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1 Data Summary


2 BridgeSettings

Configuration Settings for the Workflow

species tissue metadata genesets genetype min_umi_per_cell max_mt_percent min_genes_per_cell min_cell scr_th seu_nrmlz_method seu_scale_factor seu_n_hvg seu_n_dim seu_k_param seu_cluster_res harmony tsne spr_n_dim mrk_logfc mrk_min_pct mrk_only_pos mrk_test mrk_top_n adt trajectory traj_var_gene traj_top_n pipe_version
hs lung sample_based none none 750 20 250 3 0.25 LogNormalize 1e+06 2000 30 20 0.7 sample FALSE 30 0.25 0.5 TRUE wilcox 0 FALSE none 1000 50 1.0.0

3 BridgeQC

3.1 Sample-level Characteristics and Quality Control Metrics

3.1.1 Metadata

sample disease tissue sample_id
001C Control Lung S1
002C Control Lung S2
003C Control Lung S3
010I IPF Lung S4
021I IPF Lung S5
022I IPF Lung S6
025I IPF Lung S7
034C Control Lung S8
034I IPF Lung S9
040I IPF Lung S10
041I IPF Lung S11
051I IPF Lung S12
052CO COPD Lung S13
053I IPF Lung S14
056CO COPD Lung S15
063I IPF Lung S16
065C Control Lung S17
081C Control Lung S18
084C Control Lung S19
092C Control Lung S20
098C Control Lung S21
123I IPF Lung S22
133C Control Lung S23
135I IPF Lung S24
1372C Control Lung S25
137C Control Lung S26
137CO COPD Lung S27
138I IPF Lung S28
145I IPF Lung S29
152CO COPD Lung S30
153CO COPD Lung S31
157I IPF Lung S32
158I IPF Lung S33
160C Control Lung S34
166I IPF Lung S35
174I IPF Lung S36
177I IPF Lung S37
178CO COPD Lung S38
179I IPF Lung S39
184CO COPD Lung S40
186CO COPD Lung S41
192C Control Lung S42
192CO COPD Lung S43
193CO COPD Lung S44
194CO COPD Lung S45
207CO COPD Lung S46
208C Control Lung S47
209I IPF Lung S48
210I IPF Lung S49
212I IPF Lung S50
214I IPF Lung S51
217CO COPD Lung S52
218C Control Lung S53
221I IPF Lung S54
222C Control Lung S55
222I IPF Lung S56
225I IPF Lung S57
226C Control Lung S58
228I IPF Lung S59
235CO COPD Lung S60
237CO COPD Lung S61
238CO COPD Lung S62
23CO COPD Lung S63
244C Control Lung S64
253C Control Lung S65
296C Control Lung S66
29I IPF Lung S67
388C Control Lung S68
396C Control Lung S69
439C Control Lung S70
454C Control Lung S71
465C Control Lung S72
47I IPF Lung S73
483C Control Lung S74
484C Control Lung S75
49I IPF Lung S76
59I IPF Lung S77
8CO COPD Lung S78

