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| 1 | { |
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| 2 | "cells": [ |
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| 3 | { |
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| 4 | "cell_type": "code", |
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| 5 | "execution_count": 1, |
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| 6 | "id": "chubby-conversion", |
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| 7 | "metadata": {}, |
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| 8 | "outputs": [ |
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| 9 | { |
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| 10 | "name": "stderr", |
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| 11 | "output_type": "stream", |
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| 12 | "text": [ |
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| 13 | "Loading required package: limma\n", |
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| 14 | "\n", |
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| 15 | "\n", |
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| 16 | "Attaching package: ‘dplyr’\n", |
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| 17 | "\n", |
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| 19 | "The following objects are masked from ‘package:stats’:\n", |
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| 26 | " intersect, setdiff, setequal, union\n", |
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| 27 | "\n", |
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| 28 | "\n", |
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| 29 | "\n", |
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| 30 | "Attaching package: ‘tidyr’\n", |
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| 31 | "\n", |
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| 32 | "\n", |
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| 33 | "The following object is masked from ‘package:magrittr’:\n", |
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| 35 | " extract\n", |
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| 38 | "\n", |
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| 39 | "Attaching package: ‘reshape’\n", |
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| 40 | "\n", |
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| 43 | "\n", |
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| 44 | " expand, smiths\n", |
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| 48 | "\n", |
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| 49 | " rename\n", |
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| 51 | "\n", |
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| 53 | "\n", |
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| 54 | "\n", |
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| 55 | "Attaching package: ‘gplots’\n", |
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| 56 | "\n", |
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| 67 | "The following objects are masked from ‘package:reshape’:\n", |
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| 69 | " colsplit, melt, recast\n", |
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| 74 | " smiths\n", |
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| 371 | "\\item 'ggplot2'\n", |
||
| 372 | "\\item 'magrittr'\n", |
||
| 373 | "\\item 'dplyr'\n", |
||
| 374 | "\\item 'edgeR'\n", |
||
| 375 | "\\item 'limma'\n", |
||
| 376 | "\\item 'stats'\n", |
||
| 377 | "\\item 'graphics'\n", |
||
| 378 | "\\item 'grDevices'\n", |
||
| 379 | "\\item 'utils'\n", |
||
| 380 | "\\item 'datasets'\n", |
||
| 381 | "\\item 'methods'\n", |
||
| 382 | "\\item 'base'\n", |
||
| 383 | "\\end{enumerate*}\n", |
||
| 384 | "\n", |
||
| 385 | "\\end{enumerate}\n" |
||
| 386 | ], |
||
| 387 | "text/markdown": [ |
||
| 388 | "1. 1. 'edgeR'\n", |
||
| 389 | "2. 'limma'\n", |
||
| 390 | "3. 'stats'\n", |
||
| 391 | "4. 'graphics'\n", |
||
| 392 | "5. 'grDevices'\n", |
||
| 393 | "6. 'utils'\n", |
||
| 394 | "7. 'datasets'\n", |
||
| 395 | "8. 'methods'\n", |
||
| 396 | "9. 'base'\n", |
||
| 397 | "\n", |
||
| 398 | "\n", |
||
| 399 | "\n", |
||
| 400 | "2. 1. 'dplyr'\n", |
||
| 401 | "2. 'edgeR'\n", |
||
| 402 | "3. 'limma'\n", |
||
| 403 | "4. 'stats'\n", |
||
| 404 | "5. 'graphics'\n", |
||
| 405 | "6. 'grDevices'\n", |
||
| 406 | "7. 'utils'\n", |
||
| 407 | "8. 'datasets'\n", |
||
| 408 | "9. 'methods'\n", |
||
| 409 | "10. 'base'\n", |
||
| 410 | "\n", |
||
| 411 | "\n", |
||
| 412 | "\n", |
||
| 413 | "3. 1. 'magrittr'\n", |
||
| 414 | "2. 'dplyr'\n", |
||
| 415 | "3. 'edgeR'\n", |
||
| 416 | "4. 'limma'\n", |
||
| 417 | "5. 'stats'\n", |
||
| 418 | "6. 'graphics'\n", |
||
| 419 | "7. 'grDevices'\n", |
||
| 420 | "8. 'utils'\n", |
||
| 421 | "9. 'datasets'\n", |
||
| 422 | "10. 'methods'\n", |
||
| 423 | "11. 'base'\n", |
||
| 424 | "\n", |
||
| 425 | "\n", |
||
| 426 | "\n", |
||
| 427 | "4. 1. 'ggplot2'\n", |
||
| 428 | "2. 'magrittr'\n", |
||
| 429 | "3. 'dplyr'\n", |
||
| 430 | "4. 'edgeR'\n", |
||
| 431 | "5. 'limma'\n", |
||
| 432 | "6. 'stats'\n", |
||
| 433 | "7. 'graphics'\n", |
||
| 434 | "8. 'grDevices'\n", |
||
| 435 | "9. 'utils'\n", |
||
| 436 | "10. 'datasets'\n", |
||
| 437 | "11. 'methods'\n", |
||
| 438 | "12. 'base'\n", |
||
| 439 | "\n", |
||
| 440 | "\n", |
||
| 441 | "\n", |
||
| 442 | "5. 1. 'anndata'\n", |
||
| 443 | "2. 'ggplot2'\n", |
||
| 444 | "3. 'magrittr'\n", |
||
| 445 | "4. 'dplyr'\n", |
||
| 446 | "5. 'edgeR'\n", |
||
| 447 | "6. 'limma'\n", |
||
| 448 | "7. 'stats'\n", |
||
| 449 | "8. 'graphics'\n", |
||
| 450 | "9. 'grDevices'\n", |
||
| 451 | "10. 'utils'\n", |
||
| 452 | "11. 'datasets'\n", |
||
| 453 | "12. 'methods'\n", |
||
| 454 | "13. 'base'\n", |
||
| 455 | "\n", |
||
| 456 | "\n", |
||
| 457 | "\n", |
||
| 458 | "6. 1. 'reticulate'\n", |
||
| 459 | "2. 'anndata'\n", |
||
| 460 | "3. 'ggplot2'\n", |
||
