Use NMF to perform dimension reduction.
runNMF(
gobject,
spat_unit = NULL,
feat_type = NULL,
expression_values = c("normalized", "scaled", "custom"),
reduction = c("cells", "feats"),
name = NULL,
feats_to_use = "hvf",
return_gobject = TRUE,
scale_unit = TRUE,
k = 20,
method = c("rcppml"),
rev = FALSE,
set_seed = TRUE,
seed_number = 1234,
verbose = TRUE,
toplevel = 1L,
...
)giotto object
spatial unit (e.g. "cell")
feature type (e.g. "rna", "dna", "protein")
expression values to use
"cells" or "feats"
arbitrary name for NMF run
subset of features to use for NMF
boolean: return giotto object (default = TRUE)
scale features before NMF (default = TRUE)
NMF rank (number of components to decompose into). Default is 20
which implementation to use (only rcppml right now)
do a reverse NMF
use of seed
seed number to use
verbosity of the function
relative stackframe where call was made
additional parameters for NMF (see details)
giotto object with updated NMF dimension reduction
See nmf for more information about other parameters.
g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> 3.2 read Giotto spatial centroid information
#> 3.3 read Giotto spatial overlap information
#> 4. read Giotto image information
#> python already initialized in this session
#> active environment : '/usr/bin/python3'
#> python version : 3.10
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#> "/usr/bin/python3"
x <- runNMF(g, k = 20)
#> "hvf" column was found in the feats metadata information and will be
#> used to select highly variable features
#> [Running RcppML NMF]. This citation shown once per session:
#> Zachary J. DeBruine, Karsten Melcher, Timothy J. Triche Jr
#> Fast and robust non-negative matrix factorization for single-cell experiments
#> bioRxiv 2021.09.01.458620.
#> https://doi.org/10.1101/2021.09.01.458620
#>
#> iter | tol
#> ---------------
#> 1 | 8.68e-01
#> 2 | 1.06e-01
#> 3 | 4.20e-02
#> 4 | 2.40e-02
#> 5 | 1.50e-02
#> 6 | 1.07e-02
#> 7 | 8.13e-03
#> 8 | 5.39e-03
#> 9 | 3.11e-03
#> 10 | 2.04e-03
#> 11 | 1.53e-03
#> 12 | 1.18e-03
#> 13 | 9.46e-04
#> 14 | 7.65e-04
#> 15 | 6.38e-04
#> 16 | 5.10e-04
#> 17 | 4.68e-04
#> 18 | 4.30e-04
#> 19 | 3.94e-04
#> 20 | 3.51e-04
#> 21 | 3.07e-04
#> 22 | 2.39e-04
#> 23 | 1.90e-04
#> 24 | 1.70e-04
#> 25 | 1.53e-04
#> 26 | 1.35e-04
#> 27 | 1.21e-04
#> 28 | 1.05e-04
#> 29 | 9.11e-05
#> Setting dimension reduction [cell][rna] nmf
x <- runUMAP(x,
dim_reduction_to_use = "nmf",
dimensions_to_use = 1:20,
name = "nmf_umap"
)
#> Setting dimension reduction [cell][rna] nmf_umap
x <- createNearestNetwork(x,
dim_reduction_to_use = "nmf",
dim_reduction_name = "nmf",
dimensions_to_use = 1:20
)
x <- doLeidenCluster(x, name = "nmf_leiden", network_name = "sNN.nmf")
#> Error in py_run_file_impl(file, local, convert): ModuleNotFoundError: No module named 'igraph'
#> Run `reticulate::py_last_error()` for details.
plotUMAP(x, dim_reduction_name = "nmf_umap", cell_color = "nmf_leiden")
#> Error in plot_point_layer_ggplot(ggobject = pl, instrs = instructions(gobject), annotated_DT_selected = annotated_DT_selected, annotated_DT_other = annotated_DT_other, cell_color = cell_color, color_as_factor = color_as_factor, cell_color_code = cell_color_code, cell_color_gradient = cell_color_gradient, gradient_midpoint = gradient_midpoint, gradient_style = gradient_style, gradient_limits = gradient_limits, select_cell_groups = select_cell_groups, select_cells = select_cells, show_other_cells = show_other_cells, other_cell_color = other_cell_color, other_point_size = other_point_size, show_cluster_center = show_cluster_center, show_center_label = show_center_label, center_point_size = center_point_size, center_point_border_col = center_point_border_col, center_point_border_stroke = center_point_border_stroke, label_size = label_size, label_fontface = label_fontface, edge_alpha = edge_alpha, point_size = point_size, point_alpha = point_alpha, point_border_col = point_border_col, point_border_stroke = point_border_stroke, show_legend = show_legend): nmf_leiden is not a color or a column name
spatPlot2D(x, cell_color = "nmf_leiden")
#> Error in (function (ggobject, instrs = NULL, sdimx = NULL, sdimy = NULL, plot_method = c("ggplot", "scattermore"), cell_locations_metadata_selected, cell_locations_metadata_other, cell_color = NULL, color_as_factor = TRUE, cell_color_code = NULL, cell_color_gradient = NULL, gradient_midpoint = NULL, gradient_style = "divergent", gradient_limits = NULL, select_cell_groups = NULL, select_cells = NULL, point_size = 2, point_alpha = 1, point_border_col = "lightgrey", point_border_stroke = 0.1, show_cluster_center = FALSE, show_center_label = TRUE, center_point_size = 4, center_point_border_col = "black", center_point_border_stroke = 0.1, label_size = 4, label_fontface = "bold", show_other_cells = TRUE, other_cell_color = "lightgrey", other_point_size = 1, show_legend = TRUE, ...) { if (is.null(sdimx) || is.null(sdimy)) { warning(wrap_txt("plot_method = ggplot,\n but spatial dimensions for sdimx and/or sdimy\n are not specified. It will default to the\n 'sdimx' and 'sdimy' ")) sdimx <- "sdimx" sdimy <- "sdimy" } plot_method <- match.arg(plot_method, c("ggplot", "scattermore")) point_size <- as.numeric(point_size) point_alpha <- as.numeric(point_alpha) point_border_stroke <- as.numeric(point_border_stroke) center_point_size <- as.numeric(center_point_size) center_point_border_stroke <- as.numeric(center_point_border_stroke) label_size <- as.numeric(label_size) other_point_size <- as.numeric(other_point_size) pl <- ggobject if ((!is.null(select_cells) || !is.null(select_cell_groups)) && isTRUE(show_other_cells)) { pl <- pl + ggplot2::geom_point(data = cell_locations_metadata_other, mapping = aes_string(x = sdimx, sdimy), color = other_cell_color, show.legend = FALSE, size = other_point_size, alpha = point_alpha) } if (is.null(cell_color)) { cell_color <- "lightblue" pl <- pl + giotto_point(plot_method = plot_method, data = cell_locations_metadata_selected, mapping = aes_string(x = sdimx, y = sdimy), show.legend = show_legend, shape = 21, fill = cell_color, size = point_size, stroke = point_border_stroke, color = point_border_col, alpha = point_alpha) } else if (length(cell_color) > 1L) { if (is.numeric(cell_color) || is.factor(cell_color)) { if (nrow(cell_locations_metadata_selected) != length(cell_color)) { stop("\n vector needs to be the same lengths as\n number of cells \n") } cell_locations_metadata_selected[["temp_color"]] <- cell_color pl <- pl + giotto_point(plot_method = plot_method, data = cell_locations_metadata_selected, mapping = aes_string2(x = sdimx, y = sdimy, fill = "temp_color"), show.legend = show_legend, shape = 21, size = point_size, color = point_border_col, stroke = point_border_stroke, alpha = point_alpha) } else if (is.character(cell_color)) { if (!all(cell_color %in% grDevices::colors())) { stop("cell_color is not numeric,\n a factor or vector of colors \n") } pl <- pl + giotto_point(plot_method = plot_method, data = cell_locations_metadata_selected, mapping = aes_string2(x = sdimx, y = sdimy), show.legend = show_legend, shape = 21, fill = cell_color, size = point_size, color = point_border_col, stroke = point_border_stroke, alpha = point_alpha) } } else if (is.character(cell_color)) { if (!cell_color %in% colnames(cell_locations_metadata_selected)) { if (!cell_color %in% grDevices::colors()) { stop(cell_color, " is not a color or a column name \n") } pl <- pl + giotto_point(plot_method = plot_method, data = cell_locations_metadata_selected, mapping = aes_string2(x = sdimx, y = sdimy), show.legend = show_legend, shape = 21, fill = cell_color, size = point_size, color = point_border_col, stroke = point_border_stroke, alpha = point_alpha) } else { class_cell_color <- class(cell_locations_metadata_selected[[cell_color]]) if ((class_cell_color %in% c("integer", "numeric")) && !isTRUE(color_as_factor)) { if (!is.null(gradient_limits) && is.vector(gradient_limits) && length(gradient_limits) == 2) { lower_lim <- gradient_limits[[1]] upper_lim <- gradient_limits[[2]] numeric_data <- cell_locations_metadata_selected[[cell_color]] limit_numeric_data <- ifelse(numeric_data > upper_lim, upper_lim, ifelse(numeric_data < lower_lim, lower_lim, numeric_data)) cell_locations_metadata_selected[[cell_color]] <- limit_numeric_data } pl <- pl + giotto_point(plot_method = plot_method, data = cell_locations_metadata_selected, mapping = aes_string2(x = sdimx, y = sdimy, fill = cell_color), show.legend = show_legend, shape = 21, size = point_size, color = point_border_col, stroke = point_border_stroke, alpha = point_alpha) } else { if (isTRUE(color_as_factor)) { factor_data <- factor(cell_locations_metadata_selected[[cell_color]]) cell_locations_metadata_selected[[cell_color]] <- factor_data } if (isTRUE(show_cluster_center) || isTRUE(show_center_label)) { annotated_DT_centers <- cell_locations_metadata_selected[, .(center_1 = stats::median(get("sdimx")), center_2 = stats::median(get("sdimy"))), by = cell_color] factor_center_data <- factor(annotated_DT_centers[[cell_color]]) annotated_DT_centers[[cell_color]] <- factor_center_data } pl <- pl + giotto_point(plot_method = plot_method, data = cell_locations_metadata_selected, mapping = aes_string2(x = sdimx, y = sdimy, fill = cell_color), show.legend = show_legend, shape = 21, size = point_size, color = point_border_col, stroke = point_border_stroke, alpha = point_alpha) if (isTRUE(show_cluster_center) && (isTRUE(color_as_factor) || class_cell_color %in% c("character", "factor"))) { pl <- pl + giotto_point(plot_method = plot_method, data = annotated_DT_centers, mapping = aes_string2(x = "center_1", y = "center_2", fill = cell_color), color = center_point_border_col, stroke = center_point_border_stroke, size = center_point_size, shape = 21, alpha = point_alpha) } if (isTRUE(show_center_label)) { pl <- pl + ggrepel::geom_text_repel(data = annotated_DT_centers, mapping = aes_string2(x = "center_1", y = "center_2", label = cell_color), size = label_size, fontface = label_fontface) } } if (!is.null(cell_color_code)) { pl <- pl + ggplot2::scale_fill_manual(values = cell_color_code) } else if (isTRUE(color_as_factor)) { number_colors <- length(unique(factor_data)) cell_color_code <- set_default_color_discrete_cell(instrs = instrs)(n = number_colors) names(cell_color_code) <- unique(factor_data) pl <- pl + ggplot2::scale_fill_manual(values = cell_color_code) } else if (!isTRUE(color_as_factor)) { if (is.null(gradient_midpoint)) { gradient_midpoint <- stats::median(cell_locations_metadata_selected[[cell_color]]) } pl <- pl + set_default_color_continuous_cell(colors = cell_color_gradient, instrs = instrs, style = gradient_style, midpoint = gradient_midpoint, type = "fill", ) } } } return(pl)})(ggobject = structure(list(data = structure(list(), class = "waiver"), layers = list(), scales = <environment>, guides = <environment>, mapping = structure(list(), names = character(0), class = "uneval"), theme = structure(list(line = structure(list(colour = 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"/usr/bin/python3", show_plot = TRUE, return_plot = FALSE, save_plot = FALSE, save_dir = NA, plot_format = "png", dpi = 300, units = "in", height = 9, width = 9, is_docker = FALSE, active_spat_unit = "cell", active_feat_type = "rna"), class = "giottoInstructions"), sdimx = "sdimx", sdimy = "sdimy", plot_method = "ggplot", cell_locations_metadata_selected = structure(list(cell_ID = c("AAAGGGATGTAGCAAG-1", "AAATGGCATGTCTTGT-1", "AAATGGTCAATGTGCC-1", "AAATTAACGGGTAGCT-1", "AACAACTGGTAGTTGC-1", "AACAGGAAATCGAATA-1", "AACAGGATGGGCCGCG-1", "AACCATGGGATCGCTA-1", "AACCCAGAGACGGAGA-1", "AACCGAGCTTGGTCAT-1", "AACCGTTGTGTTTGCT-1", "AACGATAGAAGGGCCG-1", "AACGATATGTCAACTG-1", "AACGCGGTCTCCAGCC-1", "AACGTCAGACTAGTGG-1", "AACTCGATGGCGCAGT-1", "AACTGATATTAGGCCT-1", "AACTGGGTCCCGACGT-1", "AACTTGCCCGTATGCA-1", "AAGAGATGAATCGGTA-1", "AAGCTCGTGCCAAGTC-1", "AAGGAGCGGTTGGTGC-1", "AAGGCGCGTAAAGCTT-1", "AAGGCTGTGCTCATCG-1", "AAGTAGAAGACCGGGT-1", "AAGTAGTGACGCGAGG-1", "AATCCAAGGGCCTGAG-1", "AATGACTGTCAGCCGG-1", "AATGCAACCGGGTACC-1", "AATGGTTCTCACAAGC-1", "AATTTGGTTCCAAAGA-1", "ACAACAGCATGAGCTA-1", "ACAACGGTCCCTGCGA-1", "ACAAGGACAAGAGGTT-1", "ACACAAAGACGGGTGG-1", "ACACACTTTCTACACG-1", "ACACCCAGCATGCAGC-1", "ACACCCGAGAAATCCG-1", "ACACCTTAAGTAGGGC-1", "ACACGAGACTCCTTCT-1", "ACACGGGAACTTAGGG-1", "ACATAATAAGGCGGTG-1", "ACCAAGAACGCGTGTC-1", "ACCACAAGTTTCTATC-1", "ACCCTTCATCTGCGAA-1", "ACCGACTGAGTCCCAC-1", "ACCTAATCGACTTCCT-1", "ACGATCATACATAGAG-1", "ACGCATTCGTGAGTAC-1", "ACGCCAGATGATTTCT-1", "ACGCGGGCCAAGGACA-1", "ACGGAATTTAGCAAAT-1", "ACGTAGATTGCTGATG-1", "ACGTGCGCCTCGTGCA-1", "ACGTTTAGTTGTGATC-1", "ACTACCAGCTCTCTGG-1", "ACTATTCGTCCGTGGT-1", "ACTCAATAAAGGCACG-1", "ACTGCCGTCGTAACTC-1", "ACTGTAGCACTTTGGA-1", "ACTGTCCAGGATTATA-1", "ACTGTCTTCTTTAGAA-1", "ACTGTGCTAGTAGATC-1", "ACTTAGTACGACAAGA-1", "ACTTCGCCATACGCAC-1", "ACTTGGGACCCGGTGG-1", "ACTTTCCTATAGCTTC-1", "AGAACGTGGTACATTC-1", "AGACCATGGGATACAA-1", "AGACCCGCCCTCCTCG-1", "AGACTAGCCTTCCAGA-1", "AGAGCGGGCTAATCAT-1", "AGAGCGTACAAGCTCG-1", "AGATAACTTCAGGGCC-1", "AGATACGACTTCATAT-1", "AGATCTCAGGTGTGAT-1", "AGATGACTCGCCCACG-1", "AGCACCAGTACTCACG-1", "AGCACTTAAGGACGCC-1", "AGCATCGTCGATAATT-1", "AGCCCTAAGCGAAGTT-1", "AGCGACATCCCATTCA-1", "AGCGACCAACGATATT-1", "AGCGGGAAGGGTCCAT-1", "AGCTATTTAATCCAAC-1", "AGGACATCGCACGTCG-1", "AGGCCACCCGTTATGA-1", "AGGCCCATTGTACAGG-1", "AGGCTATGGTTAGCTT-1", "AGGGCGTGATCGGCTA-1", "AGGGCTGCAGTTACAG-1", "AGGGTTTAGTTCGGGA-1", "AGGTAACCTCCTATTC-1", "AGGTACGATATTGCCA-1", "AGGTAGGTACAAAGCT-1", "AGGTTGAGGCACGCTT-1", "AGTAATGTCTTGCCGC-1", "AGTAGGTAACATACAT-1", "AGTATAATACTAGGCA-1", "AGTCAGCCACCGCCTG-1", "AGTCGGCCCAAACGAC-1", "AGTCTTCTCCTCAAAT-1", "AGTTCCTACAGAATTA-1", "AGTTTCGCAGGTCGGA-1", "ATAAAGGCTCGGTCGT-1", "ATAACGGAGTCCAACG-1", "ATAATAGTGTAGGGAC-1", "ATACCACGGGCAACTT-1", "ATACTAGCATGACCCT-1", "ATAGACGAAGAGAAAG-1", "ATAGAGTACTGGGACA-1", "ATAGGGATATCCTTGA-1", "ATAGGTTGGGCAGATG-1", "ATATCTTAGGGCCTTC-1", "ATATTCCCACAGGTCA-1", "ATATTGCTGTCAAAGT-1", "ATATTTAACCCTCAAG-1", "ATCAATCTGGGCTGCA-1", "ATCACTTCATCCTCGC-1", "ATCCAACGCAGTCATA-1", "ATCGACTCTTTCCGTT-1", "ATCGCACGCCGGGAGA-1", "ATCTCCCTGCAATCTA-1", "ATCTGGTTAAGACTGT-1", "ATCTGTAATTGTACCC-1", "ATCTTGACCTGCAACG-1", "ATGAAGCCAAGGAGCC-1", "ATGACGCCGGCTCTAA-1", "ATGAGGAGTGTTAATC-1", "ATGATCGGGAATAGAC-1", "ATGCACTACCGCATTG-1", "ATGCATGATCCAGGAT-1", "ATGCCGGTCTTGCATA-1", "ATGCGACAGTCCCATT-1", "ATGCTCAGTGTTGCAT-1", "ATGGATTGACCAAACG-1", "ATGTGCATCCGACGCA-1", "ATGTTACGAGCAATAC-1", "ATTAATACTACGCGGG-1", "ATTAATTCGGTCACTC-1", "ATTACTAGCCTCTTGC-1", "ATTAGATTGATAGCGG-1", "ATTATGCCATAGGGAG-1", "ATTCCTCCGCCAGTGC-1", "ATTCTCGTCTCTTTAG-1", "ATTGAAGATCTTAGTG-1", "ATTGCTGCTCCTCCAT-1", "ATTGGGAATATCTTGG-1", "ATTTAACTCGTATTAC-1", "ATTTACAGTTTACTGG-1", "ATTTGTTCCAGGGCTC-1", "CAAACGGTCGCACTTT-1", "CAAACTATTGAGCTTC-1", "CAAATTGTCAGCAAGC-1", "CAACGACCCGTTTACA-1", "CAACGGTTCTTGATAC-1", "CAACTCCTTGATCCCG-1", "CAAGCAACGTCGGAGT-1", "CAAGCACCAAATGCCT-1", "CAATAAACCTTGGCCC-1", "CAATTAAGGGTGATGA-1", "CACACAGGGATAGATT-1", "CACAGCACCCACGGCA-1", "CACAGCTAGGGAGTGA-1", "CACCATCGGAGGAGAC-1", "CACCCACACGTCACCC-1", "CACCGCCAGAAGGTTT-1", "CACCTAATCAGTTTAC-1", "CACCTCGATGGTGGAC-1", "CACGAAAGTTAGTCCC-1", "CACGCACAGCGCAGCT-1", "CACGCAGCGAGGCTTT-1", "CACTAAAGTTGCCTAT-1", "CACTCAGCTCTTGAGG-1", "CACTGTCCAAGTGAGA-1", "CACTTAATCAGACGGA-1", "CACTTCGCCACAGGCT-1", "CAGAGCATGAGCTTGC-1", "CAGATACTAACATAGT-1", "CAGCTCACTGAGACAT-1", "CAGCTCGTGCTTGTGT-1", "CAGCTTAGTAGGTAGC-1", "CAGTGTCCGCAGAATG-1", "CAGTTCAAATTGACAC-1", "CATACAAAGCCGAACC-1", "CATAGTAGCATAGTAG-1", "CATCATCTACCCGGAC-1", "CATCGCCCGCGGCCAA-1", "CATGAACCGACATTTG-1", "CATGATGCACAATTCT-1", "CATGCGTTAGACAGAA-1", "CATGCTGGCTCCAATT-1", "CATGGCAGGAAGATCG-1", "CATGGTCTAGATACCG-1", "CATTACGTCGGCCCGT-1", "CATTATGCTTGTTGTG-1", "CCAAACAGAACCCTCG-1", "CCAACGATGCACTGAT-1", "CCAAGACTTCTGCGAA-1", "CCAAGCGTAACTCGTA-1", "CCAAGGAACAGAGAGG-1", "CCAATAGTGCCGTCGA-1", "CCAATTGAATGTTAAT-1", "CCACGAGAAGAGAATC-1", "CCAGGGACGTGGCCTC-1", "CCATAAACAACCCGAC-1", "CCATAGGTTGGCGTGG-1", "CCATCCATACCAAGTC-1", "CCATGCCTGTTTAGTA-1", "CCATGCTCTGCAGGAA-1", "CCATTAGCGATAATCC-1", "CCATTCCCTGCCCACA-1", "CCCGACCATAGTCCGC-1", "CCCGCCATGCTCCCGT-1", "CCCGTCAGCGTCTGAC-1", "CCCTCATTCTGGAATT-1", "CCCTGAAATGAGTTGA-1", "CCGAAAGTGGTGAGCA-1", "CCGCCGGTCAACACAC-1", "CCGGAATGGTTTCAGT-1", "CCGGCGCATATTGGAT-1", "CCGGGACCCGCAGAGA-1", "CCGGGCGGTCTCGTCA-1", "CCGTACCCAAGCGCCA-1", "CCGTGCCCATGACGGC-1", "CCTACGGCTCAGTCGA-1", "CCTACTGCTTACACTT-1", "CCTAGTTAGTCGCATG-1", "CCTATGGGTTACCGTC-1", "CCTCACCAATCTTGAC-1", "CCTCCGACAATTCAAG-1", "CCTCGAAGTGGACGGG-1", "CCTCGCCAGCAAATTA-1", "CCTCTAATCTGCCAAG-1", "CCTCTCTCCCATCTAG-1", "CCTCTGGCCTAGACGG-1", "CCTGAATATTTACATA-1", "CCTGCTATTTGAGAAG-1", "CCTGGCTAGACCCGCC-1", "CCTGTCACCCGGGCTC-1", "CCTTCTTGATCCAGTG-1", "CGAACGCCCAGTGCCG-1", "CGAAGTTGCTCTGTGT-1", "CGAGAGATGTGAACCT-1", "CGAGCGTTGATCAGCC-1", "CGAGCTGGGCTTTAGG-1", "CGAGGCTAAATATGGC-1", "CGCAAACACGAGTTAC-1", "CGCAATTACTTTCGGT-1", "CGCAATTAGGGTAATA-1", "CGCAATTCTACAATAA-1", "CGCATTAGCTAATAGG-1", "CGCGCCCGACTTAATA-1", "CGCGTTCATGAAATAC-1", "CGCTATACCGCCCACT-1", "CGCTATTCAATGTATG-1", "CGCTCGACATAATGAT-1", "CGCTCTCCGTAGATTA-1", "CGCTGTGACGCCGCAC-1", "CGCTTCCACTGAAATC-1", "CGCTTTCATACCGGTG-1", "CGGAGTTTGAGAGACA-1", "CGGCACTCAAGAAAGT-1", "CGGCCACGCACAAAGT-1", "CGGGATCAATGTAAGA-1", "CGGGCAGCTAAACCGC-1", "CGGTGTACTTGATCCC-1", "CGTACCTGATAGGCCT-1", "CGTAGCGAATTGTCAG-1", "CGTATTAAGAGATCTA-1", "CGTCAGTGCGCACAAG-1", "CGTCTGGAAGGGCCCG-1", "CGTGTCCCATTCGCGA-1", "CGTTGTTTCAATTCCC-1", "CGTTTAAGCGGAGCAC-1", "CGTTTCGCTCATTACA-1", "CTAATTCGCACGCGCT-1", "CTAATTTCAACAACAC-1", "CTACGCACGGAGTACC-1", "CTACTGCCACCTGACC-1", "CTAGCATAGTATAATG-1", "CTAGTGAAGGACAGGA-1", "CTAGTTGGGCCCGGTA-1", "CTATCGGGTCTCAACA-1", "CTATGTCACTAGCCCA-1", "CTATGTCTATTGAAAC-1", "CTCAGGACTCACCTGT-1", "CTCATGGCTCACAATC-1", "CTCATTTGATGGGCGG-1", "CTCCGGCCTAATATGC-1", "CTCCTAAGTTATGTCT-1", "CTCGAGGTCGAACAGT-1", "CTCGCACCTATATAGT-1", "CTCGGTCCGTAGCCTG-1", "CTCTCACAATCGATGA-1", "CTCTGCGAAGCAAGCA-1", "CTCTGGACGCCTGGTG-1", "CTCTGTTTGAGGATTC-1", "CTGAAAGAGATCCGAC-1", "CTGCACCTGGAACCGC-1", "CTGGAAATGGATGCTT-1", "CTGTATGGTGTAGAAA-1", "CTGTTCACTGCCTGTG-1", "CTGTTCATCTCACGGG-1", "CTTAACTTCGAAGTAC-1", "CTTACACGGTATTCCA-1", "CTTACACTGGGAAATA-1", "CTTATGTTGACTACCA-1", "CTTCATAGCTCAAGAA-1", "CTTCTTACGTCGTATA-1", "CTTGTACTTGTTGACT-1", "CTTGTTTATGTAGCCA-1", "CTTTAACTTTCAAAGG-1", "CTTTGGCTTTAGTAAA-1", "GAAACAGCCATGCAGT-1", "GAAACTCTAATGAAGG-1", "GAAAGTGACTAACTGC-1", "GAAATCGCGCGCAACT-1", "GAAATGGCGGTGTTAG-1", "GAAATTCACATCGCTG-1", "GAACACACATCAACCA-1", "GAACGACCGAATGATA-1", "GAAGAACGGTGCAGGT-1", "GAAGCGTGAGGAATTT-1", "GAAGCTTGCTGACCGC-1", "GAAGTGCTGGATAGCT-1", "GAATCGACATGGTCAC-1", "GAATCGCCGGACACGG-1", "GAATGTTGGGTAATCT-1", "GAATTATAGTGAAAGG-1", "GAATTTCTCGCTGCAG-1", "GACAACGCAGCTTACG-1", "GACACAAGGGAAGAAA-1", "GACCAGAGCCCTGTAG-1", "GACGCCTGTTGCAGGG-1", "GACGTGTAGGGATTAT-1", "GACTAAGATCATGCAC-1", "GACTAAGTAGGCTCAC-1", "GAGACTGATGGGTAGA-1", "GAGATCTGCTTGGCAT-1", "GAGATCTGTCACTCCG-1", "GAGCATCATCCCTGGG-1", "GAGCGAGGGAGTACCG-1", "GAGCTCTCGGACCTAA-1", "GAGGAATGGAGAGGTT-1", "GAGGCTATCAAAGTCG-1", "GAGGGCATCGCGTATC-1", "GAGGTACATCCATCTT-1", "GAGTACGGGTATACAA-1", "GATAAATCGGTGGATG-1", "GATATCTCATGCAATA-1", "GATCATTCCAAACATT-1", "GATCCCTTTATACTGC-1", "GATCGACACTATCTGA-1", "GATCGGTGGCCATAAC-1", "GATGCTACAAGCGCCT-1", "GATGGCGCACACATTA-1", "GATGTTTGTGCGAGAT-1", "GATTCCCTTGTCGCAG-1", "GATTCCGCGTTTCCGT-1", "GCAAATATTACGCTTT-1", "GCAAGTGCACAGAGAA-1", "GCACTAGTCGCGCTAT-1", "GCAGGACTATAGAATA-1", "GCATCCCTAACTTTGA-1", "GCCCACCAAGGCTGTC-1", "GCCCAGTTGGTATGCC-1", "GCCCGCGCGTTTGACA-1", "GCCGAAATTCCTACGT-1", "GCCTAGCGATCTGACC-1", "GCCTATTCCGATATAG-1", "GCCTTCAGCCCTACCG-1", "GCGAAGCCATACCCGT-1", "GCGAGTTCTGCAAAGA-1", "GCGCTAATTGAATAGA-1", "GCGGGAACCAGGCCCT-1", "GCGGTCCCTAGACGCA-1", "GCGTCGAAATGTCGGT-1", "GCTAATACCGAATGCC-1", "GCTAGTTTCATTGAGG-1", "GCTCTAAACCCTGACG-1", "GCTCTATGTTACGTGC-1", "GCTCTCGGGTACCGAA-1", "GCTGCTCTCCGGACAC-1", "GCTGGCATATTCACCT-1", "GCTTGATGATAATCAG-1", "GGAGAAGTCATTGGCA-1", "GGAGTGCCGCCCTGGA-1", "GGATTCAGTACGGTGG-1", "GGCAATAGTCAATGAG-1", "GGCACTCCACTGGGCA-1", "GGCATACAGGTAGCGG-1", "GGCGGAGTAATATTAG-1", "GGCGGTAGGATCATTG-1", "GGCGTCCTATCCGCTG-1", "GGCTATTAAGTTGTAT-1", "GGCTCGTGCCACCAGC-1", "GGCTCTGCTCCAACGC-1", "GGCTGGCTAGCTTAAA-1", "GGGACTGCATAGATAG-1", "GGGAGAACTCACAGTA-1", "GGGAGTTAATGAGGCG-1", "GGGCGATATGTGTGAA-1", "GGGCGGTCCTATTGTC-1", "GGGCGTACATTTATAT-1", "GGGCTGCCTAGGGCGA-1", "GGGCTGGTTAGTCGCG-1", "GGGTATGTATGCACTT-1", "GGGTCACCGTGACGGT-1", "GGGTGTTTCAGCTATG-1", "GGTAACTATGTATCTG-1", "GGTAGTGCTCGCACCA-1", "GGTATTGCCGAGTTTA-1", "GGTCGGTAATTAGACA-1", "GGTGAGATGCAGATAA-1", "GGTGCGGATAAGTGGC-1", "GGTTAGGCTTGGAGAA-1", "GGTTTACAATCTCAAT-1", "GGTTTGAGTGCTGGAA-1", "GTAAGCGGGCAGTCAG-1", "GTAAGTAGGGTATACC-1", "GTACGAGATTGCGACA-1", "GTACTAAGATTTGGAG-1", "GTACTGAGGTCGTAAC-1", "GTACTGCATGAAGCGT-1", "GTACTTGGGCACTTCT-1", "GTAGACGTCGTTACAT-1", "GTATAGGACTCAGTAG-1", "GTATCAGCTTGGGTTC-1", "GTCAAAGTTTACATAG-1", "GTCACTCTCCAAATCT-1", "GTCAGAATAGTCTATG-1", "GTCATGCACCTCCGTT-1", "GTCATGGACATGACTA-1", "GTCCCAACGTAAAGTA-1", "GTCCGGCTGAATTGCG-1", "GTCGGATATCTCAGAC-1", "GTCGTCAATTATAAGG-1", "GTGAAACGTGCTCCAC-1", "GTGCAACAAATGTGGC-1", "GTGCACGAAAGTGACT-1", "GTGCGGGTCTCCAAAT-1", "GTGCTCAAGTACTGTC-1", "GTGGACGTGCTGAGAC-1", "GTGGGCTTAGACACAC-1", "GTTCACAGGAGTCTAG-1", "GTTCCAGTCTGACCAT-1", "GTTCTTCCCTCGATGT-1", "GTTGCACGGAGTTTCG-1", "GTTGTCGTGTTAGTTG-1", "GTTTCCTGGAGGGTGA-1", "GTTTGACCAAATCCTA-1", "GTTTGGCCGCTCAGCG-1", "TAAATGAATCCGTTTC-1", "TAACAAAGGGAGAAGC-1", "TAACGCTTTGAGAGCG-1", "TAACTATCGAAGGTCC-1", "TAACTCCATGGAGGCT-1", "TAAGGAACTTGTGGGA-1", "TAAGGCAACATAAGAT-1", "TAATACACAGTAGTAT-1", "TAATATTGAAATTCGC-1", "TACAAGTCTCGTGCAT-1", "TACCAGCTAGGTTTAA-1", "TACCTCACGCTTGTAC-1", "TACGAACACGACTTCA-1", "TACGAGAACTTCACGT-1", "TACGATGTTGATCATC-1", "TACGTGCACTATGCTG-1", "TAGAGCTACGAAGAAC-1", "TAGAGTCTAAGCGAAC-1", "TAGCTAAGTCCGGGAG-1", "TAGGGTGTTTCAAGAG-1", "TAGGTGAGCCCTACTC-1", "TAGGTTCGAGTTCGTC-1", "TAGTGCCCTCCAGAGT-1", "TAGTTTATTCTTGCTT-1", "TATAAATCCACAAGCT-1", "TATAAGTGAGGATAGC-1", "TATACACGCAAAGTAT-1", "TATCCATATCATGCGA-1", "TATCCTGCATGGGAAT-1", "TATCGATGATTAAACG-1", "TATGCTCCCTACTTAC-1", "TATGGATGTGCTACGC-1", "TATGGCCCGGCCTCGC-1", "TATGGGTACGTATCGT-1", "TATTCAATTCTAATCC-1", "TATTCGTGCCAGAATA-1", "TATTTAGTCTAGATCG-1", "TATTTGTTACCCTTTA-1", "TCAAACAACCGCGTCG-1", "TCAAACTTAGATTGTT-1", "TCAACACATTGGGTAA-1", "TCAACCATGTTCGGGC-1", "TCACAGGTTATTGGGC-1", "TCACGCATTGTAGATC-1", "TCACGGTCATCGCACA-1", "TCAGCAAATGCATCTC-1", "TCAGGGTGTAACGTAA-1", "TCAGGTTCTTTGAGAA-1", "TCAGTACTGACCCGCG-1", "TCATCACTCGAGCTCG-1", "TCATCCCAGAGGGTGG-1", "TCCACAATGGTTTACG-1", "TCCAGGCGAGTACGGT-1", "TCCCAAAGACGAAGGA-1", "TCCCAGCTTTAGTCTG-1", "TCCCAGGCTTAGCTAA-1", "TCCCGCGTACTCCTGG-1", "TCCCGGGTGTGCTGCT-1", "TCCCGTCAGTCCCGCA-1", "TCCCTGGCTCGCTGGA-1", "TCCGATAATTGCCATA-1", "TCCGATGACTGAGCTC-1", "TCCGATGGTGCGACAT-1", "TCCGATTACATTGCCG-1", "TCCTCTACGAGATGGC-1", "TCCTTCAGTGGTCGAA-1", "TCCTTTCTTACGCTTA-1", "TCGAAATTTAGGACCA-1", "TCGCATCCCTAAGTGT-1", "TCGCCGGTCGATCCGT-1", "TCGCGTCCAGAAGGTC-1", "TCGCTCGATATATTCC-1", "TCGCTGGGCGGATTGT-1", "TCGCTGTGCGTAAATC-1", "TCGGAATGCGCTCTGA-1", "TCGGAGTACATGAGTA-1", "TCGTATAGTGCAATTA-1", "TCGTCCGCTGGCGTCT-1", "TCTACCGTCCACAAGC-1", "TCTAGCATCTTCGATG-1", "TCTAGTTATCAGAAGA-1", "TCTATCATGCAGTTAC-1", "TCTATCGGTCGCAACA-1", "TCTATTACGCTGGCGA-1", "TCTCTAATAGCTGGTA-1", "TCTGAACCGGTCGGCT-1", "TCTTACAGAGGTACCG-1", "TCTTACGGCATCCGAC-1", "TCTTCGATACCAATAA-1", "TCTTGATGCGTAGCGA-1", "TCTTGGTAACACCAAA-1", "TGAAACTTATGCAAGC-1", "TGACCCACGTTAGACA-1", "TGAGAATGCTTTACCG-1", "TGATCGGTTTGACCCT-1", "TGATCTCCGGCGCCAG-1", "TGATTCGTCTATCACT-1", "TGATTTCCTCCTGACG-1", "TGCAAGAATGACGTAA-1", "TGCAGGATCGGCAAAG-1", "TGCATGGATCGGATCT-1", "TGCCACCTGGCGAAAC-1", "TGCCTGATCAAACGAT-1", "TGCGCAAAGCATTTGG-1", "TGCGCGATTAACGGAG-1", "TGCGGAGTAAAGGTGC-1", "TGCGGCATAGTTCAAC-1", "TGCGGTGAAATTTCAT-1", "TGCTGGTTGGACAATT-1", "TGGAAGACGAACACCA-1", "TGGAAGGATAAAGATG-1", "TGGAGTGATGCGATGA-1", "TGGCCAAACTGAAGTA-1", "TGGCCGTATATTGACC-1", "TGGCTTTGGGTAGACA-1", "TGGGAAATGCCTTTCC-1", "TGGGCACGTTCTATGG-1", "TGGGCCACAAGAGCGC-1", "TGGTTCGTAGCAAAGG-1", "TGTAGTGATCTATAAT-1", "TGTCCCGACATAGCAC-1", "TGTCCTAAGTCACCGC-1", "TGTCTACAGTTTCTGT-1", "TGTGACTACGCCAGTC-1", "TGTGTCGCGAGTTGCA-1", "TGTTCTTCCATTGACT-1", "TGTTTCGGTACTTCTC-1", "TTAACCAACCCTCCCT-1", "TTAAGCGCCTGACCCA-1", "TTAATCAGTACGTCAG-1", "TTAATGTAGACCAGGT-1", "TTAATTTCAGACGCGG-1", "TTACAACTACGCATCC-1", "TTACATCGTGGCCTGG-1", "TTACCATTGATTACCC-1", "TTAGCAACATGGATGT-1", "TTATATTTGGCAATCC-1", "TTATCCAATCGAACTC-1", "TTATCTGTATCATAAC-1", "TTATGACAAACTGGAT-1", "TTATTAGGGAAGCATC-1", "TTCAAAGTCTCTAGCC-1", "TTCAACGACCCGACCG-1", "TTCAAGCCGAGCTGAG-1", "TTCATGGCGCAACAGG-1", "TTCCTCGAGGGTGTCT-1", "TTCGACGGGAAGGGCG-1", "TTCGCACTCGCGTGCT-1", "TTCGTACTCCAGAACG-1", "TTCTACTTGCGAGGGC-1", "TTCTAGGCCAATTGTG-1", "TTCTTAGTGAACGGTG-1", "TTCTTAGTGGCTCAGA-1", "TTCTTGTAACCTAATG-1", "TTGAAGAATTCCCAGG-1", "TTGAATATGGACTTTC-1", "TTGATCTAACTTTGTC-1", "TTGATTATGCAGATGA-1", "TTGCACGGAGCAGCAC-1", "TTGCTCCCATACCGGA-1", "TTGCTGAAGGAACCAC-1", "TTGCTGATCATGTTCG-1", "TTGGATTGGGTACCAC-1", "TTGGCTCGCATGAGAC-1", "TTGGGACACTGCCCGC-1", "TTGGGCGGCGGTTGCC-1", "TTGTAATCCGTACTCG-1", "TTGTATCACACAGAAT-1", "TTGTCGTTCAGTTACC-1", "TTGTGGCCCTGACAGT-1", "TTGTTCAGTGTGCTAC-1"), in_tissue = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), nr_feats = c(227L, 283L, 248L, 222L, 313L, 338L, 345L, 247L, 211L, 237L, 254L, 264L, 311L, 343L, 429L, 265L, 252L, 273L, 334L, 413L, 195L, 375L, 382L, 256L, 165L, 264L, 322L, 220L, 253L, 156L, 324L, 270L, 276L, 335L, 309L, 296L, 238L, 156L, 426L, 238L, 223L, 345L, 380L, 245L, 306L, 206L, 247L, 220L, 343L, 338L, 441L, 247L, 239L, 276L, 245L, 402L, 254L, 274L, 304L, 147L, 283L, 271L, 216L, 237L, 373L, 216L, 201L, 257L, 332L, 208L, 367L, 336L, 239L, 314L, 278L, 311L, 261L, 176L, 302L, 285L, 256L, 299L, 237L, 287L, 246L, 320L, 404L, 364L, 207L, 332L, 255L, 186L, 316L, 253L, 344L, 321L, 318L, 317L, 239L, 251L, 379L, 307L, 296L, 270L, 288L, 318L, 272L, 319L, 335L, 305L, 388L, 292L, 188L, 313L, 274L, 212L, 270L, 226L, 236L, 393L, 210L, 199L, 281L, 284L, 302L, 307L, 180L, 300L, 318L, 226L, 219L, 219L, 293L, 