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613
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614
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615
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616
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617
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618
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2015,BR2015
619
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2008,BR2008
620
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2040,BR2040
621
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2009,BR2009
622
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2034,BR2034
623
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2047,BR2047
624
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2048,BR2048
625
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2033,BR2033
626
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2019,BR2019
627
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2030,BR2030
628
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2037,BR2037
629
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2028,BR2028
630
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2025,BR2025
631
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2022,BR2022
632
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2038,BR2038
633
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2031,BR2031
634
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2050,BR2050
635
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2026,BR2026
636
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2046,BR2046
637
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2003,BR2003
638
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1010,BR1010
639
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1030,BR1030
640
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1041,BR1041
641
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1025,BR1025
642
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1035,BR1035
643
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1008,BR1008
644
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1014,BR1014
645
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1033,BR1033
646
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1048,BR1048
647
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2024,BR2024
648
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2051,BR2051
649
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2045,BR2045
650
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2041,BR2041
651
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2018,BR2018
652
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2043,BR2043
653
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2010,BR2010
654
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2005,BR2005
655
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2052,BR2052
656
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2002,BR2002
657
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2029,BR2029
658
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2014,BR2014
659
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2044,BR2044
660
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2004,BR2004
661
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2012,BR2012
662
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR2035,BR2035
663
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1032,BR1032
664
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1015,BR1015
665
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1043,BR1043
666
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1051,BR1051
667
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1007,BR1007
668
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1047,BR1047
669
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1044,BR1044
670
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1045,BR1045
671
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1049,BR1049
672
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1016,BR1016
673
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1054,BR1054
674
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1004,BR1004
675
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1040,BR1040
676
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1018,BR1018
677
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1059,BR1059
678
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1011,BR1011
679
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1042,BR1042
680
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1006,BR1006
681
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1021,BR1021
682
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1013,BR1013
683
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1058,BR1058
684
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1031,BR1031
685
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1026,BR1026
686
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1017,BR1017
687
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1009,BR1009
688
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1005,BR1005
689
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1056,BR1056
690
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1023,BR1023
691
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1024,BR1024
692
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1001,BR1001
693
+ 065_A_GSE271896_GSE271896_Sound_Life_deep_longitudinal_immune_profiling,BR1029,BR1029
694
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,AN5,AN5
695
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,AN3,AN3
696
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PP14,PP14
697
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,AN6,AN6
698
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,AN14,AN14
699
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PP13,PP13
700
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,AN13,AN13
701
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PP15,PP15
702
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PP6,PP6
703
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,AN7,AN7
704
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PP16,PP16
705
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PP9,PP9
706
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PC27,PC27
707
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PC18,PC18
708
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PC10,PC10
709
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PC19,PC19
710
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PC6,PC6
711
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PC5,PC5
712
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PC21,PC21
713
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP22,NP22
714
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP35,NP35
715
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP39,NP39
716
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP28,NP28
717
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP19,NP19
718
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP27,NP27
719
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,AP1,AP1
720
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP18,NP18
721
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP15,NP15
722
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP20,NP20
723
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP30,NP30
724
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP13,NP13
725
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PP2,PP2
726
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,AP6,AP6
727
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,AP12,AP12
728
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PP1,PP1
729
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,AP5,AP5
730
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,AP9,AP9
731
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,AP4,AP4
732
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP23,NP23
733
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP21,NP21
734
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP37,NP37
735
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP17,NP17
736
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP36,NP36
737
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP24,NP24
738
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP32,NP32
739
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PC11,PC11
740
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,NP31,NP31
741
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PP4,PP4
742
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,AP13,AP13
743
+ 093_C_Local_and_systemic_SARS-CoV-2_response_CELLxG_Local_and_systemic_SARS-CoV-2_response_CELLxGENE,PC9,PC9
744
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4482,SF4482
745
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4284,SF4284