3.1.2 Cells & Genes (RNA)

sample_id pre_qc_gene pre_qc_cell post_qc_gene post_qc_cell
S1 29,354 1,862 23,558 1,768
S2 27,688 700 21,742 643
S3 31,444 3,460 26,114 3,297
S4 33,738 4,867 28,627 4,696
S5 33,465 3,122 28,433 3,065
S6 33,798 4,843 28,577 4,724
S7 31,821 3,239 26,513 2,903
S8 27,129 706 21,039 670
S9 33,489 4,279 28,177 4,201
S10 34,451 4,949 29,216 4,753
S11 32,803 3,670 27,440 3,537
S12 34,820 6,839 29,671 6,655
S13 31,874 3,482 26,255 3,277
S14 33,540 3,718 28,091 3,633
S15 32,283 4,382 26,819 4,248
S16 30,810 3,292 25,177 3,076
S17 29,408 1,883 23,614 1,766
S18 26,641 982 20,086 844
S19 26,512 477 20,324 460
S20 29,393 4,319 23,673 3,719
S21 34,575 9,240 29,355 8,872
S22 31,449 1,851 25,811 1,804
S23 34,280 8,566 28,726 8,151
S24 35,814 6,368 30,139 6,162
S25 33,186 4,595 27,877 4,447
S26 29,008 999 23,169 966
S27 19,114 60 11,648 59
S28 35,024 3,665 29,644 3,582
S29 32,570 4,886 27,213 4,708
S30 33,694 4,923 27,698 4,738
S31 31,944 3,273 25,800 3,001
S32 35,620 7,063 30,600 6,874
S33 32,787 6,518 27,451 6,180
S34 31,563 4,541 26,068 4,216
S35 34,632 5,442 28,438 5,090
S36 33,360 5,876 27,951 5,652
S37 31,297 6,562 25,893 6,163
S38 32,146 8,879 26,734 8,313
S39 31,009 4,524 25,600 4,312
S40 34,421 6,514 28,623 6,256
S41 30,183 3,586 24,508 3,360
S42 33,156 4,028 27,672 3,856
S43 34,429 6,867 29,368 6,684
S44 33,148 6,802 27,991 6,570
S45 32,437 2,944 26,692 2,875
S46 31,689 3,165 26,240 3,089
S47 30,120 2,049 24,813 1,917
S48 33,496 4,592 28,260 4,451
S49 31,252 2,763 25,811 2,681
S50 35,452 4,496 29,806 4,368
S51 31,327 957 25,018 933
S52 33,140 2,573 26,869 2,512
S53 35,044 4,823 29,223 4,489
S54 34,779 5,330 28,847 5,097
S55 35,016 10,110 29,962 9,732
S56 35,642 8,641 30,670 8,383
S57 36,373 12,085 31,239 11,607
S58 34,525 10,736 28,697 9,939
S59 32,972 3,215 27,687 3,091
S60 31,642 4,176 26,016 4,038
S61 33,122 3,566 27,516 3,477
S62 30,877 1,961 25,453 1,921
S63 31,139 1,850 25,403 1,728
S64 23,437 166 17,010 166
S65 28,909 945 23,356 895
S66 32,664 3,303 27,225 3,184
S67 30,511 1,263 25,031 1,207
S68 29,417 2,287 23,499 2,075
S69 25,865 1,268 20,025 1,210
S70 33,836 7,197 28,560 6,948
S71 27,765 1,163 21,982 1,139
S72 31,870 2,622 26,648 2,559
S73 32,062 2,463 26,409 2,381
S74 30,179 2,166 24,470 1,876
S75 27,998 1,110 22,017 1,080
S76 33,849 4,624 28,590 4,530
S77 30,569 1,167 24,922 1,153
S78 27,654 453 21,757 444