| 461 | "4. 'magrittr'\n", |
||
| 462 | "5. 'dplyr'\n", |
||
| 463 | "6. 'edgeR'\n", |
||
| 464 | "7. 'limma'\n", |
||
| 465 | "8. 'stats'\n", |
||
| 466 | "9. 'graphics'\n", |
||
| 467 | "10. 'grDevices'\n", |
||
| 468 | "11. 'utils'\n", |
||
| 469 | "12. 'datasets'\n", |
||
| 470 | "13. 'methods'\n", |
||
| 471 | "14. 'base'\n", |
||
| 472 | "\n", |
||
| 473 | "\n", |
||
| 474 | "\n", |
||
| 475 | "7. 1. 'tidyr'\n", |
||
| 476 | "2. 'reticulate'\n", |
||
| 477 | "3. 'anndata'\n", |
||
| 478 | "4. 'ggplot2'\n", |
||
| 479 | "5. 'magrittr'\n", |
||
| 480 | "6. 'dplyr'\n", |
||
| 481 | "7. 'edgeR'\n", |
||
| 482 | "8. 'limma'\n", |
||
| 483 | "9. 'stats'\n", |
||
| 484 | "10. 'graphics'\n", |
||
| 485 | "11. 'grDevices'\n", |
||
| 486 | "12. 'utils'\n", |
||
| 487 | "13. 'datasets'\n", |
||
| 488 | "14. 'methods'\n", |
||
| 489 | "15. 'base'\n", |
||
| 490 | "\n", |
||
| 491 | "\n", |
||
| 492 | "\n", |
||
| 493 | "8. 1. 'reshape'\n", |
||
| 494 | "2. 'tidyr'\n", |
||
| 495 | "3. 'reticulate'\n", |
||
| 496 | "4. 'anndata'\n", |
||
| 497 | "5. 'ggplot2'\n", |
||
| 498 | "6. 'magrittr'\n", |
||
| 499 | "7. 'dplyr'\n", |
||
| 500 | "8. 'edgeR'\n", |
||
| 501 | "9. 'limma'\n", |
||
| 502 | "10. 'stats'\n", |
||
| 503 | "11. 'graphics'\n", |
||
| 504 | "12. 'grDevices'\n", |
||
| 505 | "13. 'utils'\n", |
||
| 506 | "14. 'datasets'\n", |
||
| 507 | "15. 'methods'\n", |
||
| 508 | "16. 'base'\n", |
||
| 509 | "\n", |
||
| 510 | "\n", |
||
| 511 | "\n", |
||
| 512 | "9. 1. 'SeuratObject'\n", |
||
| 513 | "2. 'Seurat'\n", |
||
| 514 | "3. 'reshape'\n", |
||
| 515 | "4. 'tidyr'\n", |
||
| 516 | "5. 'reticulate'\n", |
||
| 517 | "6. 'anndata'\n", |
||
| 518 | "7. 'ggplot2'\n", |
||
| 519 | "8. 'magrittr'\n", |
||
| 520 | "9. 'dplyr'\n", |
||
| 521 | "10. 'edgeR'\n", |
||
| 522 | "11. 'limma'\n", |
||
| 523 | "12. 'stats'\n", |
||
| 524 | "13. 'graphics'\n", |
||
| 525 | "14. 'grDevices'\n", |
||
| 526 | "15. 'utils'\n", |
||
| 527 | "16. 'datasets'\n", |
||
| 528 | "17. 'methods'\n", |
||
| 529 | "18. 'base'\n", |
||
| 530 | "\n", |
||
| 531 | "\n", |
||
| 532 | "\n", |
||
| 533 | "10. 1. 'scales'\n", |
||
| 534 | "2. 'SeuratObject'\n", |
||
| 535 | "3. 'Seurat'\n", |
||
| 536 | "4. 'reshape'\n", |
||
| 537 | "5. 'tidyr'\n", |
||
| 538 | "6. 'reticulate'\n", |
||
| 539 | "7. 'anndata'\n", |
||
| 540 | "8. 'ggplot2'\n", |
||
| 541 | "9. 'magrittr'\n", |
||
| 542 | "10. 'dplyr'\n", |
||
| 543 | "11. 'edgeR'\n", |
||
| 544 | "12. 'limma'\n", |
||
| 545 | "13. 'stats'\n", |
||
| 546 | "14. 'graphics'\n", |
||
| 547 | "15. 'grDevices'\n", |
||
| 548 | "16. 'utils'\n", |
||
| 549 | "17. 'datasets'\n", |
||
| 550 | "18. 'methods'\n", |
||
| 551 | "19. 'base'\n", |
||
| 552 | "\n", |
||
| 553 | "\n", |
||
| 554 | "\n", |
||
| 555 | "11. 1. 'gplots'\n", |
||
| 556 | "2. 'scales'\n", |
||
| 557 | "3. 'SeuratObject'\n", |
||
| 558 | "4. 'Seurat'\n", |
||
| 559 | "5. 'reshape'\n", |
||
| 560 | "6. 'tidyr'\n", |
||
| 561 | "7. 'reticulate'\n", |
||
| 562 | "8. 'anndata'\n", |
||
| 563 | "9. 'ggplot2'\n", |
||
| 564 | "10. 'magrittr'\n", |
||
| 565 | "11. 'dplyr'\n", |
||
| 566 | "12. 'edgeR'\n", |
||
| 567 | "13. 'limma'\n", |
||
| 568 | "14. 'stats'\n", |
||
| 569 | "15. 'graphics'\n", |
||
| 570 | "16. 'grDevices'\n", |
||
| 571 | "17. 'utils'\n", |
||
| 572 | "18. 'datasets'\n", |
||
| 573 | "19. 'methods'\n", |
||
| 574 | "20. 'base'\n", |
||
| 575 | "\n", |
||
| 576 | "\n", |
||
| 577 | "\n", |
||
| 578 | "12. 1. 'reshape2'\n", |
||
| 579 | "2. 'gplots'\n", |
||
| 580 | "3. 'scales'\n", |
||
| 581 | "4. 'SeuratObject'\n", |
||
| 582 | "5. 'Seurat'\n", |
||
| 583 | "6. 'reshape'\n", |
||
| 584 | "7. 'tidyr'\n", |
||
| 585 | "8. 'reticulate'\n", |
||
| 586 | "9. 'anndata'\n", |
||
| 587 | "10. 'ggplot2'\n", |
||
| 588 | "11. 'magrittr'\n", |
||
| 589 | "12. 'dplyr'\n", |
||
| 590 | "13. 'edgeR'\n", |
||
| 591 | "14. 'limma'\n", |
||
| 592 | "15. 'stats'\n", |
||
| 593 | "16. 'graphics'\n", |
||
| 594 | "17. 'grDevices'\n", |
||
| 595 | "18. 'utils'\n", |
||
| 596 | "19. 'datasets'\n", |
||
| 597 | "20. 'methods'\n", |
||
| 598 | "21. 'base'\n", |
||
| 599 | "\n", |
||
| 600 | "\n", |
||
| 601 | "\n", |
||
| 602 | "\n", |
||
| 603 | "\n" |
||
| 604 | ], |
||
| 605 | "text/plain": [ |
||
| 606 | "[[1]]\n", |
||
| 607 | "[1] \"edgeR\" \"limma\" \"stats\" \"graphics\" \"grDevices\" \"utils\" \n", |
||
| 608 | "[7] \"datasets\" \"methods\" \"base\" \n", |
||
| 609 | "\n", |
||
| 610 | "[[2]]\n", |
||
| 611 | " [1] \"dplyr\" \"edgeR\" \"limma\" \"stats\" \"graphics\" \"grDevices\"\n", |
||
| 612 | " [7] \"utils\" \"datasets\" \"methods\" \"base\" \n", |
||
| 613 | "\n", |
||
| 614 | "[[3]]\n", |
||
| 615 | " [1] \"magrittr\" \"dplyr\" \"edgeR\" \"limma\" \"stats\" \"graphics\" \n", |
||
| 616 | " [7] \"grDevices\" \"utils\" \"datasets\" \"methods\" \"base\" \n", |
||
| 617 | "\n", |
||
| 618 | "[[4]]\n", |
||
| 619 | " [1] \"ggplot2\" \"magrittr\" \"dplyr\" \"edgeR\" \"limma\" \"stats\" \n", |
||
| 620 | " [7] \"graphics\" \"grDevices\" \"utils\" \"datasets\" \"methods\" \"base\" \n", |
||
| 621 | "\n", |
||
| 622 | "[[5]]\n", |
||
| 623 | " [1] \"anndata\" \"ggplot2\" \"magrittr\" \"dplyr\" \"edgeR\" \"limma\" \n", |
||
| 624 | " [7] \"stats\" \"graphics\" \"grDevices\" \"utils\" \"datasets\" \"methods\" \n", |
||
| 625 | "[13] \"base\" \n", |
||
| 626 | "\n", |
||
| 627 | "[[6]]\n", |
||
| 628 | " [1] \"reticulate\" \"anndata\" \"ggplot2\" \"magrittr\" \"dplyr\" \n", |
||
| 629 | " [6] \"edgeR\" \"limma\" \"stats\" \"graphics\" \"grDevices\" \n", |
||
| 630 | "[11] \"utils\" \"datasets\" \"methods\" \"base\" \n", |
||
| 631 | "\n", |
||
| 632 | "[[7]]\n", |
||
| 633 | " [1] \"tidyr\" \"reticulate\" \"anndata\" \"ggplot2\" \"magrittr\" \n", |
||
| 634 | " [6] \"dplyr\" \"edgeR\" \"limma\" \"stats\" \"graphics\" \n", |
||
| 635 | "[11] \"grDevices\" \"utils\" \"datasets\" \"methods\" \"base\" \n", |
||
| 636 | "\n", |
||
| 637 | "[[8]]\n", |
||
| 638 | " [1] \"reshape\" \"tidyr\" \"reticulate\" \"anndata\" \"ggplot2\" \n", |
||