746
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4137,SF4137
747
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4436,SF4436
748
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4423,SF4423
749
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4517,SF4517
750
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4313,SF4313
751
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4399,SF4399
752
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4254,SF4254
753
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4319,SF4319
754
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4492,SF4492
755
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4314,SF4314
756
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4027,SF4027
757
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4387,SF4387
758
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4384,SF4384
759
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4381,SF4381
760
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4468,SF4468
761
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4440,SF4440
762
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4620,SF4620
763
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4214,SF4214
764
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4413,SF4413
765
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4333,SF4333
766
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4227,SF4227
767
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4626,SF4626
768
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4216,SF4216
769
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4298,SF4298
770
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4406,SF4406
771
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4265,SF4265
772
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4394,SF4394
773
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4613,SF4613
774
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4382,SF4382
775
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4521,SF4521
776
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4061,SF4061
777
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4444,SF4444
778
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4452,SF4452
779
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4638,SF4638
780
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4335,SF4335
781
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4136,SF4136
782
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4544,SF4544
783
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4149,SF4149
784
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4445,SF4445
785
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4621,SF4621
786
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4081,SF4081
787
+ 200_A_GSE275067_Immunobiology_of_Aging_Cohort_PBMC_Profiling,SF4125,SF4125
788
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829
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830
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831
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832
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833
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834
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835
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836
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837
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838
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839
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840
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841
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842
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843
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844
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845
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846
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847
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848
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849
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850
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851
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852
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853
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854
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856
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867
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868
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869
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870
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871
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873
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874
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875
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876
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877
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878
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879
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880
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881
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882
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883
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884
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885
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886
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887
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888
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889
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890
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891
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892
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893
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894
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895
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896
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897
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901
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902
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903
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904
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905
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907
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908
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909
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911
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912
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913
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914
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915
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916
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919
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920
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921
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922
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923
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924
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925
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926
+ 301_A_GSE163121_SLE_healthy_Bcell_PBMC_scRNAseq,SLE2,SLE2
927
+ 302_A_GSE314010_SLE_IL36_stim_PBMC_scRNAseq,HD2,HD2
928
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929
+ 302_A_GSE314010_SLE_IL36_stim_PBMC_scRNAseq,HD7,HD7
930
+ 304_A_Parse_10M_PBMC_12donor_90cytokines,Donor10,Donor10
931
+ 304_A_Parse_10M_PBMC_12donor_90cytokines,Donor11,Donor11
932
+ 304_A_Parse_10M_PBMC_12donor_90cytokines,Donor1,Donor1
933
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934
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935
+ 304_A_Parse_10M_PBMC_12donor_90cytokines,Donor5,Donor5
936
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937
+ 304_A_Parse_10M_PBMC_12donor_90cytokines,Donor7,Donor7
938
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939
+ 304_A_Parse_10M_PBMC_12donor_90cytokines,Donor9,Donor9
paper-releases/pbisc-diffusion/updates/20261015T141849Z-rare-cell-7k-8k-panels/diffusion/dataset/server_1/model8/plans/rare_gene_context_expansion_20261015/coordination/ilc_training_support/expression_chunk_receipts.json ADDED
The diff for this file is too large to render. See raw diff
 
paper-releases/pbisc-diffusion/updates/20261015T141849Z-rare-cell-7k-8k-panels/diffusion/dataset/server_1/model8/plans/rare_gene_context_expansion_20261015/coordination/ilc_training_support/extraction_code_snapshot.py ADDED
@@ -0,0 +1,342 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """Bounded, CPU-only source-labelled TRAIN ILC marker support audit.
3
+
4
+ Reuses the chunk/row algorithm in corpus_macrophage_markers_20261015.
5
+ Selection is fixed by source L1 annotation and registered split, never expression.