272L, 281L, 230L, 267L, 230L, 247L, 308L, 233L, 247L, 214L, 214L, 305L, 279L, 279L, 278L, 300L, 215L, 258L, 274L, 220L, 262L, 225L, 150L, 154L, 332L, 308L, 400L, 200L, 173L, 335L, 356L, 331L, 256L, 161L, 274L, 328L, 303L, 386L, 253L, 267L, 284L, 186L, 294L, 285L, 309L, 426L, 280L, 298L, 194L, 228L, 309L, 296L, 135L, 240L, 215L, 250L, 367L, 362L, 305L, 318L, 220L, 245L, 255L, 289L, 317L, 214L, 246L, 308L, 343L, 261L, 332L, 342L, 272L, 300L, 309L, 321L, 232L, 374L, 230L, 426L, 222L, 194L, 308L, 372L, 236L, 363L, 209L, 305L, 255L, 285L, 270L, 288L, 234L, 312L, 206L, 361L, 322L, 207L, 317L, 265L, 338L, 189L, 272L, 300L, 362L, 299L, 277L, 331L, 251L, 199L, 232L, 242L, 319L, 342L, 162L, 303L, 294L, 299L, 204L, 297L, 359L, 235L, 194L, 187L, 252L, 324L, 219L, 265L, 283L, 343L, 341L, 231L, 227L, 314L, 195L, 305L, 322L, 358L, 336L, 172L, 285L, 346L, 260L, 288L, 238L, 263L, 244L, 223L, 335L, 325L, 230L, 268L, 323L, 267L, 308L, 256L, 277L, 309L, 325L, 267L, 271L, 182L, 334L, 169L, 272L, 270L, 234L, 342L, 248L, 164L, 94L, 342L, 243L, 210L, 273L, 160L, 277L, 208L, 235L, 261L, 285L, 323L, 288L, 253L, 314L, 313L, 388L, 279L, 222L, 270L, 315L, 255L, 272L, 327L, 306L, 161L, 248L, 407L, 219L, 209L, 273L, 219L, 207L, 197L, 225L, 361L, 309L, 152L, 288L, 368L, 281L, 239L, 347L, 294L, 287L, 164L, 223L, 288L, 362L, 194L, 286L, 347L, 345L, 294L, 279L, 316L, 286L, 243L, 403L, 359L, 280L, 315L, 302L, 143L, 202L, 325L, 232L, 204L, 287L, 261L, 340L, 188L, 235L, 300L, 313L, 230L, 244L, 276L, 222L, 194L, 350L, 280L, 185L, 235L, 262L, 245L, 338L, 259L, 239L, 361L, 336L, 170L, 306L, 214L, 231L, 279L, 323L, 214L, 205L, 291L, 267L, 153L, 242L, 244L, 422L, 307L, 284L, 253L, 179L, 289L, 239L, 280L, 269L, 329L, 353L, 284L, 294L, 188L, 215L, 331L, 337L, 302L, 185L, 261L, 340L, 117L, 221L, 168L, 215L, 215L, 205L, 289L, 334L, 288L, 197L, 268L, 355L, 309L, 245L, 321L, 237L, 303L, 294L, 250L, 263L, 302L, 179L, 403L, 246L, 255L, 218L, 179L, 356L, 163L, 232L, 369L, 278L, 204L, 369L, 319L, 227L, 357L, 266L, 224L, 295L, 151L, 314L, 316L, 368L, 234L, 256L, 233L, 308L, 319L, 163L, 174L, 216L, 370L, 355L, 226L, 90L, 252L, 264L, 282L, 247L, 364L, 233L, 445L, 217L, 318L, 261L, 386L, 336L, 303L, 256L, 197L, 258L, 282L, 318L, 207L, 237L, 321L, 294L, 171L, 218L, 302L, 367L, 234L, 338L, 256L, 263L, 330L, 346L, 238L, 164L, 303L, 303L, 383L, 210L, 234L, 195L, 234L, 257L, 230L, 267L, 121L, 165L, 164L, 233L, 314L, 310L, 246L, 344L, 286L, 218L, 146L, 171L, 310L, 259L, 225L, 277L, 245L, 212L, 348L, 309L, 373L, 241L, 215L, 398L, 321L, 288L, 349L, 375L, 326L, 234L, 374L, 242L, 283L, 156L, 342L, 379L, 319L, 429L, 171L, 267L, 315L, 278L, 297L, 192L, 254L, 267L, 231L, 253L, 383L, 289L, 249L, 265L, 367L, 277L, 294L, 330L, 306L, 218L, 185L, 193L, 266L, 186L, 263L, 266L, 207L, 332L, 384L, 190L, 281L, 303L, 263L, 405L, 389L, 297L, 153L, 126L, 287L, 359L, 103L, 289L, 380L, 272L, 178L, 313L, 331L, 355L, 227L, 253L, 188L, 364L, 335L, 315L, 240L, 220L, 204L, 237L), perc_feats = c(35.8044164037855, 44.6372239747634, 39.1167192429022, 35.0157728706625, 49.3690851735016, 53.3123028391167, 54.416403785489, 38.9589905362776, 33.2807570977918, 37.3817034700315, 40.0630914826498, 41.6403785488959, 49.0536277602524, 54.1009463722397, 67.6656151419558, 41.7981072555205, 39.7476340694006, 43.0599369085174, 52.6813880126183, 65.1419558359621, 30.7570977917981, 59.1482649842271, 60.2523659305994, 40.3785488958991, 26.0252365930599, 41.6403785488959, 50.788643533123, 34.7003154574132, 39.9053627760252, 24.6056782334385, 51.1041009463722, 42.5867507886435, 43.5331230283912, 52.8391167192429, 48.7381703470032, 46.6876971608833, 37.5394321766561, 24.6056782334385, 67.192429022082, 37.5394321766561, 35.1735015772871, 54.416403785489, 59.9369085173502, 38.6435331230284, 48.2649842271293, 32.4921135646688, 38.9589905362776, 34.7003154574132, 54.1009463722397, 53.3123028391167, 69.5583596214511, 38.9589905362776, 37.6971608832808, 43.5331230283912, 38.6435331230284, 63.4069400630915, 40.0630914826498, 43.217665615142, 47.9495268138801, 23.186119873817, 44.6372239747634, 42.7444794952681, 34.0694006309148, 37.3817034700315, 58.8328075709779, 34.0694006309148, 31.7034700315457, 40.5362776025237, 52.3659305993691, 32.807570977918, 57.8864353312303, 52.9968454258675, 37.6971608832808, 49.5268138801262, 43.8485804416404, 49.0536277602524, 41.1671924290221, 27.7602523659306, 47.6340694006309, 44.9526813880126, 40.3785488958991, 47.1608832807571, 37.3817034700315, 45.2681388012618, 38.801261829653, 50.4731861198738, 63.7223974763407, 57.4132492113565, 32.6498422712934, 52.3659305993691, 40.2208201892744, 29.3375394321767, 49.8422712933754, 39.9053627760252, 54.2586750788643, 50.6309148264984, 50.1577287066246, 50, 37.6971608832808, 39.589905362776, 59.7791798107255, 48.4227129337539, 46.6876971608833, 42.5867507886435, 45.4258675078864, 50.1577287066246, 42.9022082018927, 50.3154574132492, 52.8391167192429, 48.1072555205047, 61.198738170347, 46.0567823343849, 29.6529968454259, 49.3690851735016, 43.217665615142, 33.4384858044164, 42.5867507886435, 35.6466876971609, 37.2239747634069, 61.98738170347, 33.1230283911672, 31.3880126182965, 44.3217665615142, 44.794952681388, 47.6340694006309, 48.4227129337539, 28.391167192429, 