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comparison galaxy/tools/LC-MSMS/XSeekerPreparator.R @ 11:f4fc4a0f41e2 draft

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" master branch Updating"
author lain
date Thu, 18 Mar 2021 10:46:01 +0000
parents bb9ebd6365ea
children bdb2878ee189
comparison
equal deleted inserted replaced
10:bb9ebd6365ea 11:f4fc4a0f41e2
1 1
2 2
3 TOOL_NAME <- "XSeekerPreparator" 3 TOOL_NAME <- "XSeekerPreparator"
4 VERSION <- "1.1.6" 4 VERSION <- "1.2.0"
5 5
6 OUTPUT_SPECIFIC_TOOL <- "XSeeker_Galaxy" 6 OUTPUT_SPECIFIC_TOOL <- "XSeeker_Galaxy"
7 7
8 ENRICHED_RDATA_VERSION <- paste("1.1.2", OUTPUT_SPECIFIC_TOOL, sep="-") 8 ENRICHED_RDATA_VERSION <- paste("1.1.2", OUTPUT_SPECIFIC_TOOL, sep="-")
9 ENRICHED_RDATA_DOC <- sprintf(" 9 ENRICHED_RDATA_DOC <- sprintf("
307 dummy_adduct$set_oidscore(adduct[[i <- i+1]]) 307 dummy_adduct$set_oidscore(adduct[[i <- i+1]])
308 dummy_adduct$set_quasi(adduct[[i <- i+1]]) 308 dummy_adduct$set_quasi(adduct[[i <- i+1]])
309 dummy_adduct$set_ips(adduct[[i <- i+1]]) 309 dummy_adduct$set_ips(adduct[[i <- i+1]])
310 dummy_adduct$set_formula_add(adduct[[i <- i+1]]) 310 dummy_adduct$set_formula_add(adduct[[i <- i+1]])
311 dummy_adduct$set_formula_ded(adduct[[i <- i+1]]) 311 dummy_adduct$set_formula_ded(adduct[[i <- i+1]])
312 dummy_adduct$save() 312 invisible(dummy_adduct$save())
313 dummy_adduct$clear(unset_id=TRUE) 313 dummy_adduct$clear(unset_id=TRUE)
314 } 314 }
315 message("Adducts created") 315 message("Adducts created")
316 } 316 }
317 317
337 for (i in seq_len(nrow(compounds))) { 337 for (i in seq_len(nrow(compounds))) {
338 dummy_compound$set_mz(compounds[i, "mz"]) 338 dummy_compound$set_mz(compounds[i, "mz"])
339 dummy_compound$set_name(compounds[i, "name"]) 339 dummy_compound$set_name(compounds[i, "name"])
340 dummy_compound$set_common_name(compounds[i, "common_name"]) 340 dummy_compound$set_common_name(compounds[i, "common_name"])
341 dummy_compound$set_formula(compounds[i, "formula"]) 341 dummy_compound$set_formula(compounds[i, "formula"])
342 # dummy_compound$set_mz(compounds[i, "mz"])
343 # dummy_compound$set_mz(compounds[i, "mz"])
344 compound_list[[length(compound_list)+1]] <- as.list( 342 compound_list[[length(compound_list)+1]] <- as.list(
345 dummy_compound, 343 dummy_compound,
346 c("mz", "name", "common_name", "formula") 344 c("mz", "name", "common_name", "formula")
347 ) 345 )
348 dummy_compound$clear(unset_id=TRUE) 346 dummy_compound$clear(unset_id=TRUE)
349 } 347 }
350 dummy_compound$save(bulk=compound_list) 348 invisible(dummy_compound$save(bulk=compound_list))
351 } 349 }
352 350
353 translate_compounds <- function(compounds) { 351 translate_compounds <- function(compounds) {
354 recognized_headers <- list( 352 recognized_headers <- list(
355 c("HMDB_ID", "MzBank", "X.M.H..", "X.M.H...1", "MetName", "ChemFormula", "INChIkey") 353 c("HMDB_ID", "MzBank", "X.M.H..", "X.M.H...1", "MetName", "ChemFormula", "INChIkey")