| 639 | " [6] \"magrittr\" \"dplyr\" \"edgeR\" \"limma\" \"stats\" \n", |
||
| 640 | "[11] \"graphics\" \"grDevices\" \"utils\" \"datasets\" \"methods\" \n", |
||
| 641 | "[16] \"base\" \n", |
||
| 642 | "\n", |
||
| 643 | "[[9]]\n", |
||
| 644 | " [1] \"SeuratObject\" \"Seurat\" \"reshape\" \"tidyr\" \"reticulate\" \n", |
||
| 645 | " [6] \"anndata\" \"ggplot2\" \"magrittr\" \"dplyr\" \"edgeR\" \n", |
||
| 646 | "[11] \"limma\" \"stats\" \"graphics\" \"grDevices\" \"utils\" \n", |
||
| 647 | "[16] \"datasets\" \"methods\" \"base\" \n", |
||
| 648 | "\n", |
||
| 649 | "[[10]]\n", |
||
| 650 | " [1] \"scales\" \"SeuratObject\" \"Seurat\" \"reshape\" \"tidyr\" \n", |
||
| 651 | " [6] \"reticulate\" \"anndata\" \"ggplot2\" \"magrittr\" \"dplyr\" \n", |
||
| 652 | "[11] \"edgeR\" \"limma\" \"stats\" \"graphics\" \"grDevices\" \n", |
||
| 653 | "[16] \"utils\" \"datasets\" \"methods\" \"base\" \n", |
||
| 654 | "\n", |
||
| 655 | "[[11]]\n", |
||
| 656 | " [1] \"gplots\" \"scales\" \"SeuratObject\" \"Seurat\" \"reshape\" \n", |
||
| 657 | " [6] \"tidyr\" \"reticulate\" \"anndata\" \"ggplot2\" \"magrittr\" \n", |
||
| 658 | "[11] \"dplyr\" \"edgeR\" \"limma\" \"stats\" \"graphics\" \n", |
||
| 659 | "[16] \"grDevices\" \"utils\" \"datasets\" \"methods\" \"base\" \n", |
||
| 660 | "\n", |
||
| 661 | "[[12]]\n", |
||
| 662 | " [1] \"reshape2\" \"gplots\" \"scales\" \"SeuratObject\" \"Seurat\" \n", |
||
| 663 | " [6] \"reshape\" \"tidyr\" \"reticulate\" \"anndata\" \"ggplot2\" \n", |
||
| 664 | "[11] \"magrittr\" \"dplyr\" \"edgeR\" \"limma\" \"stats\" \n", |
||
| 665 | "[16] \"graphics\" \"grDevices\" \"utils\" \"datasets\" \"methods\" \n", |
||
| 666 | "[21] \"base\" \n" |
||
| 667 | ] |
||
| 668 | }, |
||
| 669 | "metadata": {}, |
||
| 670 | "output_type": "display_data" |
||
| 671 | } |
||
| 672 | ], |
||
| 673 | "source": [ |
||
| 674 | "packages <- c(\"edgeR\", \"dplyr\", \"magrittr\", \"ggplot2\",\"anndata\",\"reticulate\",\n", |
||
| 675 | " \"tidyr\", \"reshape\", \"Seurat\", \"scales\",\"gplots\",\"reshape2\"\n", |
||
| 676 | " )\n", |
||
| 677 | "lapply(packages, library, character.only = TRUE)" |
||
| 678 | ] |
||
| 679 | }, |
||
| 680 | { |
||
| 681 | "cell_type": "code", |
||
| 682 | "execution_count": 2, |
||
| 683 | "id": "insured-woman", |
||
| 684 | "metadata": {}, |
||
| 685 | "outputs": [ |
||
| 686 | { |
||
| 687 | "name": "stderr", |
||
| 688 | "output_type": "stream", |
||
| 689 | "text": [ |
||
| 690 | "Warning message in asMethod(object):\n", |
||
| 691 | "“sparse->dense coercion: allocating vector of size 5.0 GiB”\n" |
||
| 692 | ] |
||
| 693 | } |
||
| 694 | ], |
||
| 695 | "source": [ |
||
| 696 | "h5ad_file <- \"./CTCL/object_revision/CTCL1-8_tumorcell_raw.h5ad\" ### CTCL1-8: tumours with seperate dermal and epidemal samples\n", |
||
| 697 | "sdata <- read_h5ad(h5ad_file)\n", |
||
| 698 | "Target_subset <- CreateSeuratObject(counts = t(as.matrix(sdata$X)), meta.data = sdata$obs)" |
||
| 699 | ] |
||
| 700 | }, |
||
| 701 | { |
||
| 702 | "cell_type": "code", |
||
| 703 | "execution_count": 3, |
||
| 704 | "id": "serial-message", |
||
| 705 | "metadata": {}, |
||
| 706 | "outputs": [], |
||
| 707 | "source": [ |
||
| 708 | "pseudobulk <- function(seurat_object, column, label){\n", |
||
| 709 | " seurat_object@meta.data[,column] <- as.character(seurat_object@meta.data[,column])\n", |
||
| 710 | " tmp <- seurat_object[,seurat_object@meta.data[,column]==label]\n", |
||
| 711 | " tmp2 <- Matrix::rowSums(tmp@assays$RNA@counts)\n", |
||
| 712 | " return(tmp2)\n", |
||
| 713 | "}\n", |
||
| 714 | "\n", |
||
| 715 | "### column -- donor_id\n", |
||
| 716 | "### label -- each donor" |
||
| 717 | ] |
||
| 718 | }, |
||
| 719 | { |
||
| 720 | "cell_type": "code", |
||
| 721 | "execution_count": 4, |
||
| 722 | "id": "worth-religious", |
||
| 723 | "metadata": {}, |
||
| 724 | "outputs": [ |
||
| 725 | { |
||
| 726 | "data": { |
||
| 727 | "text/plain": [ |
||
| 728 | "\n", |
||
| 729 | "CTCL1 CTCL2 CTCL3 CTCL4 CTCL5 CTCL6 CTCL7 CTCL8 \n", |
||
| 730 | " 8069 1857 9411 1073 7636 3745 3673 7139 " |
||
| 731 | ] |
||
| 732 | }, |
||
| 733 | "metadata": {}, |
||
| 734 | "output_type": "display_data" |
||
| 735 | }, |
||
| 736 | { |
||
| 737 | "data": { |
||
| 738 | "text/plain": [ |
||
| 739 | "\n", |
||
| 740 | " Dermis Epidermis \n", |
||
| 741 | " 21975 20628 " |
||
| 742 | ] |
||
| 743 | }, |
||
| 744 | "metadata": {}, |
||
| 745 | "output_type": "display_data" |
||
| 746 | } |
||
| 747 | ], |
||
| 748 | "source": [ |
||
| 749 | "table(Target_subset$donor); table(Target_subset$tissue)" |
||
| 750 | ] |
||
| 751 | }, |
||
| 752 | { |
||
| 753 | "cell_type": "code", |
||
| 754 | "execution_count": 5, |
||
| 755 | "id": "western-router", |
||
| 756 | "metadata": {}, |
||
| 757 | "outputs": [ |
||
| 758 | { |
||
| 759 | "data": { |
||
| 760 | "text/plain": [ |
||
| 761 | "\n", |
||
| 762 | " CTCL1_Dermis CTCL1_Epidermis CTCL2_Dermis CTCL2_Epidermis CTCL3_Dermis \n", |
||
| 763 | " 5332 2737 710 1147 6504 \n", |
||
| 764 | "CTCL3_Epidermis CTCL4_Dermis CTCL4_Epidermis CTCL5_Dermis CTCL5_Epidermis \n", |
||
| 765 | " 2907 67 1006 2459 5177 \n", |
||
| 766 | " CTCL6_Dermis CTCL6_Epidermis CTCL7_Dermis CTCL7_Epidermis CTCL8_Dermis \n", |
||
| 767 | " 403 3342 544 3129 5956 \n", |
||
| 768 | "CTCL8_Epidermis \n", |
||
| 769 | " 1183 " |
||
| 770 | ] |
||
| 771 | }, |
||
| 772 | "metadata": {}, |
||
| 773 | "output_type": "display_data" |
||
| 774 | } |
||
| 775 | ], |
||
| 776 | "source": [ |
||
| 777 | "Target_subset$donor_tissue <- paste(Target_subset$donor, Target_subset$tissue, sep='_')\n", |
||
| 778 | "table(Target_subset$donor_tissue)" |
||
| 779 | ] |
||
| 780 | }, |
||
| 781 | { |
||
| 782 | "cell_type": "code", |
||
| 783 | "execution_count": 8, |
||
| 784 | "id": "optical-spanking", |
||
| 785 | "metadata": {}, |
||
| 786 | "outputs": [ |
||
| 787 | { |
||
| 788 | "data": { |
||
| 789 | "text/html": [ |
||
| 790 | "<style>\n", |
||
| 791 | ".list-inline {list-style: none; margin:0; padding: 0}\n", |
||