3.1.3 UMI per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 999 999 2,260 3,960 6,670 28,583 28,583
S2 1,009 1,009 4,343 7,013 10,965 36,524 36,524
S3 1,001 1,001 3,035 5,906 11,330 48,220 48,220
S4 1,006 1,006 3,276 5,678 9,526 85,160 85,160
S5 1,001 1,001 2,930 5,083 9,528 117,292 117,292
S6 1,002 1,002 2,749 4,752 8,258 144,295 144,295
S7 1,016 1,016 3,658 6,461 10,401 95,551 95,551
S8 998 998 2,626 4,540 8,403 29,012 29,012
S9 1,002 1,002 2,700 4,995 9,258 85,545 85,545
S10 1,003 1,003 3,458 6,118 10,181 110,848 110,848
S11 999 999 3,836 6,841 11,259 65,753 65,753
S12 1,004 1,004 2,716 4,381 7,275 94,428 94,428
S13 998 998 2,419 3,723 5,245 36,415 36,415
S14 1,002 1,002 3,172 5,978 11,215 69,783 69,783
S15 1,001 1,001 2,045 3,125 5,235 29,610 29,610
S16 1,000 1,000 2,695 4,980 8,972 52,572 52,572
S17 1,001 1,001 2,109 3,592 5,638 34,779 34,779
S18 1,000 1,000 2,080 3,200 4,854 26,723 26,723
S19 999 999 2,892 5,198 8,992 21,290 21,290
S20 1,008 1,008 4,478 7,731 10,696 23,807 23,807
S21 999 999 2,035 4,008 9,015 55,865 55,865
S22 1,002 1,002 3,860 6,462 11,939 73,686 73,686
S23 1,000 1,000 3,278 6,060 9,692 63,791 63,791
S24 999 999 3,528 5,906 9,574 70,613 70,613
S25 1,001 1,001 2,678 4,933 8,828 68,838 68,838
S26 1,009 1,009 4,017 6,090 9,838 43,933 43,933
S27 860 860 2,086 3,681 6,453 29,439 29,439
S28 1,006 1,006 3,523 6,809 11,809 91,304 91,304
S29 1,000 1,000 2,702 4,784 9,261 70,500 70,500
S30 1,000 1,000 1,723 3,366 5,837 91,268 91,268
S31 999 999 2,153 3,495 5,187 27,794 27,794
S32 1,001 1,001 3,565 6,032 10,275 122,498 122,498
S33 1,001 1,001 2,429 4,680 8,867 44,610 44,610
S34 1,001 1,001 3,281 5,624 7,876 39,533 39,533
S35 999 999 2,153 3,632 6,469 61,348 61,348
S36 1,000 1,000 3,342 5,692 9,669 63,716 63,716
S37 1,000 1,000 1,596 3,455 7,782 44,445 44,445
S38 999 999 1,314 1,869 3,420 68,943 68,943
S39 1,005 1,005 2,857 5,494 9,209 43,864 43,864
S40 1,001 1,001 1,935 2,930 5,218 49,868 49,868
S41 999 999 2,651 4,898 8,061 42,361 42,361
S42 1,003 1,003 2,999 5,314 8,986 176,969 176,969
S43 1,001 1,001 2,774 4,866 12,662 76,773 76,773
S44 999 999 1,978 2,879 5,112 97,600 97,600
S45 1,008 1,008 2,428 4,597 9,152 45,880 45,880
S46 1,000 1,000 2,483 3,702 5,525 41,955 41,955
S47 1,009 1,009 2,575 4,022 6,493 66,332 66,332
S48 1,000 1,000 3,296 5,780 10,426 83,283 83,283
S49 997 997 3,018 5,571 9,150 46,239 46,239
S50 1,003 1,003 3,391 6,142 11,310 80,338 80,338
S51 1,000 1,000 2,881 4,908 7,532 33,429 33,429
S52 999 999 2,305 3,637 5,533 45,239 45,239
S53 1,001 1,001 2,213 3,684 6,216 88,327 88,327
S54 1,000 1,000 2,809 5,263 9,346 63,803 63,803
S55 1,000 1,000 2,483 4,098 8,010 59,527 59,527
S56 1,000 1,000 3,074 5,868 10,848 83,601 83,601
S57 1,000 1,000 3,524 6,325 10,344 96,840 96,840
S58 999 999 2,054 3,270 5,111 46,032 46,032
S59 1,003 1,003 2,300 4,090 7,594 97,063 97,063
S60 999 999 1,968 3,200 5,175 57,355 57,355
S61 1,002 1,002 2,198 3,524 5,623 34,506 34,506
S62 1,004 1,004 3,764 6,822 11,957 90,454 90,454
S63 1,007 1,007 4,976 10,205 18,235 71,088 71,088
S64 998 998 1,485 2,280 4,821 23,338 23,338
S65 1,020 1,020 3,653 6,518 11,342 65,342 65,342
S66 1,001 1,001 2,692 4,885 8,016 35,083 35,083
S67 1,063 1,063 6,320 11,091 17,156 129,605 129,605
S68 997 997 1,757 2,892 4,826 24,265 24,265
S69 996 996 1,304 2,032 3,794 17,131 17,131
S70 1,000 1,000 2,289 3,684 7,438 81,946 81,946
S71 1,001 1,001 2,890 4,253 7,294 25,865 25,865
S72 999 999 3,130 4,534 11,779 43,030 43,030
S73 1,001 1,001 3,879 6,459 10,747 89,115 89,115
S74 1,005 1,005 3,252 5,728 8,937 25,534 25,534
S75 1,020 1,020 3,022 4,889 10,944 46,395 46,395
S76 1,000 1,000 3,009 4,720 7,361 74,794 74,794
S77 1,006 1,006 3,554 5,732 11,082 88,080 88,080
S78 1,028 1,028 3,992 6,765 11,129 47,669 47,669