369 return (csv_header_translator(translator, compounds)) 367 return (csv_header_translator(translator, compounds))
370 } 368 }
371 369
372 guess_translator <- function(header) { 370 guess_translator <- function(header) {
373 result <- list( 371 result <- list(
374 # HMDB_ID=NULL,< 372 # HMDB_ID=NULL,
375 mz=NULL, 373 mz=NULL,
376 name=NULL, 374 name=NULL,
377 common_name=NULL, 375 common_name=NULL,
378 formula=NULL, 376 formula=NULL,
379 # inchi_key=NULL 377 # inchi_key=NULL
411 } 409 }
412 410
413 csv_header_translator <- function(translation_table, csv) { 411 csv_header_translator <- function(translation_table, csv) {
414 header_names <- names(translation_table) 412 header_names <- names(translation_table)
415 result <- data.frame(1:nrow(csv)) 413 result <- data.frame(1:nrow(csv))
416 # colnames(result) <- header_names
417 for (i in seq_along(header_names)) { 414 for (i in seq_along(header_names)) {
418 result[, header_names[[i]]] <- csv[, translation_table[[i]]] 415 result[, header_names[[i]]] <- csv[, translation_table[[i]]]
419 } 416 }
420 print(result[, "mz"])
421 result[, "mz"] <- as.numeric(result[, "mz"]) 417 result[, "mz"] <- as.numeric(result[, "mz"])
422 print(result[, "mz"])
423 return (result) 418 return (result)
424 } 419 }
425 420
426 set_database_version <- function(orm, version) { 421 set_database_version <- function(orm, version) {
427 orm$set_tag( 422 orm$set_tag(
479 message(sprintf("File grouping variable: %s", file_grouping_var)) 474 message(sprintf("File grouping variable: %s", file_grouping_var))
480 if(is.null(file_grouping_var)) { 475 if(is.null(file_grouping_var)) {
481 stop("Malformed variableMetada.") 476 stop("Malformed variableMetada.")
482 } 477 }
483 478
479 context <- new.env()
480 context$samples <- list()
481 context$peaks <- rdata$xa@xcmsSet@peaks
482 context$groupidx <- rdata$xa@xcmsSet@groupidx
483 xcms_set <- rdata$xa@xcmsSet
484 singlefile <- rdata$singlefile
484 process_arg_list <- rdata$listOFlistArguments 485 process_arg_list <- rdata$listOFlistArguments
486 var_meta <- rdata$variableMetadata
487 ## We needed to get rid of the rdata, which is very big.
488 ## So we gathered all variable assignment from rdata here, and got rid of it.
489 rm(rdata)
490
485 process_params <- list() 491 process_params <- list()
486 for (list_name in names(process_arg_list)) { 492 for (list_name in names(process_arg_list)) {
487 param_list <- list() 493 param_list <- list()
488 for (param_name in names(process_arg_list[[list_name]])) { 494 for (param_name in names(process_arg_list[[list_name]])) {
489 param_list[[param_name]] <- process_arg_list[[list_name]][[param_name]] 495 param_list[[param_name]] <- process_arg_list[[list_name]][[param_name]]
490 } 496 }
491 process_params[[length(process_params)+1]] <- param_list 497 process_params[[length(process_params)+1]] <- param_list
492 } 498 }
493 message("Parameters from previous processes extracted.") 499 message("Parameters from previous processes extracted.")