6
+ """
7
+ from __future__ import annotations
8
+
9
+ import argparse
10
+ from collections import defaultdict
11
+ import hashlib
12
+ import json
13
+ from pathlib import Path
14
+ import resource
15
+ import time
16
+
17
+ import h5py
18
+ import numpy as np
19
+ import pandas as pd
20
+ import pyarrow.parquet as pq
21
+ import zarr
22
+
23
+ ROOT = Path(__file__).resolve().parent
24
+ PLAN = ROOT.parents[1]
25
+ M = PLAN.parents[1]
26
+ M7 = M.parent / 'model_7_no_annotation_sh_hwang/data'
27
+ E2 = M7 / 'e2_full_source'
28
+ BLOCK = Path('/home/sj_server_1/PBISC/PBISC-data/single-cell-data-block-blood')
29
+ KEY = ['dataset_id', 'donor_id', 'sample_id']
30
+ SOURCE_CT = Path('/home/sj_server_1/PBISC/paper-release-20260928/manuscript/source_data/corpus/corpus_celltypes.csv')
31
+ zarr.config.set({'async.concurrency': 2, 'threading.max_workers': 2})
32
+
33
+
34
+ def sha(path: Path) -> str:
35
+ h = hashlib.sha256()
36
+ with Path(path).open('rb') as f:
37
+ for b in iter(lambda: f.read(1 << 20), b''): h.update(b)
38
+ return h.hexdigest()
39
+
40
+
41
+ def receipt(path: Path) -> dict:
42
+ p = Path(path)
43
+ return {'path': str(p), 'bytes': p.stat().st_size, 'sha256': sha(p)}
44
+
45
+
46
+ def dump(name: str, value: dict | list) -> None:
47
+ (ROOT / name).write_text(json.dumps(value, indent=2) + '\n')
48
+
49
+
50
+ def targets() -> pd.DataFrame:
51
+ p = ROOT / 'locked_target_genes.csv'
52
+ t = pd.read_csv(p)
53
+ assert len(t) == 48 and not t.gene_id.duplicated().any()
54
+ return t
55
+
56
+
57
+ def prepare() -> None:
58
+ start = time.time()
59
+ target_source = PLAN / 'ilc_targeted_audit_genes.csv'
60
+ t = pd.read_csv(target_source)
61
+ t.to_csv(ROOT / 'locked_target_genes.csv', index=False)
62
+ t = targets()
63
+ cols = t.raw_panel_position.to_numpy(dtype=int)
64
+ var = pd.read_parquet(E2 / 'metadata/var.parquet')
65
+ assert len(var) == 20097
66
+ assert var.iloc[cols].gene_id.tolist() == t.gene_id.tolist()
67
+ assert var.iloc[cols].gene_symbol.tolist() == t.gene_symbol.tolist()
68
+ assert var.iloc[cols].panel_index.tolist() == cols.tolist()
69
+ cfgp = M / 'runs/f01_6k_1x_seed20260627/effective_config.json'
70
+ cfg = json.loads(cfgp.read_text())['data']
71
+ assert cfg['n_genes'] == 6000
72
+ manifest_path = Path(cfg['gene_selection_manifest'])
73
+ position_path = Path(cfg['gene_positions_path'])
74
+ assert sha(manifest_path) == cfg['gene_selection_manifest_sha256']
75
+ assert sha(position_path) == cfg['gene_positions_sha256']
76
+ manifest = json.loads(manifest_path.read_text())
77
+ selected_path = Path(manifest['selected_genes_path'])
78
+ assert sha(selected_path) == manifest['selected_genes_sha256']
79
+ selected = pd.read_csv(selected_path)
80
+ assert len(selected) == 6000 and not selected.gene_id.duplicated().any()
81
+ assert np.array_equal(np.load(position_path), selected.panel_position)
82
+ assert var.iloc[selected.panel_position].gene_id.tolist() == selected.gene_id.tolist()
83
+ assert np.array_equal(t.in_final6k, t.gene_id.isin(selected.gene_id))
84
+ records = [receipt(p) for p in [target_source, ROOT / 'locked_target_genes.csv',
85
+ E2 / 'metadata/var.parquet', SOURCE_CT, cfgp, manifest_path, position_path, selected_path]]
86
