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Here we will describe how to use Pheno-Ranker with the data from the recently published Phenopacket Corpus.

Data Download

We will download the data from the Monarch Initiative phenopacket-store GitHub repository BSD3 open-source license. If you don't have wget, dos2unix, and jq, please install them using:

sudo apt install wget dos2unix jq

We assume that you have R installed and are capable of installing its required modules.

wget https://github.com/monarch-initiative/phenopacket-store/releases/latest/download/all_phenopackets.zip
unzip all_phenopackets.zip 
dos2unix */*/*.json

As this is an exercise, instead of using all PXF files, we will randomly select 1000 and create a JSON array file named combined.json:

jq -s '.' $(ls -1 */*/*.json | shuf -n 1000) > combined.json
About reproducibility

We used 0.1.20. You can reproduce this example with the folowing list of files:

*/*/PMID_37183190_Family_4_individual_ind_8.json
*/*/PMID_33731876_fam265.json
*/*/PMID_19800048_III_9.json
*/*/PMID_36553465_P9.json
*/*/PMID_26915616_case_report.json
*/*/PMID_24355708_IHG25353_individual_2.json
*/*/PMID_36553465_P29.json
*/*/PMID_20887961_Patient_3.json
*/*/PMID_34722527_individual_1797–3128_5_Farzad_Jamshidi_Contributionof.json
*/*/PMID_10939567_EMD1_IV-13.json
*/*/PMID_37761890_50 .json
*/*/PMID_29513881_III_5.json
*/*/PMID_27067446_IV1.json
*/*/PMID_36446582_Parenti2021_P4.json
*/*/PMID_34722527_individual_individual_6_Arif_O_Khan_TheRPGRIP1-rel.json
*/*/STX_32521962_P10.json
*/*/PMID_38596211_individual_16.json
*/*/PMID_31527767_II_5.json
*/*/STX_P_34.json
*/*/PMID_32274456_Family_FGCO_individual_1111.json
*/*/PMID_28468610_8_year_old_boy.json
*/*/PMID_16965330_Sibling_of_patient_11.json
*/*/PMID_24369382_Family_1_II.4.json
*/*/PMID_35112411_35112411_P2.json
*/*/PMID_39013458_individual_KS110007.json
*/*/PMID_33731876_fam402.json
*/*/PMID_28940506_Family_14_Individual_18.json
*/*/PMID_37660254_Patient_4.json
*/*/PMID_28132690_Family_D_IV_1.json
*/*/PMID_29196670_Patient_1.json
*/*/PMID_31844624_31844624_P1.json
*/*/PMID_37951597_Family_28_Subject_2.json
*/*/PMID_36586412_21.json
*/*/PMID_37761890_19 .json
*/*/PMID_36446582_Ockeloen2015_P4.json
*/*/PMID_12789647_K13_II_2.json
*/*/PMID_38480682_F1_II_1.json
*/*/PMID_10874631_II.2.json
*/*/PMID_36446582_Gnazzo_2020_P19.json
*/*/STX_26865513_Patient_22.json
*/*/PMID_22541558_individual_P_1.json
*/*/PMID_24736735_G043_Father.json
*/*/PMID_12789647_K14_II_1.json
*/*/PMID_25835445_1-II:12*.json
*/*/PMID_39013458_individual_VUSM110017_A_father.json
*/*/PMID_37582359_F1-II-1.json
*/*/PMID_36965478_Individual_5.json
*/*/PMID_27087320_Subject_6.json
*/*/PMID_31021519_individual_SATB2_123.json
*/*/PMID_38428335_Patient.json
*/*/PMID_36446582_Parenti2021_P9.json
*/*/PMID_36446582_KBG38.json
*/*/PMID_36446582_Murray_2017_P9_5_1.json
*/*/PMID_37943620_F/28.json
*/*/PMID_34722527_individual_individual_1_S_Gerber_Completeexon-i.json
*/*/STX_26865513_Patient_13.json
*/*/PMID_31021519_individual_SATB2_142.json
*/*/PMID_28851325_Case_1.json
*/*/PMID_36446582_Kutkowska_Kazmierczak2021_P7.json
*/*/PMID_31021519_SATB2_02_from_Zarate_et_al__2018a_Scott_et_al.json
*/*/PMID_37054711_Family_2_Subject_3.json