| 792 | ".list-inline>li {display: inline-block}\n", |
||
| 793 | ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", |
||
| 794 | "</style>\n", |
||
| 795 | "<ol class=list-inline><li>15790</li><li>16</li></ol>\n" |
||
| 796 | ], |
||
| 797 | "text/latex": [ |
||
| 798 | "\\begin{enumerate*}\n", |
||
| 799 | "\\item 15790\n", |
||
| 800 | "\\item 16\n", |
||
| 801 | "\\end{enumerate*}\n" |
||
| 802 | ], |
||
| 803 | "text/markdown": [ |
||
| 804 | "1. 15790\n", |
||
| 805 | "2. 16\n", |
||
| 806 | "\n", |
||
| 807 | "\n" |
||
| 808 | ], |
||
| 809 | "text/plain": [ |
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| 810 | "[1] 15790 16" |
||
| 811 | ] |
||
| 812 | }, |
||
| 813 | "metadata": {}, |
||
| 814 | "output_type": "display_data" |
||
| 815 | } |
||
| 816 | ], |
||
| 817 | "source": [ |
||
| 818 | "mat <- c()\n", |
||
| 819 | "coln <- c()\n", |
||
| 820 | "for (name in as.vector(sort(unique(Target_subset$donor_tissue)))) {\n", |
||
| 821 | " pp <- pseudobulk(Target_subset, 'donor_tissue', name)\n", |
||
| 822 | " mat <- cbind(mat, pp)\n", |
||
| 823 | " coln <- c(coln, name)\n", |
||
| 824 | "} \n", |
||
| 825 | "colnames(mat) <- coln\n", |
||
| 826 | "rownames(mat) <- rownames(Target_subset@assays$RNA@counts)\n", |
||
| 827 | "#mat <- mat[VariableFeatures(object = Target_subset), ]\n", |
||
| 828 | "dim(mat)" |
||
| 829 | ] |
||
| 830 | }, |
||
| 831 | { |
||
| 832 | "cell_type": "code", |
||
| 833 | "execution_count": 9, |
||
| 834 | "id": "concrete-state", |
||
| 835 | "metadata": {}, |
||
| 836 | "outputs": [ |
||
| 837 | { |
||
| 838 | "data": { |
||
| 839 | "text/html": [ |
||
| 840 | "<table class=\"dataframe\">\n", |
||
| 841 | "<caption>A matrix: 6 × 16 of type dbl</caption>\n", |
||
| 842 | "<thead>\n", |
||
| 843 | "\t<tr><th></th><th scope=col>CTCL1_Dermis</th><th scope=col>CTCL1_Epidermis</th><th scope=col>CTCL2_Dermis</th><th scope=col>CTCL2_Epidermis</th><th scope=col>CTCL3_Dermis</th><th scope=col>CTCL3_Epidermis</th><th scope=col>CTCL4_Dermis</th><th scope=col>CTCL4_Epidermis</th><th scope=col>CTCL5_Dermis</th><th scope=col>CTCL5_Epidermis</th><th scope=col>CTCL6_Dermis</th><th scope=col>CTCL6_Epidermis</th><th scope=col>CTCL7_Dermis</th><th scope=col>CTCL7_Epidermis</th><th scope=col>CTCL8_Dermis</th><th scope=col>CTCL8_Epidermis</th></tr>\n", |
||
| 844 | "</thead>\n", |
||
| 845 | "<tbody>\n", |
||
| 846 | "\t<tr><th scope=row>SAMD11</th><td> 6</td><td> 2</td><td> 0</td><td> 0</td><td> 5</td><td> 3</td><td> 0</td><td> 1</td><td> 1</td><td> 12</td><td> 0</td><td>1022</td><td> 0</td><td> 0</td><td> 2</td><td> 0</td></tr>\n", |
||
| 847 | "\t<tr><th scope=row>NOC2L</th><td>1811</td><td> 664</td><td>308</td><td>366</td><td> 4705</td><td>1334</td><td>10</td><td>213</td><td> 507</td><td> 1744</td><td>162</td><td> 692</td><td> 67</td><td> 243</td><td>1175</td><td> 234</td></tr>\n", |
||
| 848 | "\t<tr><th scope=row>KLHL17</th><td> 42</td><td> 21</td><td> 10</td><td> 15</td><td> 102</td><td> 37</td><td> 0</td><td> 17</td><td> 41</td><td> 109</td><td> 28</td><td> 80</td><td> 3</td><td> 44</td><td> 27</td><td> 4</td></tr>\n", |
||
| 849 | "\t<tr><th scope=row>PLEKHN1</th><td> 0</td><td> 0</td><td> 18</td><td> 5</td><td> 61</td><td> 0</td><td> 2</td><td> 24</td><td> 49</td><td> 156</td><td> 25</td><td> 83</td><td> 15</td><td> 129</td><td> 39</td><td> 10</td></tr>\n", |
||
| 850 | "\t<tr><th scope=row>HES4</th><td> 25</td><td> 59</td><td> 19</td><td> 51</td><td> 39</td><td> 1</td><td> 0</td><td> 1</td><td> 16</td><td> 227</td><td> 0</td><td> 11</td><td> 5</td><td> 89</td><td> 109</td><td> 6</td></tr>\n", |
||
| 851 | "\t<tr><th scope=row>ISG15</th><td>3145</td><td>1711</td><td>988</td><td>992</td><td>10230</td><td>4524</td><td>20</td><td>399</td><td>4889</td><td>15071</td><td>526</td><td>6740</td><td>157</td><td>2804</td><td>3799</td><td>1086</td></tr>\n", |
||
| 852 | "</tbody>\n", |
||
| 853 | "</table>\n" |
||
| 854 | ], |
||
| 855 | "text/latex": [ |
||
| 856 | "A matrix: 6 × 16 of type dbl\n", |
||
| 857 | "\\begin{tabular}{r|llllllllllllllll}\n", |
||
| 858 | " & CTCL1\\_Dermis & CTCL1\\_Epidermis & CTCL2\\_Dermis & CTCL2\\_Epidermis & CTCL3\\_Dermis & CTCL3\\_Epidermis & CTCL4\\_Dermis & CTCL4\\_Epidermis & CTCL5\\_Dermis & CTCL5\\_Epidermis & CTCL6\\_Dermis & CTCL6\\_Epidermis & CTCL7\\_Dermis & CTCL7\\_Epidermis & CTCL8\\_Dermis & CTCL8\\_Epidermis\\\\\n", |
||
| 859 | "\\hline\n", |
||
| 860 | "\tSAMD11 & 6 & 2 & 0 & 0 & 5 & 3 & 0 & 1 & 1 & 12 & 0 & 1022 & 0 & 0 & 2 & 0\\\\\n", |
||
| 861 | "\tNOC2L & 1811 & 664 & 308 & 366 & 4705 & 1334 & 10 & 213 & 507 & 1744 & 162 & 692 & 67 & 243 & 1175 & 234\\\\\n", |
||
| 862 | "\tKLHL17 & 42 & 21 & 10 & 15 & 102 & 37 & 0 & 17 & 41 & 109 & 28 & 80 & 3 & 44 & 27 & 4\\\\\n", |
||
| 863 | "\tPLEKHN1 & 0 & 0 & 18 & 5 & 61 & 0 & 2 & 24 & 49 & 156 & 25 & 83 & 15 & 129 & 39 & 10\\\\\n", |
||
| 864 | "\tHES4 & 25 & 59 & 19 & 51 & 39 & 1 & 0 & 1 & 16 & 227 & 0 & 11 & 5 & 89 & 109 & 6\\\\\n", |
||
| 865 | "\tISG15 & 3145 & 1711 & 988 & 992 & 10230 & 4524 & 20 & 399 & 4889 & 15071 & 526 & 6740 & 157 & 2804 & 3799 & 1086\\\\\n", |
||
| 866 | "\\end{tabular}\n" |
||
| 867 | ], |
||
| 868 | "text/markdown": [ |
||
| 869 | "\n", |
||
| 870 | "A matrix: 6 × 16 of type dbl\n", |
||
| 871 | "\n", |
||
| 872 | "| <!--/--> | CTCL1_Dermis | CTCL1_Epidermis | CTCL2_Dermis | CTCL2_Epidermis | CTCL3_Dermis | CTCL3_Epidermis | CTCL4_Dermis | CTCL4_Epidermis | CTCL5_Dermis | CTCL5_Epidermis | CTCL6_Dermis | CTCL6_Epidermis | CTCL7_Dermis | CTCL7_Epidermis | CTCL8_Dermis | CTCL8_Epidermis |\n", |
||
| 873 | "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", |
||
| 874 | "| SAMD11 | 6 | 2 | 0 | 0 | 5 | 3 | 0 | 1 | 1 | 12 | 0 | 1022 | 0 | 0 | 2 | 0 |\n", |
||
| 875 | "| NOC2L | 1811 | 664 | 308 | 366 | 4705 | 1334 | 10 | 213 | 507 | 1744 | 162 | 692 | 67 | 243 | 1175 | 234 |\n", |
||
| 876 | "| KLHL17 | 42 | 21 | 10 | 15 | 102 | 37 | 0 | 17 | 41 | 109 | 28 | 80 | 3 | 44 | 27 | 4 |\n", |
||
| 877 | "| PLEKHN1 | 0 | 0 | 18 | 5 | 61 | 0 | 2 | 24 | 49 | 156 | 25 | 83 | 15 | 129 | 39 | 10 |\n", |