+ si_path = M7 / 'e2_full_index/metadata/sample_index.parquet'
87
+ si = pd.read_parquet(si_path)
88
+ for k in KEY: si[k] = si[k].astype(str)
89
+ assert not si.duplicated(KEY).any()
90
+ records.append(receipt(si_path))
91
+ ct = pd.read_csv(SOURCE_CT)
92
+ expected = ct[ct.celltype_l1 == 'ILC'].groupby('dataset_prefix').n_cells.sum().to_dict()
93
+ assert sum(expected.values()) == 19329
94
+ files = [p for p in sorted((E2 / 'metadata/obs_sc.parquet').glob('*.parquet'))
95
+ if any(p.stem.startswith(k + '_') for k in expected)
96
+ and not p.name.endswith('__strict_overlay.parquet')]
97
+ selected_rows, checks, all_ilc = [], [], defaultdict(int)
98
+ for fi, ep in enumerate(files):
99
+ bp = BLOCK / 'metadata/obs_sc.parquet' / ep.name
100
+ eq, bq = pq.ParquetFile(ep), pq.ParquetFile(bp)
101
+ assert eq.metadata.num_rows == bq.metadata.num_rows
102
+ bcols = ['source_row_index', 'row_index', 'celltype_l1']
103
+ extra = [x for x in ['source_celltype_l1', 'label_original', 'source_file'] if x in bq.schema.names]
104
+ bi = iter(bq.iter_batches(batch_size=32768, columns=bcols + extra, use_threads=False))
105
+ offset = n_ilc = n_train = 0
106
+ for eb in eq.iter_batches(batch_size=32768, columns=KEY + ['source_row_index', 'row_index'], use_threads=False):
107
+ e, b = eb.to_pandas(), next(bi).to_pandas()
108
+ assert len(e) == len(b) and np.array_equal(e.source_row_index, b.source_row_index)
109
+ assert np.array_equal(e.row_index, np.arange(offset, offset + len(e)))
110
+ keep = b.celltype_l1.astype(str).eq('ILC').to_numpy()
111
+ if keep.any():
112
+ v = e.loc[keep].copy()
113
+ for k in KEY: v[k] = v[k].astype(str)
114
+ v['shard_row'] = v.row_index
115
+ v['block_pre_qc_row_index'] = b.loc[keep, 'row_index'].to_numpy()
116
+ v['source_label'] = b.loc[keep, 'celltype_l1'].astype(str).to_numpy()
117
+ for c in extra: v[c] = b.loc[keep, c].astype(str).to_numpy()
118
+ v = v.merge(si[KEY + ['split', 'sample_key']], on=KEY, how='left', validate='many_to_one')
119
+ assert v.split.notna().all()
120
+ prefix = next(k for k in expected if ep.stem.startswith(k + '_'))
121
+ all_ilc[prefix] += len(v)
122
+ n_ilc += len(v)
123
+ v = v[v.split == 'train'].copy()
124
+ v['shard_id'] = ep.stem
125
+ n_train += len(v)
126
+ if len(v): selected_rows.append(v)
127
+ offset += len(e)
128
+ assert next(bi, None) is None and offset == eq.metadata.num_rows
129
+ checks.append({'shard_id': ep.stem, 'metadata_rows': offset, 'source_ilc': n_ilc,
130
+ 'train_ilc': n_train, 'source_identity_and_physical_order_match': True})
131
+ records.extend([receipt(ep), receipt(bp)])
132
+ if fi % 20 == 0 or fi + 1 == len(files): print(f'metadata {fi+1}/{len(files)}', flush=True)
133
+ assert dict(all_ilc) == expected, (dict(all_ilc), expected)
134
+ v = pd.concat(selected_rows, ignore_index=True)
135
+ assert len(v) == 16439 and not v.duplicated(['shard_id', 'shard_row']).any()
136
+ v.to_parquet(ROOT / 'selected_training_ilc.parquet', index=False)
137
+ rows_by_sid = {x['shard_id']: x['metadata_rows'] for x in checks}
138
+ plans = []
139
+ for sid, d in v.groupby('shard_id', sort=True):
140
+ zp = BLOCK / 'counts/sc_counts' / (sid + '.zarr')
141
+ g = zarr.open_group(str(zp), mode='r')
142