*/*/PMID_33350388_Patient_KD_1_3.json
*/*/PMID_26286438_Patient_2.json
*/*/PMID_28652255_V_3.json
*/*/PMID_35420632_Patient_3_III-3.json
*/*/PMID_33731876_fam422.json
*/*/PMID_15781812_individual_123.json
*/*/PMID_15781812_individual_138.json
*/*/PMID_28285769_Individual_1_II_3.json
*/*/PMID_23335590_Subject_1330.json
*/*/PMID_18412284_Patient_III_8.json
*/*/PMID_37947183_Family_F11_individual_P15.json
*/*/PMID_33731876_fam252.json
*/*/PMID_26178382_UAB_R8423.json
*/*/PMID_27153400_Family_B_IV_4.json
*/*/PMID_25477417_F13_1.json
*/*/STX_26648591_Patient_2.json
*/*/PMID_36074124_2_IV_1.json
*/*/PMID_32853638_Patient_2.json
*/*/PMID_36182950_Patient_101.json
*/*/PMID_33731876_fam417.json
*/*/PMID_15911822_Patient_4.json
*/*/PMID_8704215_proband_18y.json
*/*/PMID_30945334_Individual_2.json
*/*/STX_26514728_Patient_15.json
*/*/PMID_38596211_individual_9.json
*/*/PMID_28927821_subject_I_2.json
*/*/PMID_34645992_Individual_3.json
*/*/PMID_32710489_Group_1_Patient_2.json
*/*/PMID_30580808_Ind-06-Fam-06.json
*/*/PMID_12384777_Family_4_4A.json
*/*/PMID_29883675_first_family_patient_1.json
*/*/PMID_24736735_G13684_Mother.json
*/*/PMID_33731876_fam114.json
*/*/PMID_25794864_P4.json
*/*/PMID_36446582_Low_2016_P17_10.json
*/*/PMID_33731876_fam234.json
*/*/PMID_27743463_S7.json
*/*/PMID_29290338_Family_ROT_R9008_individual_F.json
*/*/PMID_33731876_fam133.json
*/*/STX_EG0598P.json
*/*/PMID_34722527_individual_TB34R-R77_3_Tami_Current.json
*/*/PMID_33042910_Case_Presentation.json
*/*/PMID_34722527_individual_individual_3_Arif_O_Khan_TheRPGRIP1-rel.json
*/*/PMID_21358634_Family_6.json
*/*/STX_P_05.json
*/*/PMID_36446582_Kutkowska_Kazmierczak2021_P23.json
*/*/STX_G3_P31.json
*/*/PMID_19800048_III_10.json
*/*/PMID_35617047_Proband_2.json
*/*/PMID_21683322_29.json
*/*/PMID_36553465_P21.json
*/*/PMID_34038384_Family_G_II_1.json
*/*/PMID_22956686_18-IV-1.json
*/*/PMID_36528028_Individual_4.json
*/*/PMID_23211637_e_index_case.json
*/*/PMID_26437932_26437932_P1.json
*/*/PMID_37163662_Family_B_Patient_3.json
*/*/PMID_29707406_C_II_5.json
*/*/STX_32105008_case_report.json
*/*/PMID_30580808_Machol_Ind_6-Fam-36.json
*/*/PMID_24498630_ISR2.json
*/*/PMID_36996813_Individual_7.json
*/*/PMID_34722527_individual_35_5_Tobias_Eisenberger_Increasingthe.json
*/*/PMID_21367767_F34_III_2.json
*/*/STX_26514728_Patient_22.json
*/*/PMID_39013458_individual_KS110027.json
*/*/PMID_25477417_F1_1.json
*/*/PMID_36446582_Parenti2021_P16.json
*/*/PMID_21683322_22-a.json
*/*/PMID_14569098_F7_individual_1.json
*/*/PMID_20513137_individual_NF00573_GSM_GSM492688.json
*/*/PMID_37167966_F1-II:1.json
*/*/PMID_31089937_case_2_II_2.json
*/*/PMID_35344616_A21.json
*/*/PMID_36182950_Patient_97.json
*/*/PMID_8664912_Patient_0544_twin_1.json
*/*/PMID_27153400_Family_C_IV_13.json
*/*/PMID_26467218_individual_3_Denayer_et_al__22.json
*/*/PMID_34722527_individual_CR-F028_3_Leen_Abu-Safieh_Autozygome-guid.json
*/*/PMID_18256394_5.json
*/*/PMID_18423521_Patient_4.json
*/*/PMID_32154675_Family_1_Patient_2.json
*/*/STX_S011.json
*/*/PMID_39013458_individual_KS110064.json
*/*/PMID_37196654_Individual_60_(MSSNG_AU4427301).json
*/*/PMID_21594992_20_year_old_man.json
*/*/PMID_39069253_individual_21_Kruszka_et_al.json
*/*/PMID_14531790_F524_individual_II_4.json