494 500
495 var_meta <- rdata$variableMetadata
496 align_group <- rep(0, nrow(var_meta))
497 var_meta <- cbind(var_meta, align_group)
498 context <- new.env()
499 context$clusters <- list()
500 context$groupidx <- rdata$xa@xcmsSet@groupidx
501 context$peaks <- rdata$xa@xcmsSet@peaks
502 context$show_percent <- show_percent
503 501
504 indices <- as.numeric(unique(var_meta[, file_grouping_var])) 502 indices <- as.numeric(unique(var_meta[, file_grouping_var]))
505 smol_xcms_set <- orm$smol_xcms_set() 503 smol_xcms_set <- orm$smol_xcms_set()
506 mz_tab_info <- new.env() 504 mz_tab_info <- new.env()
507 xcms_set <- rdata$xa@xcmsSet
508 g <- xcms::groups(xcms_set) 505 g <- xcms::groups(xcms_set)
509 mz_tab_info$group_length <- nrow(g) 506 mz_tab_info$group_length <- nrow(g)
510 mz_tab_info$dataset_path <- xcms::filepaths(xcms_set) 507 mz_tab_info$dataset_path <- xcms::filepaths(xcms_set)
511 mz_tab_info$sampnames <- xcms::sampnames(xcms_set) 508 mz_tab_info$sampnames <- xcms::sampnames(xcms_set)
512 mz_tab_info$sampclass <- xcms::sampclass(xcms_set) 509 mz_tab_info$sampclass <- xcms::sampclass(xcms_set)
513 mz_tab_info$rtmed <- g[,"rtmed"] 510 mz_tab_info$rtmed <- g[,"rtmed"]
514 mz_tab_info$mzmed <- g[,"mzmed"] 511 mz_tab_info$mzmed <- g[,"mzmed"]
515 mz_tab_info$smallmolecule_abundance_assay <- xcms::groupval(xcms_set, value="into") 512 mz_tab_info$smallmolecule_abundance_assay <- xcms::groupval(xcms_set, value="into")
516 blogified <- blob::blob(fst::compress_fst(serialize(mz_tab_info, NULL), compression=100)) 513 blogified <- blob::blob(fst::compress_fst(serialize(mz_tab_info, NULL), compression=100))
517 smol_xcms_set$set_raw(blogified)$save() 514 rm(mz_tab_info)
515
516 invisible(smol_xcms_set$set_raw(blogified)$save())
517 smol_xcms_set_id <- smol_xcms_set$get_id()
518 rm(smol_xcms_set)
519
518 for (no in indices) { 520 for (no in indices) {
519 sample_name <- names(rdata$singlefile)[[no]] 521 sample_name <- names(singlefile)[[no]]
520 sample_path <- rdata$singlefile[[no]] 522 sample_path <- singlefile[[no]]
521 if ( 523 if (
522 is.na(no) 524 is.na(no)
523 || is.null(sample_path) 525 || is.null(sample_path)
524 || !(sample_name %in% sample_names) 526 || !(sample_name %in% sample_names)
525 ) { 527 ) {
526 next 528 next
527 } 529 }
528 ms_file=xcms::xcmsRaw(sample_path)
529 env <- new.env() 530 env <- new.env()
530 env$variableMetadata <- var_meta[var_meta[, file_grouping_var]==no,] 531 ms_file <- xcms::xcmsRaw(sample_path)
531 env$tic <- ms_file@tic 532 env$tic <- ms_file@tic
532 env$mz <- ms_file@env$mz 533 env$mz <- ms_file@env$mz
533 env$scanindex <- ms_file@scanindex 534 env$scanindex <- ms_file@scanindex
534 env$scantime <- ms_file@scantime 535 env$scantime <- ms_file@scantime
535 env$intensity <- ms_file@env$intensity 536 env$intensity <- ms_file@env$intensity
536 env$polarity <- as.character(ms_file@polarity[[1]]) 537 env$polarity <- as.character(ms_file@polarity[[1]])
538
539 ## Again, ms file is huge, so we get rid of it quickly.
540 rm(ms_file)
541
537 env$sample_name <- sample_name 542 env$sample_name <- sample_name
538 env$dataset_path <- sample_path 543 env$dataset_path <- sample_path
539 env$process_params <- process_params 544 env$process_params <- process_params
540 env$enriched_rdata <- TRUE 545 env$enriched_rdata <- TRUE
541 env$enriched_rdata_version <- ENRICHED_RDATA_VERSION 546 env$enriched_rdata_version <- ENRICHED_RDATA_VERSION
542 env$tool_name <- TOOL_NAME 547 env$tool_name <- TOOL_NAME
543 env$enriched_rdata_doc <- ENRICHED_RDATA_DOC 548 env$enriched_rdata_doc <- ENRICHED_RDATA_DOC
544 context$sample_no <- no 549 sample <- add_sample_to_database(orm, env, context, smol_xcms_set_id)
545 add_sample_to_database(orm, env, context, smol_xcms_set) 550 rm (env)
546 } 551 context$samples[no] <- sample$get_id()
552 rm (sample)
553 }
554 context$clusters <- list()
555 context$show_percent <- show_percent
556 context$cluster_mean_rt_abundance <- list()
557 context$central_feature <- list()
558 load_variable_metadata(orm, var_meta, context)
559 clusters <- context$clusters
560 rm(context)
547 message("Features enrichment") 561 message("Features enrichment")
548 complete_features(orm, context) 562 complete_features(orm, clusters, show_percent)
549 message("Features enrichment done.") 563 message("Features enrichment done.")