+ for p in sorted(zp.glob('**/zarr.json')): records.append(receipt(p))
143
+ assert g.attrs['shape'] == [rows_by_sid[sid], 20097] and g.attrs['shard_id'] == sid
144
+ mask = np.asarray(g['measurement_mask'][:])[cols].astype(bool)
145
+ ptr = np.asarray(g['indptr'][:], dtype=np.int64)
146
+ for a in ['indptr', 'measurement_mask']:
147
+ for p in sorted((zp / a / 'c').glob('*')):
148
+ if p.is_file(): records.append(receipt(p))
149
+ r = d.shard_row.to_numpy()
150
+ size = g['indices'].chunks[0]
151
+ assert size == g['data'].chunks[0] == 1000000
152
+ chunks = sorted(set(c for rr in r for c in range(int(ptr[rr]) // size, (int(ptr[rr+1]) - 1) // size + 1)))
153
+ paths = [zp / a / 'c' / str(c) for c in chunks for a in ['indices', 'data']]
154
+ plans.append({'shard_id': sid, 'cells': len(d), 'chunks': chunks,
155
+ 'compressed_bytes': sum(p.stat().st_size for p in paths if p.exists()),
156
+ 'decoded_bytes_upper_bound': len(chunks) * size * 12,
157
+ 'selected_nnz': int((ptr[r+1]-ptr[r]).sum()), 'measured': mask.tolist(),
158
+ 'source_count_matrix': g.attrs['source_count_matrix'], 'source_path': g.attrs['source_path']})
159
+ plan = {'status': 'prepared', 'source_ilc': sum(all_ilc.values()), 'train_ilc': len(v),
160
+ 'source_ilc_by_dataset_prefix': all_ilc, 'metadata_shards_scanned': len(files),
161
+ 'metadata_rows_scanned': sum(x['metadata_rows'] for x in checks),
162
+ 'selected_shards': len(plans), 'compressed_bytes': sum(x['compressed_bytes'] for x in plans),
163
+ 'decoded_bytes_upper_bound': sum(x['decoded_bytes_upper_bound'] for x in plans),
164
+ 'plans': plans, 'metadata_checks': checks, 'input_sources': records,
165
+ 'elapsed_seconds': time.time()-start}
166
+ dump('extraction_plan.json', plan)
167
+ print(json.dumps({k: plan[k] for k in ['source_ilc', 'train_ilc', 'selected_shards', 'compressed_bytes', 'decoded_bytes_upper_bound', 'elapsed_seconds']}), flush=True)
168
+
169
+
170
+ def extract() -> None:
171
+ start = time.time()
172
+ plan = json.loads((ROOT / 'extraction_plan.json').read_text())
173
+ # Coordinator reviewed the 12.40 GiB cost gate and authorized this exact local subset.
174
+ assert plan['compressed_bytes'] <= 13 * 2**30, 'Cost exceeds coordinator-reviewed 12.40 GiB subset'
175
+ t = targets(); cols = t.raw_panel_position.to_numpy(dtype=int)
176
+ v = pd.read_parquet(ROOT / 'selected_training_ilc.parquet')
177
+ out = np.zeros((len(v), len(cols)), dtype=np.int64)
178
+ measured = np.zeros_like(out, dtype=bool)
179
+ totals = np.zeros(len(v), dtype=np.int64)
180
+ chunks_read, shard_checks = [], []
181
+ dataset_timings = []; ds_started = time.time(); ds_bytes = 0; current_ds = None
182
+ for pi, pp in enumerate(plan['plans']):
183
+ sid = pp['shard_id']; d = v[v.shard_id == sid].sort_values('shard_row')
184
+ ds = str(d.dataset_id.iloc[0])
185
+ if current_ds is None: current_ds = ds
186
+ elif ds != current_ds:
187
+ timing = {'dataset_id': current_ds, 'seconds': time.time()-ds_started, 'compressed_bytes': ds_bytes,
188
+ 'process_peak_rss_kib': resource.getrusage(resource.RUSAGE_SELF).ru_maxrss}
189
+ dataset_timings.append(timing); print(json.dumps({'dataset_done': timing}), flush=True)
190
+ current_ds = ds; ds_started = time.time(); ds_bytes = 0
191