*/*/PMID_36446582_Low_2016_P6_7.json
*/*/PMID_31721432_patient_4.json
*/*/PMID_31949313_F4_II_9.json
*/*/PMID_37167966_F4-II:1.json
*/*/PMID_26178382_UAB_R7444.json
*/*/PMID_20513137_individual_GUE_GSM_GSM492710.json
*/*/PMID_36446582_Novara_2017_P4.json
*/*/STX_P_01.json
*/*/STX_CHCO_01.json
*/*/PMID_19776401_Patient_4-2.json
*/*/PMID_33731876_fam263.json
*/*/PMID_29169895_patient_2_sister.json
*/*/PMID_37761890_53 .json
*/*/PMID_30679813_Individual_10.json
*/*/PMID_25477417_F14_1.json
*/*/PMID_29290338_Family_UG_R01_C2_individual_F.json
*/*/PMID_23913798_Patient_1.json
*/*/PMID_28051070_Brother_1_203.json
*/*/PMID_30247636_PMID_12930321_Patient_2_IV_8.json
*/*/STX_31130284_UPN_1176.json
*/*/PMID_20937753_individual.json
*/*/PMID_10939567_EMD2_II-3.json
*/*/PMID_22541558_individual_M_1.json
*/*/PMID_34521999_49.json
*/*/PMID_35638551_Patient_PM4.json
*/*/PMID_10580070_B_III-1.json
*/*/PMID_36446582_Miyatake_2017_P1.json
*/*/PMID_21487760_Patient_1.json
*/*/PMID_20513137_individual_LAF_GSM_GSM492712.json
*/*/PMID_29290338_Family_UAB_R3537__individual_RS.json
*/*/PMID_38441608_Patient_3.json
*/*/PMID_27807076_Patient_II_4.json
*/*/PMID_28782633_Family_1_8_year_old_daughter_P10.json
*/*/PMID_34722527_individual_individual_2_Jana_Zernant_GenotypingMicr.json
*/*/STX_31780880_Patient1214.json
*/*/PMID_35420632_patient21_IV-2.json
*/*/PMID_36446582_Goldenberg2016_P27.json
*/*/PMID_20519243_4.json
*/*/PMID_31239556_individual_11.json
*/*/PMID_18297069_Case_2.json
*/*/PMID_33731876_fam61.json
*/*/PMID_15983957_F3_individual_II_2.json
*/*/PMID_39069253_individual_16_Kruszka_et_al.json
*/*/PMID_35470444_P5.json
*/*/PMID_25868664_Family_25_Case_8.json
*/*/PMID_33776626_fetus.json
*/*/PMID_30580808_Ind-13-Fam-13.json
*/*/PMID_27764983_Family_2_brother_of_MZ.json
*/*/PMID_36965478_Individual_2.json
*/*/PMID_29290338_Family_UAB_R0008_individual_RS.json
*/*/PMID_30099644_III:7.json
*/*/PMID_36303223_individual_26_PMID_31870341.json
*/*/PMID_26426912_Patient_1.json
*/*/PMID_31569402_Family_11_Patient_I.json
*/*/PMID_17160901_family_Q_individual_1.json
*/*/PMID_26178382_UAB_R8216.json
*/*/PMID_36446582_KBG14.json
*/*/PMID_36446582_Parenti2016_P2.json
*/*/PMID_36446582_Goldenberg2016_P6.json
*/*/PMID_32710489_Group_1_Patient_11.json
*/*/PMID_33087723_E5.json
*/*/STX_26514728_Patient_9.json
*/*/PMID_26981933_Family_B_individual_B7.json
*/*/PMID_35977029_family_9.json
*/*/PMID_26178382_UAB_R3412.json
*/*/PMID_31021519_SATB2_07_from_Zarate_et_al__2018a.json
*/*/PMID_30879264_case_report.json
*/*/PMID_25835445_1-III:13*.json
*/*/PMID_21236492_Individual_P8.json
*/*/PMID_36189931_Individual_19.json
*/*/PMID_38325380_Patient_10.json
*/*/PMID_33731876_fam320.json
*/*/PMID_23972372_family_2_V_2.json
*/*/STX_CHCO_02.json
*/*/PMID_33209733_Case_presentation.json
*/*/PMID_35977029_family_6.json
*/*/PMID_10939567_Spo20.json
*/*/PMID_29290338_Family_UAB_R45401FN_201_individual_PrS.json
*/*/PMID_30580808_Ind-04-Fam-04.json
*/*/PMID_34035203_34035203_P1.json
*/*/PMID_28619360_probadn.json
*/*/PMID_25447906_Patient_1.json
*/*/PMID_37964426_S6_L2611del.json
*/*/PMID_34722527_individual_individual_2_Naeimeh_Tayebi_Targetednextg.json
*/*/PMID_25439728_IV.6.json
*/*/PMID_33731876_fam109.json