||
| 878 | "| HES4 | 25 | 59 | 19 | 51 | 39 | 1 | 0 | 1 | 16 | 227 | 0 | 11 | 5 | 89 | 109 | 6 |\n", |
||
| 879 | "| ISG15 | 3145 | 1711 | 988 | 992 | 10230 | 4524 | 20 | 399 | 4889 | 15071 | 526 | 6740 | 157 | 2804 | 3799 | 1086 |\n", |
||
| 880 | "\n" |
||
| 881 | ], |
||
| 882 | "text/plain": [ |
||
| 883 | " CTCL1_Dermis CTCL1_Epidermis CTCL2_Dermis CTCL2_Epidermis CTCL3_Dermis\n", |
||
| 884 | "SAMD11 6 2 0 0 5 \n", |
||
| 885 | "NOC2L 1811 664 308 366 4705 \n", |
||
| 886 | "KLHL17 42 21 10 15 102 \n", |
||
| 887 | "PLEKHN1 0 0 18 5 61 \n", |
||
| 888 | "HES4 25 59 19 51 39 \n", |
||
| 889 | "ISG15 3145 1711 988 992 10230 \n", |
||
| 890 | " CTCL3_Epidermis CTCL4_Dermis CTCL4_Epidermis CTCL5_Dermis\n", |
||
| 891 | "SAMD11 3 0 1 1 \n", |
||
| 892 | "NOC2L 1334 10 213 507 \n", |
||
| 893 | "KLHL17 37 0 17 41 \n", |
||
| 894 | "PLEKHN1 0 2 24 49 \n", |
||
| 895 | "HES4 1 0 1 16 \n", |
||
| 896 | "ISG15 4524 20 399 4889 \n", |
||
| 897 | " CTCL5_Epidermis CTCL6_Dermis CTCL6_Epidermis CTCL7_Dermis\n", |
||
| 898 | "SAMD11 12 0 1022 0 \n", |
||
| 899 | "NOC2L 1744 162 692 67 \n", |
||
| 900 | "KLHL17 109 28 80 3 \n", |
||
| 901 | "PLEKHN1 156 25 83 15 \n", |
||
| 902 | "HES4 227 0 11 5 \n", |
||
| 903 | "ISG15 15071 526 6740 157 \n", |
||
| 904 | " CTCL7_Epidermis CTCL8_Dermis CTCL8_Epidermis\n", |
||
| 905 | "SAMD11 0 2 0 \n", |
||
| 906 | "NOC2L 243 1175 234 \n", |
||
| 907 | "KLHL17 44 27 4 \n", |
||
| 908 | "PLEKHN1 129 39 10 \n", |
||
| 909 | "HES4 89 109 6 \n", |
||
| 910 | "ISG15 2804 3799 1086 " |
||
| 911 | ] |
||
| 912 | }, |
||
| 913 | "metadata": {}, |
||
| 914 | "output_type": "display_data" |
||
| 915 | } |
||
| 916 | ], |
||
| 917 | "source": [ |
||
| 918 | "head(mat)" |
||
| 919 | ] |
||
| 920 | }, |
||
| 921 | { |
||
| 922 | "cell_type": "code", |
||
| 923 | "execution_count": 10, |
||
| 924 | "id": "smaller-oxide", |
||
| 925 | "metadata": {}, |
||
| 926 | "outputs": [ |
||
| 927 | { |
||
| 928 | "data": { |
||
| 929 | "text/html": [ |
||
| 930 | "<table class=\"dataframe\">\n", |
||
| 931 | "<caption>A data.frame: 16 × 3</caption>\n", |
||
| 932 | "<thead>\n", |
||
| 933 | "\t<tr><th scope=col>Sample</th><th scope=col>patient</th><th scope=col>tissue</th></tr>\n", |
||
| 934 | "\t<tr><th scope=col><chr></th><th scope=col><fct></th><th scope=col><fct></th></tr>\n", |
||
| 935 | "</thead>\n", |
||
| 936 | "<tbody>\n", |
||
| 937 | "\t<tr><td>CTCL1_Dermis </td><td>CTCL1</td><td>Dermis </td></tr>\n", |
||
| 938 | "\t<tr><td>CTCL1_Epidermis</td><td>CTCL1</td><td>Epidermis</td></tr>\n", |
||
| 939 | "\t<tr><td>CTCL2_Dermis </td><td>CTCL2</td><td>Dermis </td></tr>\n", |
||
| 940 | "\t<tr><td>CTCL2_Epidermis</td><td>CTCL2</td><td>Epidermis</td></tr>\n", |
||
| 941 | "\t<tr><td>CTCL3_Dermis </td><td>CTCL3</td><td>Dermis </td></tr>\n", |
||
| 942 | "\t<tr><td>CTCL3_Epidermis</td><td>CTCL3</td><td>Epidermis</td></tr>\n", |
||
| 943 | "\t<tr><td>CTCL4_Dermis </td><td>CTCL4</td><td>Dermis </td></tr>\n", |
||
| 944 | "\t<tr><td>CTCL4_Epidermis</td><td>CTCL4</td><td>Epidermis</td></tr>\n", |
||
| 945 | "\t<tr><td>CTCL5_Dermis </td><td>CTCL5</td><td>Dermis </td></tr>\n", |
||
| 946 | "\t<tr><td>CTCL5_Epidermis</td><td>CTCL5</td><td>Epidermis</td></tr>\n", |
||
| 947 | "\t<tr><td>CTCL6_Dermis </td><td>CTCL6</td><td>Dermis </td></tr>\n", |
||
| 948 | "\t<tr><td>CTCL6_Epidermis</td><td>CTCL6</td><td>Epidermis</td></tr>\n", |
||
| 949 | "\t<tr><td>CTCL7_Dermis </td><td>CTCL7</td><td>Dermis </td></tr>\n", |
||
| 950 | "\t<tr><td>CTCL7_Epidermis</td><td>CTCL7</td><td>Epidermis</td></tr>\n", |
||
| 951 | "\t<tr><td>CTCL8_Dermis </td><td>CTCL8</td><td>Dermis </td></tr>\n", |
||
| 952 | "\t<tr><td>CTCL8_Epidermis</td><td>CTCL8</td><td>Epidermis</td></tr>\n", |
||
| 953 | "</tbody>\n", |
||
| 954 | "</table>\n" |
||
| 955 | ], |
||
| 956 | "text/latex": [ |
||
| 957 | "A data.frame: 16 × 3\n", |
||
| 958 | "\\begin{tabular}{lll}\n", |
||
| 959 | " Sample & patient & tissue\\\\\n", |
||
| 960 | " <chr> & <fct> & <fct>\\\\\n", |
||
| 961 | "\\hline\n", |
||
| 962 | "\t CTCL1\\_Dermis & CTCL1 & Dermis \\\\\n", |
||
| 963 | "\t CTCL1\\_Epidermis & CTCL1 & Epidermis\\\\\n", |
||
| 964 | "\t CTCL2\\_Dermis & CTCL2 & Dermis \\\\\n", |
||
| 965 | "\t CTCL2\\_Epidermis & CTCL2 & Epidermis\\\\\n", |
||
| 966 | "\t CTCL3\\_Dermis & CTCL3 & Dermis \\\\\n", |
||
| 967 | "\t CTCL3\\_Epidermis & CTCL3 & Epidermis\\\\\n", |
||
| 968 | "\t CTCL4\\_Dermis & CTCL4 & Dermis \\\\\n", |
||
| 969 | "\t CTCL4\\_Epidermis & CTCL4 & Epidermis\\\\\n", |
||
| 970 | "\t CTCL5\\_Dermis & CTCL5 & Dermis \\\\\n", |
||
| 971 | "\t CTCL5\\_Epidermis & CTCL5 & Epidermis\\\\\n", |
||
| 972 | "\t CTCL6\\_Dermis & CTCL6 & Dermis \\\\\n", |
||
| 973 | "\t CTCL6\\_Epidermis & CTCL6 & Epidermis\\\\\n", |
||
| 974 | "\t CTCL7\\_Dermis & CTCL7 & Dermis \\\\\n", |
||
| 975 | "\t CTCL7\\_Epidermis & CTCL7 & Epidermis\\\\\n", |
||
| 976 | "\t CTCL8\\_Dermis & CTCL8 & Dermis \\\\\n", |
||
| 977 | "\t CTCL8\\_Epidermis & CTCL8 & Epidermis\\\\\n", |
||
| 978 | "\\end{tabular}\n" |
||
| 979 | ], |
||
| 980 | "text/markdown": [ |
||
| 981 | "\n", |
||
| 982 | "A data.frame: 16 × 3\n", |
||
| 983 | "\n", |
||
| 984 | "| Sample <chr> | patient <fct> | tissue <fct> |\n", |
||
| 985 | "|---|---|---|\n", |
||
| 986 | "| CTCL1_Dermis | CTCL1 | Dermis |\n", |
||
| 987 | "| CTCL1_Epidermis | CTCL1 | Epidermis |\n", |
||
| 988 | "| CTCL2_Dermis | CTCL2 | Dermis |\n", |
||
| 989 | "| CTCL2_Epidermis | CTCL2 | Epidermis |\n", |
||
| 990 | "| CTCL3_Dermis | CTCL3 | Dermis |\n", |
||
| 991 | "| CTCL3_Epidermis | CTCL3 | Epidermis |\n", |
||
| 992 | "| CTCL4_Dermis | CTCL4 | Dermis |\n", |
||
| 993 | "| CTCL4_Epidermis | CTCL4 | Epidermis |\n", |
||
| 994 | "| CTCL5_Dermis | CTCL5 | Dermis |\n", |
||
| 995 | "| CTCL5_Epidermis | CTCL5 | Epidermis |\n", |
||
| 996 | "| CTCL6_Dermis | CTCL6 | Dermis |\n", |
||
| 997 | "| CTCL6_Epidermis | CTCL6 | Epidermis |\n", |
||
| 998 | "| CTCL7_Dermis | CTCL7 | Dermis |\n", |
||
| 999 | "| CTCL7_Epidermis | CTCL7 | Epidermis |\n", |
||