3.1.4 Gene per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 540 540 1,180 1,752 2,511 6,761 6,761
S2 638 638 1,950 2,691 3,560 6,929 6,929
S3 546 546 1,446 2,281 3,396 7,604 7,604
S4 541 541 1,492 2,231 3,151 12,666 12,666
S5 534 534 1,416 2,051 3,172 13,283 13,283
S6 514 514 1,320 1,965 2,857 12,752 12,752
S7 563 563 1,698 2,513 3,373 10,586 10,586
S8 411 411 1,345 1,952 2,975 6,108 6,108
S9 490 490 1,336 2,068 3,026 10,845 10,845
S10 251 251 1,555 2,332 3,274 11,448 11,448
S11 529 529 1,696 2,488 3,362 10,225 10,225
S12 504 504 1,304 1,842 2,635 10,267 10,267
S13 556 556 1,235 1,636 2,050 8,703 8,703
S14 484 484 1,517 2,350 3,470 11,051 11,051
S15 521 521 1,104 1,516 2,225 7,196 7,196
S16 542 542 1,358 2,088 2,902 7,850 7,850
S17 549 549 1,135 1,680 2,340 8,305 8,305
S18 472 472 1,044 1,462 1,974 5,751 5,751
S19 438 438 1,494 2,264 3,272 5,969 5,969
S20 571 571 1,877 2,593 3,133 5,312 5,312
S21 510 510 1,128 1,774 2,943 8,580 8,580
S22 590 590 1,609 2,230 3,414 8,369 8,369
S23 541 541 1,485 2,223 2,944 7,820 7,820
S24 556 556 1,524 2,210 3,098 11,119 11,119
S25 518 518 1,315 1,991 2,860 9,192 9,192
S26 488 488 1,751 2,246 3,095 7,538 7,538
S27 460 460 987 1,626 2,733 5,154 5,154
S28 395 395 1,592 2,514 3,579 11,576 11,576
S29 547 547 1,306 1,934 2,928 9,291 9,291
S30 424 424 969 1,563 2,300 11,003 11,003
S31 464 464 1,132 1,620 2,125 7,579 7,579
S32 406 406 1,602 2,313 3,266 13,055 13,055
S33 543 543 1,196 1,940 2,881 9,555 9,555
S34 514 514 1,545 2,279 2,821 9,486 9,486
S35 467 467 1,134 1,670 2,444 8,701 8,701
S36 492 492 1,470 2,200 3,070 9,920 9,920
S37 419 419 861 1,519 2,528 8,134 8,134
S38 408 408 825 1,091 1,682 9,646 9,646
S39 504 504 1,307 2,032 2,827 7,537 7,537
S40 534 534 1,047 1,416 2,099 10,328 10,328
S41 530 530 1,275 1,986 2,755 8,455 8,455
S42 472 472 1,433 2,106 2,995 12,124 12,124
S43 282 282 1,295 1,952 3,506 9,891 9,891
S44 338 338 1,060 1,382 2,029 9,964 9,964
S45 548 548 1,252 1,983 3,138 8,986 8,986
S46 438 438 1,231 1,653 2,131 8,388 8,388
S47 573 573 1,288 1,819 2,501 11,885 11,885
S48 492 492 1,483 2,245 3,220 10,266 10,266
S49 536 536 1,356 2,074 2,866 8,358 8,358
S50 496 496 1,533 2,434 3,591 12,566 12,566
S51 585 585 1,447 2,185 3,107 7,848 7,848
S52 490 490 1,132 1,567 2,080 10,249 10,249
S53 482 482 1,161 1,698 2,406 13,173 13,173
S54 560 560 1,386 2,145 3,105 11,178 11,178
S55 377 377 1,233 1,752 2,743 8,348 8,348
S56 508 508 1,450 2,271 3,340 11,315 11,315
S57 508 508 1,639 2,424 3,284 11,335 11,335
S58 484 484 1,034 1,422 1,931 9,452 9,452
S59 293 293 1,182 1,779 2,780 10,951 10,951
S60 351 351 995 1,494 2,151 9,683 9,683
S61 468 468 1,147 1,613 2,226 7,794 7,794
S62 577 577 1,549 2,373 3,504 10,509 10,509
S63 573 573 1,940 3,084 4,254 7,581 7,581
S64 648 648 945 1,344 2,231 5,604 5,604
S65 597 597 1,700 2,460 3,462 8,489 8,489
S66 519 519 1,420 2,120 3,026 7,743 7,743
S67 521 521 2,276 3,278 4,170 11,178 11,178
S68 522 522 1,014 1,451 2,066 5,474 5,474
S69 471 471 793 1,129 1,775 4,978 4,978
S70 456 456 1,217 1,734 2,890 9,222 9,222
S71 504 504 1,358 1,823 2,614 6,506 6,506
S72 527 527 1,585 2,068 3,488 8,484 8,484
S73 526 526 1,714 2,460 3,355 9,355 9,355
S74 470 470 1,612 2,464 3,257 6,467 6,467
S75 439 439 1,396 1,955 3,262 6,739 6,739
S76 365 365 1,410 1,944 2,713 10,134 10,134
S77 620 620 1,646 2,187 3,565 8,879 8,879
S78 492 492 1,804 2,486 3,604 7,299 7,299