*/*/PMID_21217753_Fam_1_IV-9.json
*/*/PMID_37660254_Patient_2.json
*/*/PMID_10580070_B_IV-16.json
*/*/STX_Syrbe_14.json
*/*/PMID_10580070_A_III-14.json
*/*/PMID_36006710_Patient_3.json
*/*/PMID_33539324_C-II.4.json
*/*/PMID_29321044_Patient_1.json
*/*/STX_EG1184P.json
*/*/PMID_38230350_Proband_P2_family_A_II_2.json
*/*/PMID_36303223_individual_32_PMID_34025712.json
*/*/PMID_39069253_individual_2_Current.json
*/*/PMID_34038384_Family_F_II_4.json
*/*/PMID_37964426_Individual_R269W_2.json
*/*/PMID_16917909_667_3.json
*/*/PMID_22158539_individual_1.json
*/*/PMID_37303127_5.json
*/*/PMID_26739615_individual_5.json
*/*/PMID_33731876_fam31.json
*/*/PMID_30968594_individual_43.json
*/*/PMID_35979925_Case_8.json
*/*/PMID_33731876_fam147.json
*/*/PMID_31239556_individual_9.json
*/*/PMID_33731876_fam108.json
*/*/PMID_18256394_12.json
*/*/PMID_24736735_G15198_.json
*/*/PMID_22772371_TAA288_III_4.json
*/*/PMID_36446582_Kutkowska_Kazmierczak2021_P10.json
*/*/PMID_21487760_Patient_2.json
*/*/PMID_31944623_Patient_A.json
*/*/PMID_30968594_individual_15.json
*/*/PMID_10580070_A_III-5.json
*/*/PMID_23388215_Proband_1.json
*/*/PMID_30679813_Individual_2.json
*/*/PMID_18000979_OX2877-III:3.json
*/*/PMID_33731876_fam42.json
*/*/PMID_28757203_Individual_2_P2.json
*/*/PMID_33766796_17.json
*/*/PMID_33045405_Liu_et_al._(2019)_patient_1.json
*/*/PMID_21358634_Family_10.json
*/*/PMID_28137957_HSP84.II.3.json
*/*/PMID_34645992_Individual_6.json
*/*/PMID_36446582_Goldenberg2016_P2.json
*/*/PMID_25105228_Individual_2.json
*/*/PMID_23993194_Family_2_Case_3.json
*/*/PMID_11992252_Patient_1.json
*/*/PMID_31021519_individual_SATB2_106.json
*/*/PMID_21683322_26.json
*/*/PMID_27843126_A-III.1.json
*/*/PMID_30612693_8.json
*/*/PMID_15623688_Individual_IV_2.json
*/*/PMID_28065471_PI:2.json
*/*/PMID_26136118_patient.json
*/*/PMID_27374306_Case_2.json
*/*/PMID_36553465_P12.json
*/*/PMID_12818525_B_III_5.json
*/*/PMID_12203992_B9.json
*/*/PMID_25896430_Patient_5.json
*/*/PMID_10939567_Spo28.json
*/*/STX_P_02.json
*/*/PMID_25835445_1-II:14*.json
*/*/PMID_36586412_3.json
*/*/PMID_23714749_23714749_P2.json
*/*/PMID_27656288_patient.json
*/*/PMID_26706854_Subject_2.json
*/*/PMID_31239556_individual_27.json
*/*/PMID_39013458_individual_KS110091.json
*/*/PMID_37951597_Family_1_Subject_1.json
*/*/PMID_24736735_P15112_.json
*/*/PMID_30580808_Ind-03-Fam-03.json
*/*/PMID_33731876_fam122.json
*/*/PMID_25477417_F3_2.json
*/*/PMID_30945334_Individual_4.json
*/*/STX_31487502_Patient_55.json
*/*/PMID_26178382_UAB_R5455_II1.json
*/*/STX_G3_P5.json
*/*/PMID_36586412_23.json
*/*/PMID_37962958_44.json
*/*/PMID_33731876_fam62.json
*/*/PMID_37913506_II-3.json
*/*/PMID_36553465_P23.json
*/*/PMID_31332438_A:II:3.json
*/*/PMID_17160901_family_C_individual_3.json
*/*/PMID_22541558_individual_D_1.json
*/*/PMID_18551513_12.json
*/*/PMID_30679813_Individual_19.json
*/*/PMID_24357517_III_13_Family_B.json
*/*/PMID_37711075_Family_2_MR176_0.json
*/*/PMID_24721225_Patient_III-1.json
*/*/PMID_20618352_Patient_1.json
*/*/PMID_34112922_A_Japanese_girl.json
*/*/PMID_12789647_K2_II_1.json
*/*/PMID_29290338_Family_UAB_R2355__individual_F.json
*/*/PMID_31021519_individual_SATB2_114.json
*/*/PMID_38459354_Family_2_proband.json
*/*/PMID_32154675_Family_5_Patient_12.json