| 1000 | "| CTCL8_Dermis | CTCL8 | Dermis |\n", |
||
| 1001 | "| CTCL8_Epidermis | CTCL8 | Epidermis |\n", |
||
| 1002 | "\n" |
||
| 1003 | ], |
||
| 1004 | "text/plain": [ |
||
| 1005 | " Sample patient tissue \n", |
||
| 1006 | "1 CTCL1_Dermis CTCL1 Dermis \n", |
||
| 1007 | "2 CTCL1_Epidermis CTCL1 Epidermis\n", |
||
| 1008 | "3 CTCL2_Dermis CTCL2 Dermis \n", |
||
| 1009 | "4 CTCL2_Epidermis CTCL2 Epidermis\n", |
||
| 1010 | "5 CTCL3_Dermis CTCL3 Dermis \n", |
||
| 1011 | "6 CTCL3_Epidermis CTCL3 Epidermis\n", |
||
| 1012 | "7 CTCL4_Dermis CTCL4 Dermis \n", |
||
| 1013 | "8 CTCL4_Epidermis CTCL4 Epidermis\n", |
||
| 1014 | "9 CTCL5_Dermis CTCL5 Dermis \n", |
||
| 1015 | "10 CTCL5_Epidermis CTCL5 Epidermis\n", |
||
| 1016 | "11 CTCL6_Dermis CTCL6 Dermis \n", |
||
| 1017 | "12 CTCL6_Epidermis CTCL6 Epidermis\n", |
||
| 1018 | "13 CTCL7_Dermis CTCL7 Dermis \n", |
||
| 1019 | "14 CTCL7_Epidermis CTCL7 Epidermis\n", |
||
| 1020 | "15 CTCL8_Dermis CTCL8 Dermis \n", |
||
| 1021 | "16 CTCL8_Epidermis CTCL8 Epidermis" |
||
| 1022 | ] |
||
| 1023 | }, |
||
| 1024 | "metadata": {}, |
||
| 1025 | "output_type": "display_data" |
||
| 1026 | } |
||
| 1027 | ], |
||
| 1028 | "source": [ |
||
| 1029 | "patient <- factor(c('CTCL1','CTCL1','CTCL2','CTCL2',\n", |
||
| 1030 | " 'CTCL3','CTCL3','CTCL4','CTCL4',\n", |
||
| 1031 | " 'CTCL5','CTCL5','CTCL6','CTCL6',\n", |
||
| 1032 | " 'CTCL7','CTCL7','CTCL8','CTCL8'))\n", |
||
| 1033 | "tissue <- factor(rep(c('Dermis', 'Epidermis'),8))\n", |
||
| 1034 | "data.frame(Sample=colnames(mat),patient,tissue)" |
||
| 1035 | ] |
||
| 1036 | }, |
||
| 1037 | { |
||
| 1038 | "cell_type": "code", |
||
| 1039 | "execution_count": 12, |
||
| 1040 | "id": "above-douglas", |
||
| 1041 | "metadata": {}, |
||
| 1042 | "outputs": [ |
||
| 1043 | { |
||
| 1044 | "name": "stderr", |
||
| 1045 | "output_type": "stream", |
||
| 1046 | "text": [ |
||
| 1047 | "Warning message in filterByExpr.DGEList(DEGs, min.count = 2, min.total.count = 10):\n", |
||
| 1048 | "“All samples appear to belong to the same group.”\n" |
||
| 1049 | ] |
||
| 1050 | }, |
||
| 1051 | { |
||
| 1052 | "data": { |
||
| 1053 | "text/html": [ |
||
| 1054 | "<table class=\"dataframe\">\n", |
||
| 1055 | "<caption>A matrix: 16 × 9 of type dbl</caption>\n", |
||
| 1056 | "<thead>\n", |
||
| 1057 | "\t<tr><th></th><th scope=col>(Intercept)</th><th scope=col>patientCTCL2</th><th scope=col>patientCTCL3</th><th scope=col>patientCTCL4</th><th scope=col>patientCTCL5</th><th scope=col>patientCTCL6</th><th scope=col>patientCTCL7</th><th scope=col>patientCTCL8</th><th scope=col>tissueEpidermis</th></tr>\n", |
||
| 1058 | "</thead>\n", |
||
| 1059 | "<tbody>\n", |
||
| 1060 | "\t<tr><th scope=row>1</th><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", |
||
| 1061 | "\t<tr><th scope=row>2</th><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n", |
||
| 1062 | "\t<tr><th scope=row>3</th><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", |
||
| 1063 | "\t<tr><th scope=row>4</th><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n", |
||
| 1064 | "\t<tr><th scope=row>5</th><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", |
||
| 1065 | "\t<tr><th scope=row>6</th><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n", |
||
| 1066 | "\t<tr><th scope=row>7</th><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", |
||
| 1067 | "\t<tr><th scope=row>8</th><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n", |
||
| 1068 | "\t<tr><th scope=row>9</th><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", |
||
| 1069 | "\t<tr><th scope=row>10</th><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n", |
||
| 1070 | "\t<tr><th scope=row>11</th><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n", |
||
| 1071 | "\t<tr><th scope=row>12</th><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n", |
||
| 1072 | "\t<tr><th scope=row>13</th><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n", |
||
| 1073 | "\t<tr><th scope=row>14</th><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td></tr>\n", |
||
| 1074 | "\t<tr><th scope=row>15</th><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n", |
||
| 1075 | "\t<tr><th scope=row>16</th><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td></tr>\n", |
||
| 1076 | "</tbody>\n", |
||
| 1077 | "</table>\n" |
||
| 1078 | ], |
||
| 1079 | "text/latex": [ |
||
| 1080 | "A matrix: 16 × 9 of type dbl\n", |
||
| 1081 | "\\begin{tabular}{r|lllllllll}\n", |
||
| 1082 | " & (Intercept) & patientCTCL2 & patientCTCL3 & patientCTCL4 & patientCTCL5 & patientCTCL6 & patientCTCL7 & patientCTCL8 & tissueEpidermis\\\\\n", |
||
| 1083 | "\\hline\n", |
||
| 1084 | "\t1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", |
||
| 1085 | "\t2 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n", |
||
| 1086 | "\t3 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", |
||
| 1087 | "\t4 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n", |
||
| 1088 | "\t5 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", |
||
| 1089 | "\t6 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n", |
||
| 1090 | "\t7 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n", |
||
| 1091 | "\t8 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1\\\\\n", |
||
| 1092 | "\t9 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0\\\\\n", |
||
| 1093 | "\t10 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1\\\\\n", |
||
| 1094 | "\t11 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0\\\\\n", |
||
| 1095 | "\t12 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1\\\\\n", |
||
| 1096 | "\t13 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0\\\\\n", |
||
| 1097 | "\t14 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1\\\\\n", |
||
| 1098 | "\t15 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\\\\\n", |
||
| 1099 | "\t16 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1\\\\\n", |
||
| 1100 | "\\end{tabular}\n" |
||
| 1101 | ], |
||
| 1102 | "text/markdown": [ |
||
| 1103 | "\n", |