3.1.5 Mitochondrial (%) per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 0.37 0.37 3.99 5.80 9.34 19.97 19.97
S2 0.33 0.33 3.85 7.14 11.55 19.81 19.81
S3 0.21 0.21 3.00 4.78 8.27 19.99 19.99
S4 0.25 0.25 3.85 5.78 9.42 19.98 19.98
S5 0.19 0.19 3.39 5.61 8.99 19.87 19.87
S6 0.16 0.16 3.07 4.96 8.29 19.97 19.97
S7 0.44 0.44 4.91 7.51 10.96 19.98 19.98
S8 0.44 0.44 3.90 6.06 9.24 19.91 19.91
S9 0.00 0.00 3.02 4.62 7.60 19.94 19.94
S10 0.17 0.17 3.98 6.53 10.10 20.00 20.00
S11 0.41 0.41 3.46 5.54 8.57 20.00 20.00
S12 0.28 0.28 4.42 7.06 10.54 19.98 19.98
S13 0.00 0.00 1.97 2.96 5.03 19.93 19.93
S14 0.48 0.48 3.26 5.15 8.29 19.96 19.96
S15 0.22 0.22 2.18 3.56 7.31 19.97 19.97
S16 0.29 0.29 3.17 5.07 8.81 20.00 20.00
S17 0.31 0.31 3.43 4.85 7.27 19.86 19.86
S18 0.60 0.60 5.91 9.27 12.99 19.91 19.91
S19 0.81 0.81 4.43 6.31 10.09 19.90 19.90
S20 0.20 0.20 4.38 6.33 9.10 19.99 19.99
S21 0.00 0.00 2.21 3.35 6.02 19.97 19.97
S22 0.57 0.57 2.84 4.44 7.21 19.95 19.95
S23 0.00 0.00 2.80 3.78 5.57 19.89 19.89
S24 0.32 0.32 4.23 5.95 8.95 19.96 19.96
S25 0.12 0.12 3.64 5.12 7.45 20.00 20.00
S26 0.68 0.68 3.39 5.08 7.22 19.81 19.81
S27 1.93 1.93 3.53 5.00 9.02 19.44 19.44
S28 0.40 0.40 3.70 5.09 7.53 19.99 19.99
S29 0.17 0.17 3.41 4.69 6.88 19.69 19.69
S30 0.19 0.19 2.24 3.38 4.77 19.87 19.87
S31 0.21 0.21 4.24 6.75 10.63 19.98 19.98
S32 0.20 0.20 3.51 5.40 8.20 19.93 19.93
S33 0.04 0.04 2.62 3.85 6.22 19.97 19.97
S34 0.18 0.18 4.59 7.12 11.56 20.00 20.00
S35 0.00 0.00 2.77 3.87 5.64 19.98 19.98
S36 0.28 0.28 2.86 4.58 7.60 19.99 19.99
S37 0.00 0.00 5.12 7.59 10.76 19.98 19.98
S38 0.29 0.29 2.80 4.25 6.34 19.95 19.95
S39 0.40 0.40 4.06 5.78 8.56 19.85 19.85
S40 0.10 0.10 2.14 2.95 4.62 19.92 19.92
S41 0.86 0.86 4.54 8.45 12.73 20.00 20.00
S42 0.16 0.16 4.03 6.01 9.07 20.00 20.00
S43 0.13 0.13 2.72 4.67 7.56 19.96 19.96
S44 0.18 0.18 2.66 3.63 6.37 19.99 19.99
S45 0.54 0.54 3.87 5.87 9.22 19.97 19.97
S46 0.13 0.13 2.22 3.67 6.57 19.85 19.85
S47 0.24 0.24 5.38 7.90 11.66 19.94 19.94
S48 0.36 0.36 3.70 5.69 9.15 19.99 19.99
S49 0.43 0.43 5.35 8.17 12.39 19.98 19.98
S50 0.05 0.05 3.85 5.79 8.84 19.99 19.99
S51 0.19 0.19 4.39 7.11 11.33 19.96 19.96
S52 0.21 0.21 3.02 4.82 7.72 19.99 19.99
S53 0.18 0.18 2.60 3.64 5.62 19.95 19.95
S54 0.18 0.18 3.54 5.30 8.04 19.97 19.97
S55 0.13 0.13 3.41 5.64 8.65 19.99 19.99
S56 0.04 0.04 3.21 4.90 7.69 19.98 19.98
S57 0.03 0.03 2.82 4.27 6.76 19.97 19.97
S58 0.30 0.30 2.77 3.59 4.87 19.97 19.97
S59 0.45 0.45 3.19 5.07 8.29 19.98 19.98
S60 0.17 0.17 3.18 5.90 9.72 19.98 19.98
S61 0.06 0.06 2.06 3.09 5.00 19.71 19.71
S62 0.39 0.39 3.52 7.17 11.53 19.98 19.98
S63 0.55 0.55 3.17 4.54 6.77 19.87 19.87
S64 1.43 1.43 2.50 3.63 5.38 19.25 19.25
S65 0.68 0.68 3.77 5.12 7.67 19.85 19.85
S66 0.08 0.08 2.98 4.80 7.86 19.94 19.94
S67 0.89 0.89 4.70 6.92 9.81 19.96 19.96
S68 0.70 0.70 4.10 6.05 8.34 19.97 19.97
S69 0.29 0.29 4.45 7.64 11.96 19.99 19.99
S70 0.00 0.00 2.64 4.27 6.95 19.82 19.82
S71 0.17 0.17 3.71 6.04 9.75 19.91 19.91
S72 0.00 0.00 1.58 2.22 3.30 19.68 19.68
S73 0.38 0.38 4.46 7.25 10.92 19.88 19.88
S74 0.61 0.61 4.79 7.47 11.94 19.99 19.99
S75 1.05 1.05 3.94 5.77 9.91 19.98 19.98
S76 0.40 0.40 3.63 5.74 8.82 19.96 19.96
S77 0.91 0.91 2.87 4.39 7.01 19.69 19.69
S78 0.47 0.47 2.22 3.58 5.82 19.92 19.92