*/*/PMID_34038384_Family_A_II_2.json
*/*/PMID_10939567_EMD2_III-7.json
*/*/PMID_37071997_Individual_12.json
*/*/PMID_30580808_Machol_Ind_3-Fam-33.json
*/*/PMID_25439728_IV.3.json
*/*/PMID_37964426_Individual_Y567C.json
*/*/PMID_31021519_Case_8_AN_from_Bengani_et_al.json
*/*/PMID_33731876_fam272.json
*/*/STX_Fila_5_EG_CB.json
*/*/PMID_36446582_Novara_2017_P12.json
*/*/PMID_33500254_Case_2.json
*/*/PMID_18695062_18695062_P3.json
*/*/PMID_18930476_individual_III3.json
*/*/STX_30776697_Patient_70.json
*/*/PMID_34722527_individual_LCA-F009_2_Leen_Abu-Safieh_Autozygome-guid.json
*/*/PMID_29359243_29359243_P1.json
*/*/PMID_28781842_patient_2.json
*/*/PMID_26178382_UAB_R4844.json
*/*/STX_23020937_P4276.json
*/*/PMID_36747105_proband.json
*/*/PMID_37196654_Individual_55_(SSC_13446.p1).json
*/*/PMID_28132690_Family_D_III_1.json
*/*/PMID_38014483_Family_d_individual_d_4.json
*/*/PMID_28074573_GC1.json
*/*/PMID_36586412_14.json
*/*/PMID_16965330_patient_5.json
*/*/PMID_33731876_fam29.json
*/*/PMID_26008899_patient.json
*/*/PMID_17160901_family_K_individual_1.json
*/*/PMID_35052493_individual_G02.json
*/*/PMID_25533820_proband.json
*/*/PMID_36446582_Scarano_2013_P1.json
*/*/PMID_33731876_fam216.json
*/*/PMID_34521999_2.json
*/*/PMID_30046498_proband.json
*/*/STX_26514728_Patient_4.json
*/*/PMID_22034507_AIV_8.json
*/*/PMID_37196654_Individual_25.json
*/*/PMID_31021519_SATB2_34_from_Zarate_et_al__2018a_Scott_et_al.json
*/*/PMID_36047608_Patient_1_F1_II_4.json
*/*/PMID_22219643_II_13.json
*/*/PMID_30057029_Individual_15.json
*/*/PMID_34421895_P3.json
*/*/PMID_25896430_Patient_4.json
*/*/PMID_30968594_individual_46.json
*/*/PMID_12789647_K15_IV_1.json
*/*/PMID_39013458_individual_KS130015.json
*/*/PMID_35150594_C:II:1.json
*/*/STX_EG0542P.json
*/*/PMID_27743463_S4.json
*/*/PMID_32725632_patient_25.json
*/*/PMID_23714749_23714749_P1.json
*/*/PMID_33731876_fam198.json
*/*/STX_G3_P4.json
*/*/PMID_35923690_50–8.json
*/*/PMID_21683322_24.json
*/*/PMID_17661815_Patient_B_III_1.json
*/*/PMID_33855675_girl.json
*/*/PMID_26178382_UAB_R8414.json
*/*/PMID_36553465_P4.json
*/*/PMID_34722527_individual_133_12_Hui_Wang_ComprehensiveM.json
*/*/PMID_27374306_Case_1.json
*/*/PMID_30771478_Patient_1.json
*/*/PMID_36446582_Scarano_2013_P8.json
*/*/PMID_25868664_Family_15_Case_14.json
*/*/PMID_36586412_6.json
*/*/PMID_34722527_individual_QT491_3_Lin_Li_DetectionofVa.json
*/*/PMID_33731876_fam324.json
*/*/PMID_33731876_fam44.json
*/*/PMID_28331220_Patient_1.json
*/*/PMID_23714749_23714749_P13.json
*/*/PMID_28488678_V_5.json
*/*/PMID_14702087_Patient_4_of_PMID_1790747.json
*/*/PMID_14569098_F2_individual_7.json
*/*/PMID_33766796_3.json
*/*/PMID_36446582_Parenti2021_P8.json
*/*/PMID_34722527_individual_individual_1_Juliana_Maria_Ferraz_Sallum1_7_Clinicalandmo.json
*/*/PMID_38054405_Family_1_individual_P1.json
*/*/PMID_29290338_Family_UG_R391__individual_RS.json
*/*/PMID_33381903_nan.json
*/*/PMID_16917909_620_2.json
*/*/PMID_33277917_Patient_18.json
*/*/PMID_31021519_SATB2_04_from_Zarate_et_al__2018a_Scott_et_al.json
*/*/PMID_28488678_IV_3.json
*/*/PMID_12189163_V:6.json
*/*/PMID_36446582_KBG25.json
*/*/PMID_32376980_34.json
*/*/PMID_37196654_Individual_15.json
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Analytics