550 return (NULL) 564 return (NULL)
551 } 565 }
552 566
553 find_grouping_var <- function(var_meta) { 567 find_grouping_var <- function(var_meta) {
569 stop("Could not find any class column in your variableMetadata.") 583 stop("Could not find any class column in your variableMetadata.")
570 } 584 }
571 return (classes[[1]]) 585 return (classes[[1]])
572 } 586 }
573 587
574 add_sample_to_database <- function(orm, env, context, smol_xcms_set) { 588 add_sample_to_database <- function(orm, env, context, smol_xcms_set_id) {
575 message(sprintf("Processing sample %s", env$sample_name)) 589 message(sprintf("Processing sample %s", env$sample_name))
576 sample <- ( 590 sample <- (
577 orm$sample() 591 orm$sample()
578 $set_name(env$sample_name) 592 $set_name(env$sample_name)
579 $set_path(env$dataset_path) 593 $set_path(env$dataset_path)
580 $set_kind("enriched_rdata") 594 $set_kind("enriched_rdata")
581 $set_polarity( 595 $set_polarity(
582 if (is.null(env$polarity) || identical(env$polarity, character(0))) "" 596 if (is.null(env$polarity) || identical(env$polarity, character(0))) ""
583 else env$polarity 597 else env$polarity
584 ) 598 )
585 $set_smol_xcms_set(smol_xcms_set)
586 $set_raw(blob::blob(fst::compress_fst( 599 $set_raw(blob::blob(fst::compress_fst(
587 serialize(env, NULL), 600 serialize(env, NULL),
588 compression=100 601 compression=100
589 ))) 602 )))
590 $save()
591 ) 603 )
592 load_variable_metadata(orm, sample, env$variableMetadata, context) 604 sample[["smol_xcms_set_id"]] <- smol_xcms_set_id
605 sample$modified__[["smol_xcms_set_id"]] <- smol_xcms_set_id
606 sample <- sample$save()
593 load_process_params(orm, sample, env$process_params) 607 load_process_params(orm, sample, env$process_params)
594 message(sprintf("Sample %s inserted.", env$sample_name)) 608 message(sprintf("Sample %s inserted.", env$sample_name))
595 return (sample) 609 return (sample)
596 } 610 }
597 611
598 612
599 load_variable_metadata <- function(orm, sample, var_meta, context) { 613 load_variable_metadata <- function(orm, var_meta, context) {
600 all_clusters <- orm$cluster()$all() 614 all_clusters <- orm$cluster()$all()
601 615
602 next_feature_id <- get_next_id(orm$feature()$all(), "featureID") 616 next_feature_id <- get_next_id(orm$feature()$all(), "featureID") + 1
603 next_cluster_id <- get_next_id(all_clusters, "clusterID") 617 next_cluster_id <- 0
604 next_pc_group <- get_next_id(all_clusters, "pc_group") 618 next_pc_group <- get_next_id(all_clusters, "pc_group")
605 next_align_group <- get_next_id(all_clusters, "align_group") 619 next_align_group <- get_next_id(all_clusters, "align_group") + 1
606 message("Extracting features") 620 message("Extracting features")
607 invisible(create_features( 621 invisible(create_features(
608 orm, sample, var_meta, context, 622 orm, var_meta, context,
609 next_feature_id, next_cluster_id, 623 next_feature_id, next_cluster_id,
610 next_pc_group, next_align_group 624 next_pc_group, next_align_group
611 )) 625 ))
612 message("Extracting features done.") 626 message("Extracting features done.")