3.2 Distribution Plots for Quality Control Metrics

Thresholds, represented by dashed lines, were implemented to filter the data and only retain cells of high quality.

3.2.1 UMI per cell

3.2.2 Gene per cell

3.2.3 Mitochondrial (%) per cell


3.3 Barcodes contamination

3.3.1 Raw count

Number of barcodes shared between pairs of samples post-QC.

## [1] "No overlaps among samples' barcodes."

3.3.2 Jaccard Index

Fraction (%) of barcodes shared between pairs of samples post-QC.

## [1] "No overlaps among samples' barcodes."


3.4 Doublet Cell Detection using Scrublet Scoring System

Observed scores are used for doublet classification. Dashed line indicates the threshold used to identify doublets.

3.4.1 Observed scores

3.4.2 Simulated scores


3.5 Impact of Quality Control on Cell, Gene, and UMI Abundances

3.5.1 Number of cells and genes per sample

diamonds and diamonds refer to before and after QC, respectively.

3.5.2 Average number of UMIs/cell and genes/cell per sample

The error bars represent the standard deviation of the number of UMIs and genes across cells per sample.


3.6 Key QC Metrics of Merged Samples

min 0% 25% 50% 75% 100% max
UMI per cell 860 860 2,545 4,595 8,401 176,969 176,969
Gene per cell 251 251 1,270 1,919 2,863 13,283 13,283
Mitochondrial (%) per cell 0.00 0.00 3.09 4.89 8.03 20.00 20.00

3.7 Visualization of Merged scRNA-Seq Object in 2D Space

Note: Labels may have been removed if they overlap excessively.

3.7.1 Signacx SPRING Projection

3.7.2 Seurat UMAP Projection

3.7.3 Seurat UMAP Projection (no-batch correction)


3.8 Visualizing Cell QC Metrics Using Dimensionality Reduction

Note: QC metrics based on kernel density estimation.