We will use the utility bff-pxf-plot to gather some general statistics.

From now on, we assume that you can find the pheno-ranker directory at ../. Please replace it with the correct path.

../pheno-ranker/utils/bff_pxf_plot/bff-pxf-plot -i combined.json
Display plot

Output

Cohort Mode

Let's start with a simple calculation.

../pheno-ranker/bin/pheno-ranker -r combined.json

Since a heatmap won't work for a 1000 x 1000 matrix, we'll perform multidimensional scaling using the matrix.txt results.

Rscript ../pheno-ranker/share/r/mds.R
See R code 
library(ggplot2)
library(ggrepel)

# Read in the input file as a matrix 
data <- as.matrix(read.table("matrix.txt", header = TRUE, row.names = 1, check.names = FALSE))

# Calculate distance matrix
#d <- dist(data)
#d <- 1 - data  # J-similarity to J-distance

# Perform multidimensional scaling
#fit <- cmdscale(d, eig=TRUE, k=2)
fit <- cmdscale(data, eig=TRUE, k=2)

# Extract (x, y) coordinates of multidimensional scaling
x <- fit$points[,1]
y <- fit$points[,2]

# Create data frame
df <- data.frame(x, y, label=row.names(data))

# Save image
png(filename = "mds.png", width = 1000, height = 1000,
    units = "px", pointsize = 12, bg = "white", res = NA)

# Create scatter plot
ggplot(df, aes(x, y, label = label)) +
  geom_point() +
  geom_text_repel(size = 5, # Adjust the size of the text
                  box.padding = 0.2, # Adjust the padding around the text
                  max.overlaps = 10) + # Change the maximum number of overlaps
  labs(title = "Multidimensional Scaling Results",
       x = "Hamming Distance MDS Coordinate 1",
       y = "Hamming Distance MDS Coordinate 2") + # Add title and axis labels
  theme(
        plot.title = element_text(size = 30, face = "bold", hjust = 0.5),
        axis.title = element_text(size = 25),
        axis.text = element_text(size = 15)) 

#dev.off()
Display plot

Output

From now on, we will focus on the phenotypicFeatures terms, as ideally, we would like to use them to classify patients.