613 return (NULL) 627 return (NULL)
614 } 628 }
615 629
616 get_next_id <- function(models, attribute) { 630 get_next_id <- function(models, attribute) {
617 if ((id <- models$max(attribute)) == Inf || id == -Inf) { 631 if ((id <- models$max(attribute)) == Inf || id == -Inf) {
618 return (1) 632 return (0)
619 } 633 }
620 return (id + 1) 634 return (id)
621 } 635 }
622 636
623 create_features <- function( 637 create_features <- function(
624 orm, sample, var_meta, context, 638 orm, var_meta, context,
625 next_feature_id, next_cluster_id, 639 next_feature_id, next_cluster_id,
626 next_pc_group, next_align_group 640 next_pc_group, next_align_group
627 ) { 641 ) {
628 field_names <- as.list(names(orm$feature()$fields__)) 642 field_names <- as.list(names(orm$feature()$fields__))
629 field_names[field_names=="id"] <- NULL 643 field_names[field_names=="id"] <- NULL
641 message("\r", sprintf("\r%d %%", percent), appendLF=FALSE) 655 message("\r", sprintf("\r%d %%", percent), appendLF=FALSE)
642 } 656 }
643 657
644 curent_var_meta <- var_meta[row, ] 658 curent_var_meta <- var_meta[row, ]
645 659
660
661 set_feature_fields_from_var_meta(dummy_feature, curent_var_meta)
662
663 dummy_feature$set_featureID(next_feature_id)
664 next_feature_id <- next_feature_id + 1
665 fake_iso <- dummy_feature$get_iso()
666 iso <- extract_iso(fake_iso)
667 clusterID <- extract_clusterID(fake_iso, next_cluster_id)
668 context$clusterID <- clusterID
669 dummy_feature$set_iso(iso)
670
671
646 peak_list <- context$peaks[context$groupidx[[row]], ] 672 peak_list <- context$peaks[context$groupidx[[row]], ]
647 if (! ("matrix" %in% class(peak_list))) { 673 if (! ("matrix" %in% class(peak_list))) {
648 peak_list <- matrix(peak_list, nrow=1, ncol=length(peak_list), dimnames=list(c(), names(peak_list))) 674 peak_list <- matrix(peak_list, nrow=1, ncol=length(peak_list), dimnames=list(c(), names(peak_list)))
649 } 675 }
650 sample_peak_list <- peak_list[as.integer(peak_list[, "sample"]) == context$sample_no, , drop=FALSE] 676
677 clusterID <- as.character(clusterID)
678 if (is.null(context$central_feature[[clusterID]])) {
679 int_o <- extract_peak_var(peak_list, "into")
680 context$central_feature[[clusterID]] <- (
681 peak_list[peak_list[, "into"] == int_o,]["sample"]
682 )
683 }
684
685 sample_peak_list <- peak_list[as.integer(peak_list[, "sample"]) == context$central_feature[[clusterID]], , drop=FALSE]
651 if (!identical(sample_peak_list, numeric(0)) && !is.null(nrow(sample_peak_list)) && nrow(sample_peak_list) != 0) { 686 if (!identical(sample_peak_list, numeric(0)) && !is.null(nrow(sample_peak_list)) && nrow(sample_peak_list) != 0) {
652 if (!is.na(int_o <- extract_peak_var(sample_peak_list, "into"))) { 687 if (!is.na(int_o <- extract_peak_var(sample_peak_list, "into"))) {
653 dummy_feature$set_int_o(int_o) 688 dummy_feature$set_int_o(int_o)
654 } 689 }
655 if (!is.na(int_b <- extract_peak_var(sample_peak_list, "intb"))) { 690 if (!is.na(int_b <- extract_peak_var(sample_peak_list, "intb"))) {
657 } 692 }
658 if (!is.na(max_o <- extract_peak_var(sample_peak_list, "maxo"))) { 693 if (!is.na(max_o <- extract_peak_var(sample_peak_list, "maxo"))) {
659 dummy_feature$set_max_o(max_o) 694 dummy_feature$set_max_o(max_o)
660 } 695 }
661 } 696 }
662
663 set_feature_fields_from_var_meta(dummy_feature, curent_var_meta)
664
665 dummy_feature$set_featureID(next_feature_id)
666 next_feature_id <- next_feature_id + 1
667 fake_iso <- dummy_feature$get_iso()
668 iso <- extract_iso(fake_iso)
669 clusterID <- extract_clusterID(fake_iso, next_cluster_id)
670 context$clusterID <- clusterID
671 dummy_feature$set_iso(iso)
672 create_associated_cluster( 697 create_associated_cluster(
673 sample, dummy_feature, clusterID, 698 context$central_feature[[clusterID]],
699 dummy_feature, clusterID,
674 context, curent_var_meta, next_pc_group, 700 context, curent_var_meta, next_pc_group,
675 next_align_group 701 next_align_group
676 ) 702 )
677 next_align_group <- next_align_group + 1 703 next_align_group <- next_align_group + 1
678 features[[length(features)+1]] <- as.list(dummy_feature, field_names) 704 features[[length(features)+1]] <- as.list(dummy_feature, field_names)
679 dummy_feature$clear() 705 dummy_feature$clear()
680 } 706 }
681 message("")## +\n for previous message 707 message("")## +\n for previous message
682 message("Saving features") 708 message("Saving features")
683 dummy_feature$save(bulk=features) 709 rm(var_meta)
710 invisible(dummy_feature$save(bulk=features))
684 message("Saved.") 711 message("Saved.")