+ zp = BLOCK / 'counts/sc_counts' / (sid + '.zarr')
192
+ g = zarr.open_group(str(zp), mode='r')
193
+ mask = np.asarray(g['measurement_mask'][:])[cols].astype(bool)
194
+ assert mask.tolist() == pp['measured']
195
+ measured[d.index] = mask
196
+ ptr = np.asarray(g['indptr'][:], dtype=np.int64)
197
+ segs = defaultdict(list); size = 1000000
198
+ for oi, r in zip(d.index, d.shard_row):
199
+ a, b = int(ptr[r]), int(ptr[r+1])
200
+ while a < b:
201
+ c = a // size; end = min(b, (c+1)*size)
202
+ segs[c].append((int(oi), a-c*size, end-c*size)); a = end
203
+ assert sorted(segs) == pp['chunks']
204
+ nnz = 0
205
+ for c, pieces in sorted(segs.items()):
206
+ a, b = c*size, min((c+1)*size, g['indices'].shape[0])
207
+ ix = np.asarray(g['indices'][a:b], dtype=np.int32)
208
+ val = np.asarray(g['data'][a:b])
209
+ assert (val >= 0).all() and np.array_equal(val, val.astype(np.int64))
210
+ val = val.astype(np.int64)
211
+ for oi, lo, hi in pieces:
212
+ ids, values = ix[lo:hi], val[lo:hi]
213
+ totals[oi] += values.sum(); nnz += hi-lo
214
+ for j, col in enumerate(cols): out[oi, j] += values[ids == col].sum()
215
+ for arr in ['indices', 'data']:
216
+ p = zp / arr / 'c' / str(c)
217
+ if p.exists(): chunks_read.append(receipt(p))
218
+ assert nnz == pp['selected_nnz']
219
+ shard_checks.append({'shard_id': sid, 'cells': len(d), 'selected_nnz': nnz, 'chunks': len(segs)})
220
+ ds_bytes += pp['compressed_bytes']
221
+ print(f'expression {pi+1}/{len(plan["plans"])} {sid[:6]} cells={len(d)}', flush=True)
222
+ assert (totals > 0).all() and (out[~measured] == 0).all()
223
+ for j, gid in enumerate(t.gene_id):
224
+ v[gid] = out[:, j]
225
+ v[gid + '__measured'] = measured[:, j]
226
+ v['total_counts_20097'] = totals
227
+ v.to_parquet(ROOT / 'training_ilc_marker_counts.parquet', index=False)
228
+ dump('expression_chunk_receipts.json', chunks_read)
229
+ dataset_timings.append({'dataset_id': current_ds, 'seconds': time.time()-ds_started, 'compressed_bytes': ds_bytes,
230
+ 'process_peak_rss_kib': resource.getrusage(resource.RUSAGE_SELF).ru_maxrss})
231
+ dump('extraction_result.json', {'status': 'extracted', 'cells': len(v), 'genes': len(t),
232
+ 'elapsed_seconds': time.time()-start, 'shard_checks': shard_checks,
233
+ 'process_peak_rss_kib': resource.getrusage(resource.RUSAGE_SELF).ru_maxrss, 'dataset_timings': dataset_timings,
234
+ 'chunk_receipts': receipt(ROOT / 'expression_chunk_receipts.json')})
235
+ summarize(v, t)
236
+
237
+
238
+ def summarize(v: pd.DataFrame, t: pd.DataFrame) -> None:
239
+ tables = {'overall': [], 'by_dataset': [], 'by_donor': []}
240
+ grouped = [('overall', {}, v)]
241
+ grouped += [('by_dataset', {'dataset_id': k}, d) for k, d in v.groupby('dataset_id', sort=True)]
242
+ grouped += [('by_donor', dict(zip(KEY[:2], k)), d) for k, d in v.groupby(KEY[:2], sort=True)]
243
+ for level, labels, d in grouped:
244
+ for r in t.itertuples(index=False):
245
+ eligible = d[r.gene_id + '__measured']
246
+ det = eligible & d[r.gene_id].gt(0)
247
+ e, p = d[eligible], d[det]
248
+ n = len(e)
249
+ tables[level].append({**labels, 'gene_id': r.gene_id, 'gene_symbol': r.gene_symbol,
250
+ 'raw_panel_position': int(r.raw_panel_position), 'source_labelled_ilc_cells': len(d),
251