||
| 1104 | "A matrix: 16 × 9 of type dbl\n", |
||
| 1105 | "\n", |
||
| 1106 | "| <!--/--> | (Intercept) | patientCTCL2 | patientCTCL3 | patientCTCL4 | patientCTCL5 | patientCTCL6 | patientCTCL7 | patientCTCL8 | tissueEpidermis |\n", |
||
| 1107 | "|---|---|---|---|---|---|---|---|---|---|\n", |
||
| 1108 | "| 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", |
||
| 1109 | "| 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n", |
||
| 1110 | "| 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", |
||
| 1111 | "| 4 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n", |
||
| 1112 | "| 5 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |\n", |
||
| 1113 | "| 6 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |\n", |
||
| 1114 | "| 7 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n", |
||
| 1115 | "| 8 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |\n", |
||
| 1116 | "| 9 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |\n", |
||
| 1117 | "| 10 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |\n", |
||
| 1118 | "| 11 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |\n", |
||
| 1119 | "| 12 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |\n", |
||
| 1120 | "| 13 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |\n", |
||
| 1121 | "| 14 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n", |
||
| 1122 | "| 15 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |\n", |
||
| 1123 | "| 16 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |\n", |
||
| 1124 | "\n" |
||
| 1125 | ], |
||
| 1126 | "text/plain": [ |
||
| 1127 | " (Intercept) patientCTCL2 patientCTCL3 patientCTCL4 patientCTCL5 patientCTCL6\n", |
||
| 1128 | "1 1 0 0 0 0 0 \n", |
||
| 1129 | "2 1 0 0 0 0 0 \n", |
||
| 1130 | "3 1 1 0 0 0 0 \n", |
||
| 1131 | "4 1 1 0 0 0 0 \n", |
||
| 1132 | "5 1 0 1 0 0 0 \n", |
||
| 1133 | "6 1 0 1 0 0 0 \n", |
||
| 1134 | "7 1 0 0 1 0 0 \n", |
||
| 1135 | "8 1 0 0 1 0 0 \n", |
||
| 1136 | "9 1 0 0 0 1 0 \n", |
||
| 1137 | "10 1 0 0 0 1 0 \n", |
||
| 1138 | "11 1 0 0 0 0 1 \n", |
||
| 1139 | "12 1 0 0 0 0 1 \n", |
||
| 1140 | "13 1 0 0 0 0 0 \n", |
||
| 1141 | "14 1 0 0 0 0 0 \n", |
||
| 1142 | "15 1 0 0 0 0 0 \n", |
||
| 1143 | "16 1 0 0 0 0 0 \n", |
||
| 1144 | " patientCTCL7 patientCTCL8 tissueEpidermis\n", |
||
| 1145 | "1 0 0 0 \n", |
||
| 1146 | "2 0 0 1 \n", |
||
| 1147 | "3 0 0 0 \n", |
||
| 1148 | "4 0 0 1 \n", |
||
| 1149 | "5 0 0 0 \n", |
||
| 1150 | "6 0 0 1 \n", |
||
| 1151 | "7 0 0 0 \n", |
||
| 1152 | "8 0 0 1 \n", |
||
| 1153 | "9 0 0 0 \n", |
||
| 1154 | "10 0 0 1 \n", |
||
| 1155 | "11 0 0 0 \n", |
||
| 1156 | "12 0 0 1 \n", |
||
| 1157 | "13 1 0 0 \n", |
||
| 1158 | "14 1 0 1 \n", |
||
| 1159 | "15 0 1 0 \n", |
||
| 1160 | "16 0 1 1 " |
||
| 1161 | ] |
||
| 1162 | }, |
||
| 1163 | "metadata": {}, |
||
| 1164 | "output_type": "display_data" |
||
| 1165 | } |
||
| 1166 | ], |
||
| 1167 | "source": [ |
||
| 1168 | "#########edgeR#########\n", |
||
| 1169 | "DEGs <- DGEList(counts=mat)\n", |
||
| 1170 | "\n", |
||
| 1171 | "keep <- filterByExpr(DEGs, min.count=2, min.total.count=10)\n", |
||
| 1172 | "#keep <- filterByExpr(DEGs)\n", |
||
| 1173 | "DEGs <- DEGs[keep, , keep.lib.sizes=FALSE]\n", |
||
| 1174 | "\n", |
||
| 1175 | "DEGs <- calcNormFactors(DEGs)\n", |
||
| 1176 | "\n", |
||
| 1177 | "#cdr <- scale(colMeans(mat_sub > 0))\n", |
||
| 1178 | "design <- model.matrix(~patient+tissue)\n", |
||
| 1179 | "#design <- model.matrix(~groups)\n", |
||
| 1180 | "design" |
||
| 1181 | ] |
||
| 1182 | }, |
||
| 1183 | { |
||
| 1184 | "cell_type": "code", |
||
| 1185 | "execution_count": 13, |
||
| 1186 | "id": "comprehensive-bacon", |
||
| 1187 | "metadata": {}, |
||
| 1188 | "outputs": [ |
||
| 1189 | { |
||
| 1190 | "data": { |
||
| 1191 | "text/html": [ |
||
| 1192 | "0.0640131587931858" |
||
| 1193 | ], |
||
| 1194 | "text/latex": [ |
||
| 1195 | "0.0640131587931858" |
||
| 1196 | ], |
||
| 1197 | "text/markdown": [ |
||
| 1198 | "0.0640131587931858" |
||
| 1199 | ], |
||
| 1200 | "text/plain": [ |
||
| 1201 | "[1] 0.06401316" |
||
| 1202 | ] |
||
| 1203 | }, |
||
| 1204 | "metadata": {}, |
||
| 1205 | "output_type": "display_data" |
||
| 1206 | }, |
||
| 1207 | { |
||
| 1208 | "data": { |
||
| 1209 | "text/plain": [ |
||
| 1210 | " tissueEpidermis\n", |
||
| 1211 | "Down 3\n", |
||
| 1212 | "NotSig 9107\n", |
||
| 1213 | "Up 4" |
||
| 1214 | ] |
||
| 1215 | }, |
||
| 1216 | "metadata": {}, |
||
| 1217 | "output_type": "display_data" |
||
| 1218 | }, |
||
| 1219 | { |
||
| 1220 | "data": { |
||
| 1221 | "image/png": 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txvKrvR4WOWLFkihDh79qzUQQAAJmDX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\nEEJIQ/wfJkghzRab8rwAAAAASUVORK5CYII=", |
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| 1222 | "text/plain": [ |
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| 1223 | "plot without title" |
||
| 1224 | ] |
||
| 1225 | }, |
||
| 1226 | "metadata": { |
||
| 1227 | "image/png": { |
||
| 1228 | "height": 420, |
||
| 1229 | "width": 420 |
||
| 1230 | } |
||
| 1231 | }, |
||
| 1232 | "output_type": "display_data" |
||
| 1233 | } |
||
| 1234 | ], |
||
| 1235 | "source": [ |
||
| 1236 | "DEGs <- estimateDisp(DEGs, design = design)\n", |
||
| 1237 | "DEGs$common.dispersion\n", |
||
| 1238 | "plotBCV(DEGs)\n", |
||
| 1239 | "\n", |
||
| 1240 | "fit <- glmQLFit(DEGs, design = design)\n", |
||
| 1241 | "qlf <- glmQLFTest(fit)\n", |
||
| 1242 | "tt <- topTags(qlf, n = Inf)\n", |
||
| 1243 | "summary(decideTests(qlf))\n", |
||
| 1244 | "\n", |
||
| 1245 | "#et <- exactTest(DEGs)\n", |
||
| 1246 | "#et_FDR <- topTags(et, n = nrow(et$table), adjust.method = \"BH\", sort.by = \"PValue\", p.value = 1)\n", |
||
| 1247 | "\n", |
||
| 1248 | "#out_temp <- et_FDR$table\n", |
||
| 1249 | "#colnames(out_temp) <-paste(\"AD1.1_canonical\",colnames(out_temp),sep=\"_\")" |
||
| 1250 | ] |
||
| 1251 | }, |
||
| 1252 | { |
||
| 1253 | "cell_type": "code", |
||
| 1254 | "execution_count": 14, |
||