3.8.1 UMI per cell - SPRING Projection

3.8.2 Gene per cell - SPRING Projection

3.8.3 Mitochondrial (%) per cell - SPRING Projection

3.8.4 UMI per cell - UMAP Projection

3.8.5 Gene per cell - UMAP Projection

3.8.6 Mitochondrial (%) per cell - UMAP Projection


4 BridgeCluster

Note: Labels may have been removed if they overlap excessively.

4.1 Visualizing Cell Populations with Dimensionality Reduction

4.1.1 Signacx SPRING Projection

4.1.2 Seurat UMAP Projection


4.2 Cell Cluster Composition

Values at the top of each bar indicate the percentage of cells

4.2.1 Seurat

4.2.2 Signacx


4.3 Statistical Dispersion Analysis of Cell Composition in Clusters across sample

This measurement is a proxy to batch effect artifacts. Values adjacent to each point indicate the number of cells.

4.3.1 Seurat

4.3.2 Signacx


5 BridgeAnnotation

5.1 Visualizing Distinct Cell Type Populations with Dimensionality Reduction

Note: Labels may have been removed if they overlap excessively.

5.1.1 Signacx SPRING Projection

5.1.2 Signacx UMAP Projection


5.2 Cell Type Population Composition

Values at the top of each bar indicate the percentage of cells

5.2.1 Signacx


6 References

Publication: no referrence

Data Availability: no referrence


7 Session Information

This is the output of sessionInfo() on the computing system on which this document was compiled