../pheno-ranker/bin/pheno-ranker -r combined.json -include-terms phenotypicFeatures
Rscript ../pheno-ranker/share/r/mds.R
Display plot

Output

Now, let's examine the distribution of terms across patients. There are many ways to do this, but here we will a Perlscript.

See Perl code 
#!/usr/bin/perl
use strict;
use warnings;
use JSON::XS;

# Read from STDIN only
if (-t STDIN) {  # Check if STDIN is empty (no piped input)
    print STDERR "Usage: zcat input.json.gz | $0\n";
    print STDERR "       cat input.json | $0\n";
    exit 1;
}

# Read the entire JSON content from STDIN
my $json_text = do {
    local $/;
    <STDIN>;
};

# Decode JSON
my $json = JSON::XS->new->utf8->decode($json_text);

# Ensure the decoded JSON is an array reference
die "Input JSON is not an array.\n" unless ref($json) eq 'ARRAY';

# Print CSV header
print "key,count\n";

# Iterate through each object in the array
foreach my $obj (@$json) {
    # Ensure the current element is an object with an 'id' field
    next unless ref($obj) eq 'HASH' && exists $obj->{id};

    my $id = $obj->{id};
    my $count = 0;

    # Check if 'phenotypicFeatures' exists and is an array
    if (exists $obj->{phenotypicFeatures} && ref($obj->{phenotypicFeatures}) eq 'ARRAY') {
        $count = scalar @{ $obj->{phenotypicFeatures} };
    }

    # Print the CSV line
    print "\"$id\",$count\n";
}

cat combined.json | ./count_phenotypicFeatures.pl > counts.csv

Now, we will use R to plot a histogram.

See R code 
# Load required packages
library(ggplot2)

# Read the CSV file into a data frame
df <- read.csv("counts.csv")

# Calculate the average and median counts
average_count <- mean(df$count)
median_count <- median(df$count)

# Generate the histogram and add vertical lines for the average and median
plot <- ggplot(df, aes(x = count)) +
  geom_histogram(binwidth = 1, fill = "steelblue", color = "black") +
  geom_vline(aes(xintercept = average_count), color = "black", linetype = "dashed", linewidth = 1) +
  geom_vline(aes(xintercept = median_count), color = "red", linetype = "dashed", linewidth = 1) +
  labs(title = "Distribution of Element Counts per Object",
       x = "Number of Elements",
       y = "Frequency") +
  theme_minimal() +
  annotate("text", x = average_count + 0.5, y = Inf, label = paste("Mean:", round(average_count, 2)), vjust = 2) +
  annotate("text", x = median_count - 0.5, y = Inf, label = paste("Median:", round(median_count, 2)), vjust = 2, color = "red")

# Save the plot as a PNG file
ggsave("histogram_with_mean_median.png", plot = plot, width = 8, height = 6, dpi = 300)

# Print confirmation
cat("Histogram saved as histogram_with_mean_median.png\n")
Display plot

Output

Colored by Disease

We will use Pheno-Ranker output to fetch the diseases so that we can color the MDS plot. We will use the -e option, which exports intermediate files. Let's start by running a job:

../pheno-ranker/bin/pheno-ranker -r combined.json -include-terms diseases -e
mv export.ref_hash.json diseases_info.json

Now we have the disease information stored in diseases_info.json.