685 return (context$clusters) 712 return (context$clusters)
686 } 713 }
687 714
688 extract_peak_var <- function(peak_list, var_name, selector=max) { 715 extract_peak_var <- function(peak_list, var_name, selector=max) {
732 } 759 }
733 return (clusterID + next_cluster_id) 760 return (clusterID + next_cluster_id)
734 } 761 }
735 762
736 create_associated_cluster <- function( 763 create_associated_cluster <- function(
737 sample, feature, grouping_variable, 764 sample_no, feature, clusterID,
738 context, curent_var_meta, next_pc_group, next_align_group 765 context, curent_var_meta, next_pc_group, next_align_group
739 ) { 766 ) {
740 pcgroup <- as.numeric(curent_var_meta[["pcgroup"]]) 767 pcgroup <- as.numeric(curent_var_meta[["pcgroup"]])
741 adduct <- as.character(curent_var_meta[["adduct"]]) 768 adduct <- as.character(curent_var_meta[["adduct"]])
742 annotation <- curent_var_meta[["isotopes"]] 769 annotation <- curent_var_meta[["isotopes"]]
743 grouping_variable <- as.character(grouping_variable) 770 clusterID <- as.character(clusterID)
744 if (is.null(cluster <- context$clusters[[grouping_variable]])) { 771 if (is.null(cluster <- context$clusters[[clusterID]])) {
745 cluster <- context$clusters[[grouping_variable]] <- orm$cluster( 772 cluster <- context$clusters[[clusterID]] <- orm$cluster(
746 pc_group=pcgroup + next_pc_group, 773 pc_group=pcgroup + next_pc_group,
747 adduct=adduct, 774 adduct=adduct,
748 align_group=next_align_group, 775 align_group=next_align_group,
749 # curent_group=curent_group, 776 # curent_group=curent_group,
750 clusterID=context$clusterID, 777 clusterID=context$clusterID,
751 annotation=annotation 778 annotation=annotation
752 )$set_sample(sample) 779 )
780 ## Crappy hack to assign sample id to cluster without loading the sample.
781 ## Samples are too big (their sample$env) and slows the process, and eat all the menory
782 ## so we dont't want to load them.
783 cluster[["sample_id"]] <- context$samples[sample_no][[1]]
784 cluster$modified__[["sample_id"]] <- cluster[["sample_id"]]
753 } else { 785 } else {
754 if (context$clusterID != 0 && cluster$get_clusterID() == 0) { 786 if (context$clusterID != 0 && cluster$get_clusterID() == 0) {
755 cluster$set_clusterID(context$clusterID) 787 cluster$set_clusterID(context$clusterID)
756 } 788 }
757 } 789 }
758 cluster$save() 790 cluster$save()
759 feature$set_cluster(cluster) 791 feature$set_cluster(cluster)
760 return (feature) 792 return (feature)
761 } 793 }
762 794
763 complete_features <- function(orm, context) { 795 complete_features <- function(orm, clusters, show_percent) {
764 for (cluster in context$clusters) { 796 total <- length(clusters)
797 percent <- -1
798 i <- 0
799 for (cluster in clusters) {
800 i <- i+1
801 if (show_percent && (i / total) * 100 > percent) {
802 percent <- percent + 1
803 message("\r", sprintf("\r%d %%", percent), appendLF=FALSE)
804 }
765 features <- orm$feature()$load_by(cluster_id=cluster$get_id()) 805 features <- orm$feature()$load_by(cluster_id=cluster$get_id())
766 if (features$any()) { 806 if (features$any()) {
767 if (!is.null(rt <- features$mean("rt"))) { 807 if (!is.null(rt <- features$mean("rt"))) {
768 cluster$set_mean_rt(rt)$save() 808 cluster$set_mean_rt(rt)$save()
769 } 809 }