+ 'measured_eligible_cells': n, 'unmeasured_cells': len(d)-n, 'detected_cells': len(p),
252
+ 'detection_fraction_among_measured': len(p)/n if n else None,
253
+ 'total_raw_counts': int(e[r.gene_id].sum()),
254
+ 'mean_raw_counts_among_measured': float(e[r.gene_id].mean()) if n else None,
255
+ 'measured_dataset_scoped_donors': len(e[KEY[:2]].drop_duplicates()),
256
+ 'detected_dataset_scoped_donors': len(p[KEY[:2]].drop_duplicates()),
257
+ 'measured_samples': e.sample_key.nunique(), 'detected_samples': p.sample_key.nunique(),
258
+ 'in_final6k': bool(r.in_final6k), 'in_7k': bool(r.in_7k), 'in_8k': bool(r.in_8k),
259
+ 'sentinel': bool(r.sentinel)})
260
+ for name, rows in tables.items(): pd.DataFrame(rows).to_csv(ROOT / f'marker_support_{name}.csv', index=False)
261
+ depths = []
262
+ for name, d in [('ALL', v)] + list(v.groupby('dataset_id', sort=True)):
263
+ q = d.total_counts_20097.quantile([0, .1, .25, .5, .75, .9, 1]).to_dict()
264
+ depths.append({'dataset_id': name, 'cells': len(d), 'source_donor_ids': d.donor_id.nunique(),
265
+ 'samples': d.sample_key.nunique(), **{f'canonical_count_q{k}': x for k, x in q.items()}})
266
+ pd.DataFrame(depths).to_csv(ROOT / 'native_depth_support.csv', index=False)
267
+ v.groupby(['dataset_id', 'source_label', 'label_original'], dropna=False).size().rename('cells').reset_index().to_csv(ROOT / 'source_label_support.csv', index=False)
268
+
269
+
270
+ def verify() -> None:
271
+ t = targets(); cols = t.raw_panel_position.to_numpy(dtype=int); ids = t.gene_id.tolist()
272
+ var = pd.read_parquet(E2 / 'metadata/var.parquet')
273
+ v = pd.read_parquet(ROOT / 'training_ilc_marker_counts.parquet')
274
+ physical, direct, original = [], [], []
275
+ first_ds, boundary_ds = set(), set()
276
+ for sid, d in v.groupby('shard_id', sort=True):
277
+ zp = BLOCK / 'counts/sc_counts' / (sid + '.zarr')
278
+ g = zarr.open_group(str(zp), mode='r')
279
+ ep = E2 / 'metadata/obs_sc.parquet' / (sid + '.parquet')
280
+ bp = BLOCK / 'metadata/obs_sc.parquet' / (sid + '.parquet')
281
+ e = pq.read_table(ep, columns=['row_index', 'source_row_index']).to_pandas()
282
+ b = pq.read_table(bp, columns=['row_index', 'source_row_index', 'celltype_l1']).to_pandas()
283
+ rows = d.shard_row.to_numpy()
284
+ assert np.array_equal(e.iloc[rows].row_index, rows)
285
+ assert np.array_equal(e.iloc[rows].source_row_index, d.source_row_index)
286
+ assert np.array_equal(b.iloc[rows].source_row_index, d.source_row_index)
287
+ assert b.iloc[rows].celltype_l1.astype(str).eq('ILC').all()
288
+ assert g.attrs['shape'] == [len(e), len(var)]
289
+ physical.append({'shard_id': sid, 'cells': len(d), 'physical_identity_pass': True,
290
+ 'block_pre_qc_row_different': int((b.iloc[rows].row_index.to_numpy() != rows).sum())})
291
+ ds = str(d.dataset_id.iloc[0]); chosen = set()
292
+ if ds not in first_ds: chosen.update([d.index[0], d.index[-1]])
293
+ ptr = np.asarray(g['indptr'][:])
294
+ boundary = ptr[rows] // 1000000 != (ptr[rows+1]-1) // 1000000
295
+ if ds not in boundary_ds and boundary.any():
296
+ chosen.add(d.index[np.flatnonzero(boundary)[0]]); boundary_ds.add(ds)
297
+ for ix in sorted(chosen):
298
+ r = v.loc[ix]; a, stop = map(int, ptr[int(r.shard_row):int(r.shard_row)+2])
299