We will use R to plot by disease:

See R code 
# Load required packages
library(ggplot2)

# Read the CSV file into a data frame
df <- read.csv("counts.csv")

# Calculate the average and median counts
average_count <- mean(df$count)
median_count <- median(df$count)

# Generate the histogram and add vertical lines for the average and median
plot <- ggplot(df, aes(x = count)) +
  geom_histogram(binwidth = 1, fill = "steelblue", color = "black") +
  geom_vline(aes(xintercept = average_count), color = "black", linetype = "dashed", linewidth = 1) +
  geom_vline(aes(xintercept = median_count), color = "red", linetype = "dashed", linewidth = 1) +
  labs(title = "Distribution of Element Counts per Object",
       x = "Number of Elements",
       y = "Frequency") +
  theme_minimal() +
  annotate("text", x = average_count + 0.5, y = Inf, label = paste("Mean:", round(average_count, 2)), vjust = 2) +
  annotate("text", x = median_count - 0.5, y = Inf, label = paste("Median:", round(median_count, 2)), vjust = 2, color = "red")

# Save the plot as a PNG file
ggsave("histogram_with_mean_median.png", plot = plot, width = 8, height = 6, dpi = 300)

# Print confirmation
cat("Histogram saved as histogram_with_mean_median.png\n")
Display plot

Output

Graph

We will create a graph but we will be using only data from 50 patients to make it faster.

jq -c '.[]' combined.json | shuf -n 50 | jq -s '.' > combined_small.json
../pheno-ranker/bin/pheno-ranker -r combined_small.json -include-terms phenotypicFeatures --cytoscape-json corpus_cytoscape.json
Display plot

Converting data to QR-codes

Pheno-Ranker allows you to convert your data into QR-codes. Let's try an example.

  1. First we export intermediate files. This time we will include phenotypicFeatures only:
../pheno-ranker/bin/pheno-ranker -r combined.json -include-terms phenotypicFeatures -e
  1. We use barcode utilities to create the codes as PNG images:
../pheno-ranker/utils/barcode/pheno-ranker2barcode -i export.ref_binary_hash.json

This will create 1,000 PNG images inside the directory qr_codes .

See QR codes for the first 10 samples

QR
QR codes for 10 samples

  1. To decode the PNG and create a CSV:
../pheno-ranker/utils/barcode/barcode2pheno-ranker -t export.glob_hash.json -i qr_codes/* -o combined.qr.json --generate-csv

Patient Mode

Now, we will choose patient PMID_35344616_A2 to search for similar patients. We already know from the figures above that this patient is related to at least three other patients.

First, we will perform a dry run to obtain the JSON for that individual.

../pheno-ranker/bin/pheno-ranker -r combined.json -poi PMID_35344616_A2

This will create the file PMID_35344616_A2.json.

Now, we run it in patient mode.

../pheno-ranker/bin/pheno-ranker -r combined.json -t PMID_35344616_A2.json -include-terms phenotypicFeatures -max-out 5
See Results
RANK REFERENCE(ID) TARGET(ID) FORMAT LENGTH WEIGHTED HAMMING-DISTANCE DISTANCE-Z-SCORE DISTANCE-P-VALUE DISTANCE-Z-SCORE(RAND) JACCARD-INDEX JACCARD-Z-SCORE JACCARD-P-VALUE REFERENCE-VARS TARGET-VARS INTERSECT INTERSECT-RATE(%) COMPLETENESS(%)
1 PMID_35344616_A2 PMID_35344616_A2 PXF 31 False 0 -7.235 0.0000000 -5.5678 1.000 19.220 0.0000000 31 31 31 100.00 100.00
2 PMID_35344616_A5 PMID_35344616_A2 PXF 39 False 16 -4.143 0.0000172 -1.1209 0.590 11.200 0.0000000 31 31 23 74.19 74.19
3 PMID_35344616_A21 PMID_35344616_A2 PXF 38 False 18 -3.756 0.0000862 -0.3244 0.526 9.960 0.0000000 27 31 20 64.52 74.07
4 PMID_35344616_A17 PMID_35344616_A2 PXF 34 False 21 -3.177 0.0007451 1.3720 0.382 7.146 0.0000000 16 31 13 41.94 81.25
5 PMID_35344616_A19 PMID_35344616_A2 PXF 41 False 23 -2.790 0.0026344 0.7809 0.439 8.254 0.0000000 28 31 18 58.06 64.29

There might be cases where you want to retain phenotypicFeatures set to "excluded": true (i.e., absent). For instance, when you want to match individuals with the same diseases but lacking some features. Use --retain-excluded-phenotypicFeatures for that.

Citation

Please if you find use any ot this information for your research please cite:

  1. Phenopacket Corpus.

  2. Pheno-Ranker publication.