47.3186119873817, 50.1577287066246, 35.6466876971609, 34.5425867507886, 34.5425867507886, 46.2145110410095, 42.9022082018927, 44.3217665615142, 36.2776025236593, 42.1135646687697, 36.2776025236593, 38.9589905362776, 48.5804416403785, 36.7507886435331, 38.9589905362776, 33.7539432176656, 33.7539432176656, 48.1072555205047, 44.006309148265, 44.006309148265, 43.8485804416404, 47.3186119873817, 33.9116719242902, 40.6940063091483, 43.217665615142, 34.7003154574132, 41.3249211356467, 35.4889589905363, 23.6593059936909, 24.2902208201893, 52.3659305993691, 48.5804416403785, 63.0914826498423, 31.5457413249211, 27.2870662460568, 52.8391167192429, 56.1514195583596, 52.2082018927445, 40.3785488958991, 25.3943217665615, 43.217665615142, 51.7350157728707, 47.7917981072555, 60.8832807570978, 39.9053627760252, 42.1135646687697, 44.794952681388, 29.3375394321767, 46.3722397476341, 44.9526813880126, 48.7381703470032, 67.192429022082, 44.1640378548896, 47.0031545741325, 30.5993690851735, 35.9621451104101, 48.7381703470032, 46.6876971608833, 21.2933753943218, 37.8548895899054, 33.9116719242902, 39.4321766561514, 57.8864353312303, 57.0977917981073, 48.1072555205047, 50.1577287066246, 34.7003154574132, 38.6435331230284, 40.2208201892744, 45.583596214511, 50, 33.7539432176656, 38.801261829653, 48.5804416403785, 54.1009463722397, 41.1671924290221, 52.3659305993691, 53.9432176656151, 42.9022082018927, 47.3186119873817, 48.7381703470032, 50.6309148264984, 36.5930599369085, 58.9905362776025, 36.2776025236593, 67.192429022082, 35.0157728706625, 30.5993690851735, 48.5804416403785, 58.6750788643533, 37.2239747634069, 57.2555205047319, 32.9652996845426, 48.1072555205047, 40.2208201892744, 44.9526813880126, 42.5867507886435, 45.4258675078864, 36.9085173501577, 49.211356466877, 32.4921135646688, 56.9400630914826, 50.788643533123, 32.6498422712934, 50, 41.7981072555205, 53.3123028391167, 29.8107255520505, 42.9022082018927, 47.3186119873817, 57.0977917981073, 47.1608832807571, 43.6908517350158, 52.2082018927445, 39.589905362776, 31.3880126182965, 36.5930599369085, 38.1703470031546, 50.3154574132492, 53.9432176656151, 25.5520504731861, 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1095.70076163552, 1013.4656625271, 1194.38802909718, 1088.99443345778, 936.67022980206, 1176.93543473413, 1177.74723574121, 1021.64293042626, 802.713974099176, 1147.67871582472, 1131.01192302099, 1226.96256838525, 870.460785892088, 1022.22870709406, 896.795271403771, 996.2102724541, 1023.7651674657, 999.400651419049, 1077.23439007785, 609.822058312369, 799.892621773862, 803.519385118485, 992.90620935012, 1113.38678885843, 1148.7760839154, 1065.85921405778, 1084.6477018085, 979.82487122063, 992.516822490875, 758.82854410995, 837.209676696007, 1157.32310266157, 1050.26008916, 948.698388110398, 1101.76672867066, 1043.08842461029, 949.371092782475, 1072.44013370125, 1052.06463567415, 1116.35159775393, 1031.34610678917, 963.010326339346, 1072.16784833961, 990.116487658682, 1001.05172344948, 1234.29864311464, 1195.85293197176, 1024.11653330572, 1011.06024499406, 1221.37676532543, 1013.3624274076, 1123.15561440324, 778.323254312245, 1126.39926949168, 1272.46693250729, 1216.42004797225, 1361.8208304174, 841.747847838327, 1102.88307914987, 977.868009032042, 1122.16975079534, 1130.20864817178, 836.297449148413, 1050.90941494478, 1003.87747610452, 990.027544297921, 1049.97665609377, 1075.56023893662, 1075.64451501875, 1051.07682906703, 919.948881534829, 1183.25610320381, 1106.73404586847, 1120.56235656135, 1003.45069163949, 1097.55688659503, 822.603002712797, 879.414816012222, 884.498409861202, 1098.43925978458, 878.590133463639, 1027.29393858503, 1110.18874377088, 936.256932560715, 1190.07846348807, 916.592855409712, 894.260357637752, 1103.17140094924, 1101.23095357521, 1057.78357579323, 1226.20070515932, 1342.08106369467, 1149.18357250559, 789.595316615116, 525.519608557264, 942.686566618832, 1189.9151119911, 592.829149614415, 1119.45986306101, 912.305143466939, 940.494192466699, 805.428913159954, 1206.77802251838, 1083.72890979688, 1088.26723655449, 985.389052458918, 879.756775065605, 897.554063829254, 1115.16961458823, 1150.23011882017, 1184.01781830254, 999.641936650241, 994.664812118689, 930.548850034589, 1034.88927889182), leiden_clus = c(1, 2, 4, 4, 6, 2, 6, 5, 1, 5, 2, 5, 5, 3, 3, 2, 2, 5, 6, 7, 5, 4, 3, 4, 1, 4, 2, 1, 1, 1, 2, 1, 3, 6, 7, 2, 1, 1, 3, 1, 1, 3, 3, 4, 2, 1, 4, 1, 6, 2, 7, 5, 1, 5, 2, 3, 2, 3, 2, 1, 4, 1, 5, 5, 6, 4, 1, 5, 3, 1, 4, 2, 1, 4, 5, 5, 1, 1, 4, 1, 2, 5, 1, 2, 2, 3, 6, 5, 1, 3, 6, 1, 2, 2, 4, 2, 5, 2, 3, 5, 3, 3, 4, 2, 4, 5, 5, 4, 3, 4, 3, 5, 1, 6, 2, 1, 4, 1, 5, 3, 1, 1, 2, 5, 2, 3, 1, 5, 3, 1, 1, 3, 5, 5, 3, 1, 1, 5, 1, 2, 1, 1, 1, 3, 2, 4, 6, 2, 2, 4, 2, 4, 2, 2, 1, 1, 1, 4, 5, 6, 1, 1, 6, 3, 2, 3, 1, 2, 2, 6, 6, 4, 5, 2, 1, 4, 3, 3, 3, 4, 5, 6, 1, 4, 5, 1, 2, 5, 2, 6, 2, 2, 2, 1, 5, 3, 1, 4, 1, 1, 5, 4, 5, 2, 3, 2, 5, 5, 4, 1, 4, 4, 5, 1, 1, 3, 6, 5, 2, 3, 2, 1, 5, 2, 2, 5, 3, 2, 3, 3, 4, 2, 2, 5, 4, 2, 6, 3, 4, 3, 2, 2, 4, 1, 2, 2, 3, 1, 1, 6, 7, 1, 2, 3, 5, 1, 1, 4, 3, 1, 5, 6, 2, 4, 1, 4, 5, 1, 2, 4, 2, 2, 1, 4, 2, 4, 7, 1, 4, 1, 5, 6, 3, 5, 3, 2, 5, 2, 3, 1, 2, 3, 1, 3, 1, 2, 1, 2, 2, 5, 4, 3, 1, 1, 2, 4, 1, 2, 1, 2, 1, 5, 3, 4, 2, 2, 