## R version 4.3.2 (2023-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## Random number generation:
##  RNG:     L'Ecuyer-CMRG 
##  Normal:  Inversion 
##  Sample:  Rejection 
##  
## locale:
## [1] C
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    grid      stats     graphics  grDevices
## [6] utils     datasets  methods   base     
## 
## other attached packages:
##  [1] edgeR_4.0.2                 limma_3.58.1               
##  [3] slingshot_2.10.0            TrajectoryUtils_1.10.0     
##  [5] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
##  [7] Biobase_2.62.0              GenomicRanges_1.54.1       
##  [9] GenomeInfoDb_1.38.1         IRanges_2.36.0             
## [11] S4Vectors_0.40.2            BiocGenerics_0.48.1        
## [13] MatrixGenerics_1.14.0       matrixStats_1.1.0          
## [15] princurve_2.1.6             Nebulosa_1.12.0            
## [17] patchwork_1.1.3             data.table_1.14.10         
## [19] ComplexHeatmap_2.18.0       visNetwork_2.1.2           
## [21] plotly_4.10.3               data.tree_1.1.0            
## [23] DT_0.30                     readxl_1.4.3               
## [25] gtools_3.9.5                gplots_3.1.3               
## [27] gridtext_0.1.5              igraph_1.5.1               
## [29] sargent_1.0.1               SignacX_2.2.5              
## [31] harmony_1.2.0               Rcpp_1.0.11                
## [33] kableExtra_1.3.4            purrr_1.0.2                
## [35] reticulate_1.34.0           RColorBrewer_1.1-3         
## [37] cowplot_1.1.1               gridExtra_2.3              
## [39] pheatmap_1.0.12             ggrepel_0.9.4              
## [41] ggplot2_3.4.4               Seurat_5.0.1               
## [43] SeuratObject_5.0.1          sp_2.1-2                   
## [45] dplyr_1.1.4                 optparse_1.7.3             
## 
## loaded via a namespace (and not attached):
##   [1] spatstat.sparse_3.0-3   bitops_1.0-7           
##   [3] httr_1.4.7              webshot_0.5.5          
##   [5] doParallel_1.0.17       tools_4.3.2            
##   [7] sctransform_0.4.1       utf8_1.2.4             
##   [9] R6_2.5.1                mgcv_1.9-0             
##  [11] lazyeval_0.2.2          uwot_0.1.16            
##  [13] ggdist_3.3.1            GetoptLong_1.0.5       
##  [15] withr_2.5.2             progressr_0.14.0       
##  [17] cli_3.6.1               spatstat.explore_3.2-5 
##  [19] fastDummies_1.7.3       labeling_0.4.3         
##  [21] sass_0.4.8              mvtnorm_1.2-4          
##  [23] spatstat.data_3.0-3     proxy_0.4-27           
##  [25] ggridges_0.5.4          pbapply_1.7-2          
##  [27] commonmark_1.9.0        systemfonts_1.0.5      
##  [29] R.utils_2.12.3          svglite_2.1.2          
##  [31] parallelly_1.36.0       rstudioapi_0.15.0      
##  [33] generics_0.1.3          shape_1.4.6            
##  [35] crosstalk_1.2.1         ica_1.0-3              
##  [37] spatstat.random_3.2-2   distributional_0.3.2   
##  [39] Matrix_1.6-4            fansi_1.0.6            
##  [41] DescTools_0.99.52       abind_1.4-5            
##  [43] R.methodsS3_1.8.2       lifecycle_1.0.4        
##  [45] yaml_2.3.7              SparseArray_1.2.2      
##  [47] Rtsne_0.17              promises_1.2.1         
##  [49] crayon_1.5.2            miniUI_0.1.1.1         
##  [51] lattice_0.22-5          pillar_1.9.0           
##  [53] knitr_1.45              rjson_0.2.21           
##  [55] boot_1.3-28.1           gld_2.6.6              
##  [57] future.apply_1.11.0     codetools_0.2-19       
##  [59] leiden_0.4.3.1          glue_1.6.2             
##  [61] vctrs_0.6.5             png_0.1-8              
##  [63] spam_2.10-0             neuralnet_1.44.2       
##  [65] cellranger_1.1.0        gtable_0.3.4           
##  [67] cachem_1.0.8            ks_1.14.1              
##  [69] xfun_0.41               S4Arrays_1.2.0         
##  [71] mime_0.12               pracma_2.4.4           
##  [73] survival_3.5-7          pbmcapply_1.5.1        
##  [75] iterators_1.0.14        statmod_1.5.0          
##  [77] ellipsis_0.3.2          fitdistrplus_1.1-11    
##  [79] ROCR_1.0-11             nlme_3.1-164           
##  [81] RcppAnnoy_0.0.21        bslib_0.6.1            
##  [83] irlba_2.3.5.1           KernSmooth_2.23-22     
##  [85] colorspace_2.1-0        Exact_3.2              
##  [87] tidyselect_1.2.0        compiler_4.3.2         
##  [89] rvest_1.0.3             expm_0.999-8           
##  [91] xml2_1.3.6              DelayedArray_0.28.0    
##  [93] scales_1.3.0            caTools_1.18.2         
##  [95] lmtest_0.9-40           stringr_1.5.1          
##  [97] digest_0.6.33           goftest_1.2-3          
##  [99] spatstat.utils_3.0-4    rmarkdown_2.25         
## [101] RhpcBLASctl_0.23-42     XVector_0.42.0         
## [103] htmltools_0.5.7         pkgconfig_2.0.3        
## [105] highr_0.10              fastmap_1.1.1          
## [107] rlang_1.1.2             GlobalOptions_0.1.2    
## [109] htmlwidgets_1.6.4       shiny_1.8.0            
## [111] jquerylib_0.1.4         farver_2.1.1           
## [113] zoo_1.8-12              jsonlite_1.8.8         
## [115] mclust_6.0.1            R.oo_1.25.0            
## [117] RCurl_1.98-1.13         magrittr_2.0.3         
## [119] GenomeInfoDbData_1.2.11 dotCall64_1.1-1        
## [121] munsell_0.5.0           stringi_1.8.2          
## [123] rootSolve_1.8.2.4       zlibbioc_1.48.0        
## [125] MASS_7.3-60             plyr_1.8.9             
## [127] parallel_4.3.2          listenv_0.9.0          
## [129] lmom_3.0                deldir_2.0-2           
## [131] splines_4.3.2           tensor_1.5             
## [133] circlize_0.4.15         locfit_1.5-9.8         
## [135] spatstat.geom_3.2-7     markdown_1.12          
## [137] RcppHNSW_0.5.0          reshape2_1.4.4         
## [139] evaluate_0.23           foreach_1.5.2          
## [141] httpuv_1.6.13           RANN_2.6.1             
## [143] tidyr_1.3.0             getopt_1.20.4          
## [145] polyclip_1.10-6         future_1.33.0          
## [147] clue_0.3-65             scattermore_1.2        
## [149] xtable_1.8-4            e1071_1.7-14           
## [151] RSpectra_0.16-1         later_1.3.2            
## [153] viridisLite_0.4.2       class_7.3-22           
## [155] tibble_3.2.1            cluster_2.1.6          
## [157] globals_0.16.2