| 1255 | "id": "automated-reporter", |
||
| 1256 | "metadata": {}, |
||
| 1257 | "outputs": [ |
||
| 1258 | { |
||
| 1259 | "data": { |
||
| 1260 | "text/html": [ |
||
| 1261 | "<dl>\n", |
||
| 1262 | "\t<dt>$table</dt>\n", |
||
| 1263 | "\t\t<dd><table class=\"dataframe\">\n", |
||
| 1264 | "<caption>A data.frame: 6 × 5</caption>\n", |
||
| 1265 | "<thead>\n", |
||
| 1266 | "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n", |
||
| 1267 | "\t<tr><th></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n", |
||
| 1268 | "</thead>\n", |
||
| 1269 | "<tbody>\n", |
||
| 1270 | "\t<tr><th scope=row>NQO1</th><td> 2.6492180</td><td>6.314072</td><td>67.37661</td><td>9.401806e-07</td><td>0.008568806</td></tr>\n", |
||
| 1271 | "\t<tr><th scope=row>LIME1</th><td>-1.3123583</td><td>6.346188</td><td>48.42083</td><td>6.290526e-06</td><td>0.028665929</td></tr>\n", |
||
| 1272 | "\t<tr><th scope=row>SRXN1</th><td> 1.6014705</td><td>3.981995</td><td>41.34632</td><td>1.494247e-05</td><td>0.031568681</td></tr>\n", |
||
| 1273 | "\t<tr><th scope=row>KRT7</th><td> 1.4117856</td><td>7.959997</td><td>40.57821</td><td>1.652496e-05</td><td>0.031568681</td></tr>\n", |
||
| 1274 | "\t<tr><th scope=row>KLF2</th><td>-2.6863484</td><td>5.238607</td><td>39.82727</td><td>1.825974e-05</td><td>0.031568681</td></tr>\n", |
||
| 1275 | "\t<tr><th scope=row>JAK3</th><td>-0.8956571</td><td>6.413298</td><td>38.86949</td><td>2.078254e-05</td><td>0.031568681</td></tr>\n", |
||
| 1276 | "</tbody>\n", |
||
| 1277 | "</table>\n", |
||
| 1278 | "</dd>\n", |
||
| 1279 | "\t<dt>$adjust.method</dt>\n", |
||
| 1280 | "\t\t<dd>'BH'</dd>\n", |
||
| 1281 | "\t<dt>$comparison</dt>\n", |
||
| 1282 | "\t\t<dd>'tissueEpidermis'</dd>\n", |
||
| 1283 | "\t<dt>$test</dt>\n", |
||
| 1284 | "\t\t<dd>'glm'</dd>\n", |
||
| 1285 | "</dl>\n" |
||
| 1286 | ], |
||
| 1287 | "text/latex": [ |
||
| 1288 | "\\begin{description}\n", |
||
| 1289 | "\\item[\\$table] A data.frame: 6 × 5\n", |
||
| 1290 | "\\begin{tabular}{r|lllll}\n", |
||
| 1291 | " & logFC & logCPM & F & PValue & FDR\\\\\n", |
||
| 1292 | " & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n", |
||
| 1293 | "\\hline\n", |
||
| 1294 | "\tNQO1 & 2.6492180 & 6.314072 & 67.37661 & 9.401806e-07 & 0.008568806\\\\\n", |
||
| 1295 | "\tLIME1 & -1.3123583 & 6.346188 & 48.42083 & 6.290526e-06 & 0.028665929\\\\\n", |
||
| 1296 | "\tSRXN1 & 1.6014705 & 3.981995 & 41.34632 & 1.494247e-05 & 0.031568681\\\\\n", |
||
| 1297 | "\tKRT7 & 1.4117856 & 7.959997 & 40.57821 & 1.652496e-05 & 0.031568681\\\\\n", |
||
| 1298 | "\tKLF2 & -2.6863484 & 5.238607 & 39.82727 & 1.825974e-05 & 0.031568681\\\\\n", |
||
| 1299 | "\tJAK3 & -0.8956571 & 6.413298 & 38.86949 & 2.078254e-05 & 0.031568681\\\\\n", |
||
| 1300 | "\\end{tabular}\n", |
||
| 1301 | "\n", |
||
| 1302 | "\\item[\\$adjust.method] 'BH'\n", |
||
| 1303 | "\\item[\\$comparison] 'tissueEpidermis'\n", |
||
| 1304 | "\\item[\\$test] 'glm'\n", |
||
| 1305 | "\\end{description}\n" |
||
| 1306 | ], |
||
| 1307 | "text/markdown": [ |
||
| 1308 | "$table\n", |
||
| 1309 | ": \n", |
||
| 1310 | "A data.frame: 6 × 5\n", |
||
| 1311 | "\n", |
||
| 1312 | "| <!--/--> | logFC <dbl> | logCPM <dbl> | F <dbl> | PValue <dbl> | FDR <dbl> |\n", |
||
| 1313 | "|---|---|---|---|---|---|\n", |
||
| 1314 | "| NQO1 | 2.6492180 | 6.314072 | 67.37661 | 9.401806e-07 | 0.008568806 |\n", |
||
| 1315 | "| LIME1 | -1.3123583 | 6.346188 | 48.42083 | 6.290526e-06 | 0.028665929 |\n", |
||
| 1316 | "| SRXN1 | 1.6014705 | 3.981995 | 41.34632 | 1.494247e-05 | 0.031568681 |\n", |
||
| 1317 | "| KRT7 | 1.4117856 | 7.959997 | 40.57821 | 1.652496e-05 | 0.031568681 |\n", |
||
| 1318 | "| KLF2 | -2.6863484 | 5.238607 | 39.82727 | 1.825974e-05 | 0.031568681 |\n", |
||
| 1319 | "| JAK3 | -0.8956571 | 6.413298 | 38.86949 | 2.078254e-05 | 0.031568681 |\n", |
||
| 1320 | "\n", |
||
| 1321 | "\n", |
||
| 1322 | "$adjust.method\n", |
||
| 1323 | ": 'BH'\n", |
||
| 1324 | "$comparison\n", |
||
| 1325 | ": 'tissueEpidermis'\n", |
||
| 1326 | "$test\n", |
||
| 1327 | ": 'glm'\n", |
||
| 1328 | "\n", |
||
| 1329 | "\n" |
||
| 1330 | ], |
||
| 1331 | "text/plain": [ |
||
| 1332 | "Coefficient: tissueEpidermis \n", |
||
| 1333 | " logFC logCPM F PValue FDR\n", |
||
| 1334 | "NQO1 2.6492180 6.314072 67.37661 9.401806e-07 0.008568806\n", |
||
| 1335 | "LIME1 -1.3123583 6.346188 48.42083 6.290526e-06 0.028665929\n", |
||
| 1336 | "SRXN1 1.6014705 3.981995 41.34632 1.494247e-05 0.031568681\n", |
||
| 1337 | "KRT7 1.4117856 7.959997 40.57821 1.652496e-05 0.031568681\n", |
||
| 1338 | "KLF2 -2.6863484 5.238607 39.82727 1.825974e-05 0.031568681\n", |
||
| 1339 | "JAK3 -0.8956571 6.413298 38.86949 2.078254e-05 0.031568681" |
||
| 1340 | ] |
||
| 1341 | }, |
||
| 1342 | "metadata": {}, |
||
| 1343 | "output_type": "display_data" |
||
| 1344 | } |
||
| 1345 | ], |
||
| 1346 | "source": [ |
||
| 1347 | "head(tt)" |
||
| 1348 | ] |
||
| 1349 | }, |
||
| 1350 | { |
||
| 1351 | "cell_type": "code", |
||
| 1352 | "execution_count": 15, |
||
| 1353 | "id": "mineral-arrangement", |
||
| 1354 | "metadata": {}, |
||
| 1355 | "outputs": [], |
||
| 1356 | "source": [ |
||
| 1357 | "write.table(tt$table, file='./DEG-skin-layer.xls',quote=F,sep=\"\\t\")" |
||
| 1358 | ] |
||
| 1359 | }, |
||
| 1360 | { |
||
| 1361 | "cell_type": "code", |
||
| 1362 | "execution_count": null, |
||
| 1363 | "id": "mathematical-blackjack", |
||
| 1364 | "metadata": {}, |
||
| 1365 | "outputs": [], |
||
| 1366 | "source": [] |
||
| 1367 | } |
||
| 1368 | ], |
||
| 1369 | "metadata": { |
||
| 1370 | "kernelspec": { |
||
| 1371 | "display_name": "R", |
||
| 1372 | "language": "R", |
||
| 1373 | "name": "ir" |
||
| 1374 | }, |
||
| 1375 | "language_info": { |
||
| 1376 | "codemirror_mode": "r", |
||
| 1377 | "file_extension": ".r", |
||
| 1378 | "mimetype": "text/x-r-source", |
||
| 1379 | "name": "R", |
||
| 1380 | "pygments_lexer": "r", |
||
| 1381 | "version": "4.0.4" |
||
| 1382 | } |
||
| 1383 | }, |
||
| 1384 | "nbformat": 4, |
||
| 1385 | "nbformat_minor": 5 |
||
| 1386 | } |