+ ri, rv = np.asarray(g['indices'][a:stop]), np.asarray(g['data'][a:stop])
300
+ observed = np.array([rv[ri == c].sum() for c in cols])
301
+ assert np.array_equal(observed, r[ids].to_numpy(dtype=np.int64))
302
+ assert int(rv.sum()) == int(r.total_counts_20097)
303
+ rec = {'dataset_id': ds, 'shard_id': sid, 'shard_row': int(r.shard_row),
304
+ 'counts': observed.tolist(), 'row_sum': int(rv.sum()),
305
+ 'crosses_chunk_boundary': a // 1000000 != (stop-1) // 1000000}
306
+ direct.append(rec)
307
+ if ds not in first_ds:
308
+ hp = Path('/home/sj_server_1/gblee') / g.attrs['source_path']
309
+ with h5py.File(hp, 'r') as h:
310
+ key = g.attrs['source_count_matrix']; vg = h['raw/var' if key.startswith('raw/') else 'var']
311
+ rawids = [x.decode() if isinstance(x, bytes) else str(x) for x in vg[vg.attrs['_index']][:]]
312
+ assert len(rawids) == len(set(rawids))
313
+ rawpos = {gid: j for j, gid in enumerate(rawids)}; mx = h[key]
314
+ assert mx.attrs['encoding-type'] == 'csr_matrix'
315
+ aa, zz = map(int, mx['indptr'][int(r.source_row_index):int(r.source_row_index)+2])
316
+ oi, ov = mx['indices'][aa:zz], mx['data'][aa:zz]
317
+ raw_counts = np.array([ov[oi == rawpos[gid]].sum() if gid in rawpos else 0 for gid in ids])
318
+ raw_mask = np.array([gid in rawpos for gid in ids])
319
+ assert np.array_equal(raw_counts, observed)
320
+ assert np.array_equal(raw_mask, r[[gid + '__measured' for gid in ids]].to_numpy(dtype=bool))
321
+ canonical = np.array([rawpos[gid] for gid in var.gene_id if gid in rawpos])
322
+ assert ov[np.isin(oi, canonical)].sum() == r.total_counts_20097
323
+ selected_bytes_hash = hashlib.sha256(oi.tobytes() + ov.tobytes()).hexdigest()
324
+ original.append({'dataset_id': ds, 'path': str(hp), 'bytes': hp.stat().st_size,
325
+ 'mtime_ns': hp.stat().st_mtime_ns, 'matrix': key, 'source_row_index': int(r.source_row_index),
326
+ 'shard_id': sid, 'shard_row': int(r.shard_row), '48_counts_match': True,
327
+ '48_measurement_mask_matches_original_gene_axis': True, 'canonical_count_sum_matches': True,
328
+ 'original_selected_row_indices_values_sha256': selected_bytes_hash,
329
+ 'original_gene_axis_sha256': hashlib.sha256('\n'.join(rawids).encode()).hexdigest()})
330
+ first_ds.add(ds)
331
+ assert len(original) == v.dataset_id.nunique() == 12
332
+ assert sum(x['cells'] for x in physical) == 16439
333
+ dump('verification.json', {'status': 'PASS', 'physical_rows': physical, 'direct_csr_rows': direct,
334
+ 'original_h5ad_rows': original, 'raw_positions': cols.tolist(), 'gene_ids': ids,
335
+ 'source_label_is_not_independently_verified_biological_identity': True})
336
+ print(json.dumps({'status': 'PASS', 'cells': len(v), 'shards': len(physical),
337
+ 'direct_csr_rows': len(direct), 'original_h5ad_rows': len(original)}), flush=True)
338
+
339
+
340
+ if __name__ == '__main__':
341
+ ap = argparse.ArgumentParser(); ap.add_argument('stage', choices=['prepare', 'extract', 'verify'])
342
+ globals()[ap.parse_args().stage]()
paper-releases/pbisc-diffusion/updates/20261015T141849Z-rare-cell-7k-8k-panels/diffusion/dataset/server_1/model8/plans/rare_gene_context_expansion_20261015/coordination/ilc_training_support/extraction_plan.json ADDED
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