filename, script_dir = script_path) # Don't run the code below: for documentation purposes only. from tracts.doc_utils import prepare_example_outputs_for_docs prepare_example_outputs_for_docs("output_two_pulses") .. rst-class:: sphx-glr-timing **Total running time of the script:** (16 minutes 47.273 seconds) .. _sphx_glr_download_auto_examples_ASW_ASW_two_pulses.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: ASW_two_pulses.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: ASW_two_pulses.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: ASW_two_pulses.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_