5, 2, 4, 2, 1, 1, 5, 6, 4, 2, 3, 2, 1, 5, 7, 1, 1, 4, 2, 7, 1, 1, 3, 6, 1, 2, 2, 5, 1, 6, 2, 2, 1, 1, 2, 4, 1, 3, 3, 3, 5, 3, 3, 2, 4, 3, 2, 4, 3, 2, 1, 1, 4, 1, 1, 1, 3, 5, 5, 1, 2, 2, 1, 3, 1, 3, 1, 3, 4, 1, 3, 1, 3, 3, 4, 4, 4, 3, 4, 4, 1, 1, 5, 6, 3, 5, 3, 5, 1, 1, 4, 7, 4, 2, 2, 5, 4, 2, 3, 3, 6, 6, 2, 3, 3, 1, 3, 2, 2, 1, 5, 3, 1, 5, 1, 4, 1, 1, 5, 3, 4, 2, 2, 2, 2, 1, 4, 3, 4, 4, 1, 5, 3, 1, 3, 3, 2, 3, 1, 7, 1, 5, 3, 1, 1, 3, 6, 2, 6, 4, 4, 4, 1, 2, 6, 3, 1, 6, 5, 4, 2, 1, 1, 1, 3, 2, 1, 1, 3, 6, 4, 5, 6, 1, 3, 5, 3, 2, 3, 4, 4, 3, 1, 1, 6, 3, 5, 2, 4, 4, 1, 4, 4, 3, 4, 2, 6, 5, 2, 3, 4, 1, 2, 3, 4, 5, 1, 6, 1, 5, 1, 6, 1, 2, 1, 1, 3, 4, 1, 3, 5, 1, 1, 1, 7, 3, 1, 1, 2, 4, 3, 3, 6, 4, 1, 3, 5, 5, 2, 5, 3, 1, 6, 2, 2, 1, 5, 6, 4, 6, 1, 1, 3, 2, 2, 1, 4, 2, 4, 2, 5, 3, 4, 1, 3, 4, 2, 3, 2, 1, 1, 4, 5, 1, 5, 1, 1, 2, 6, 1, 2, 2, 5, 3, 3, 6, 1, 1, 3, 5, 1, 4, 6, 4, 1, 3, 5, 3, 4, 1, 1, 3, 6, 4, 2, 1, 5, 1), custom_leiden = c(1, 3, 2, 2, 2, 3, 2, 4, 1, 1, 3, 4, 4, 5, 6, 4, 3, 1, 2, 1, 4, 2, 6, 2, 1, 2, 3, 1, 1, 1, 3, 1, 1, 2, 1, 3, 1, 1, 6, 1, 1, 5, 5, 2, 3, 1, 2, 1, 2, 3, 1, 4, 1, 4, 3, 6, 3, 1, 3, 1, 2, 1, 4, 4, 7, 2, 1, 1, 5, 1, 2, 3, 1, 2, 4, 4, 1, 1, 2, 1, 3, 1, 1, 3, 3, 5, 2, 4, 1, 5, 2, 1, 3, 3, 2, 3, 4, 3, 6, 4, 6, 5, 2, 3, 2, 7, 4, 2, 5, 2, 8, 4, 1, 2, 3, 1, 2, 1, 1, 6, 1, 1, 3, 4, 4, 6, 1, 4, 6, 1, 1, 1, 4, 4, 5, 1, 1, 4, 1, 3, 1, 1, 1, 6, 3, 2, 2, 3, 3, 2, 3, 2, 3, 3, 1, 1, 1, 2, 4, 7, 1, 1, 2, 5, 3, 5, 1, 3, 3, 2, 7, 2, 4, 3, 1, 2, 5, 1, 6, 2, 1, 2, 1, 2, 4, 1, 3, 4, 3, 7, 3, 3, 1, 1, 4, 8, 1, 2, 1, 1, 4, 2, 1, 3, 8, 3, 4, 4, 2, 1, 2, 2, 3, 1, 1, 5, 2, 4, 3, 8, 3, 1, 4, 3, 3, 4, 1, 3, 1, 5, 2, 3, 3, 4, 2, 3, 7, 8, 2, 1, 3, 3, 2, 1, 3, 3, 1, 1, 1, 2, 1, 1, 3, 8, 1, 1, 1, 2, 8, 1, 4, 2, 3, 2, 1, 2, 4, 1, 3, 2, 3, 3, 1, 2, 3, 2, 1, 1, 2, 1, 4, 2, 1, 4, 1, 3, 4, 3, 5, 1, 3, 8, 1, 5, 1, 3, 1, 3, 4, 1, 2, 5, 1, 1, 3, 2, 1, 3, 1, 3, 1, 4, 1, 2, 3, 3, 1, 3, 2, 3, 1, 1, 4, 2, 2, 3, 6, 3, 1, 4, 1, 1, 1, 2, 3, 1, 1, 1, 1, 2, 1, 3, 3, 4, 1, 2, 3, 3, 1, 1, 3, 2, 1, 6, 1, 5, 1, 5, 1, 3, 2, 6, 3, 2, 5, 3, 1, 1, 2, 1, 1, 1, 1, 4, 4, 1, 3, 3, 1, 5, 1, 6, 1, 1, 2, 1, 6, 1, 5, 1, 2, 2, 2, 1, 2, 2, 1, 1, 3, 2, 1, 4, 1, 4, 1, 1, 2, 1, 2, 3, 3, 4, 2, 3, 5, 5, 2, 7, 3, 1, 6, 1, 8, 3, 3, 1, 3, 5, 1, 4, 1, 2, 1, 1, 4, 1, 2, 3, 3, 3, 3, 1, 2, 5, 2, 2, 1, 4, 6, 1, 6, 1, 3, 1, 1, 1, 1, 4, 5, 3, 1, 5, 2, 3, 1, 2, 2, 2, 1, 3, 2, 1, 1, 2, 4, 2, 3, 1, 1, 1, 5, 3, 1, 1, 6, 2, 2, 4, 2, 1, 6, 1, 1, 3, 6, 2, 2, 1, 1, 1, 2, 6, 4, 3, 2, 2, 1, 2, 2, 1, 2, 4, 2, 4, 3, 6, 2, 1, 3, 1, 2, 4, 1, 2, 1, 4, 1, 2, 4, 3, 1, 1, 6, 2, 1, 5, 4, 1, 1, 1, 1, 1, 1, 1, 3, 2, 5, 8, 2, 2, 1, 1, 4, 4, 3, 4, 1, 1, 7, 3, 3, 1, 4, 4, 2, 7, 1, 1, 5, 3, 3, 1, 2, 3, 2, 3, 4, 6, 2, 1, 5, 2, 3, 1, 3, 1, 1, 2, 1, 1, 3, 1, 1, 3, 7, 1, 3, 3, 1, 6, 8, 2, 1, 1, 5, 4, 1, 2, 4, 2, 1, 1, 4, 5, 2, 3, 1, 1, 7, 2, 3, 1, 1, 1), sdimx = c(5477L, 5959L, 4720L, 5202L, 4101L, 5821L, 3344L, 3275L, 3895L, 6028L, 5615L, 4858L, 6440L, 5684L, 3964L, 3069L, 5752L, 5752L, 3069L, 3069L, 6234L, 4514L, 3826L, 4376L, 4445L, 3344L, 4445L, 4445L, 5271L, 3619L, 5821L, 6165L, 3275L, 3757L, 5064L, 5615L, 4652L, 3895L, 4170L, 6097L, 6096L, 5546L, 4583L, 4927L, 5684L, 5752L, 3276L, 5546L, 5408L, 6234L, 5477L, 3069L, 5821L, 5546L, 5890L, 4032L, 6303L, 5959L, 6028L, 3895L, 4996L, 5615L, 3344L, 3413L, 6372L, 4583L, 5477L, 6234L, 3138L, 5064L, 4376L, 5615L, 3138L, 4239L, 3619L, 6372L, 4996L, 5684L, 3620L, 4789L, 5684L, 5271L, 4996L, 3757L, 6303L, 3826L, 3826L, 4996L, 3276L, 4307L, 5684L, 5546L, 4032L, 6440L, 3344L, 6303L, 5340L, 4720L, 3482L, 3138L, 3895L, 5752L, 4652L, 5477L, 4652L, 6372L, 3275L, 3207L, 5408L, 4308L, 4514L, 6096L, 5615L, 5409L, 5271L, 3413L, 4996L, 5684L, 5821L, 3757L, 5408L, 5133L, 6165L, 6440L, 5133L, 4239L, 4376L, 5202L, 3620L, 4927L, 4308L, 5684L, 6372L, 6097L, 3207L, 5408L, 5133L, 3207L, 5752L, 5752L, 4239L, 4583L, 4858L, 3895L, 4720L, 4583L, 4170L, 4789L, 4789L, 3826L, 6096L, 3964L, 6372L, 5821L, 4652L, 3826L, 4376L, 3826L, 5752L, 6303L, 5684L, 3069L, 5271L, 5340L, 5408L, 4170L, 5271L, 6165L, 6028L, 3207L, 6165L, 4170L, 4720L, 6096L, 4170L, 4514L, 3344L, 6028L, 4376L, 5271L, 4101L, 3138L, 3757L, 4720L, 3619L, 4308L, 5959L, 4239L, 4170L, 5959L, 5546L, 5340L, 5133L, 5752L, 5684L, 4789L, 4514L, 3757L, 5477L, 6097L, 4514L, 4101L, 4376L, 6372L, 4858L, 6440L, 5477L, 3826L, 4858L, 4170L, 4101L, 3895L, 6303L, 4032L, 4720L, 4239L, 3895L, 3069L, 4995L, 3413L, 5064L, 4789L, 6441L, 3551L, 4514L, 3413L, 4583L, 6165L, 5959L, 3619L, 3482L, 6096L, 6303L, 6234L, 3688L, 6372L, 6096L, 4652L, 4170L, 5821L, 6234L, 6028L, 3620L, 6234L, 4376L, 5821L, 6096L, 3757L, 3482L, 4239L, 3138L, 5133L, 6372L, 4720L, 3688L, 3344L, 4308L, 3757L, 4445L, 4101L, 4651L, 3551L, 3138L, 5271L, 5271L, 5064L, 3344L, 5890L, 6096L, 4927L, 4583L, 5477L, 3964L, 3964L, 5271L, 4583L, 3551L, 3069L, 5133L, 5546L, 5753L, 3826L, 5890L, 5959L, 5752L, 4995L, 3275L, 5546L, 5477L, 5271L, 3275L, 5271L, 5890L, 4652L, 6028L, 4651L, 5477L, 6165L, 5959L, 4583L, 4858L, 3413L, 5064L, 3551L, 5546L, 4308L, 4514L, 3069L, 3757L, 5340L, 4720L, 3138L, 5821L, 3964L, 5408L, 6234L, 3344L, 5890L, 4789L, 4583L, 4652L, 5546L, 3963L, 5340L, 3620L, 5408L, 4101L, 4032L, 5959L, 5890L, 3276L, 4927L, 6165L, 4445L, 3344L, 3413L, 4858L, 4170L, 5064L, 4239L, 6303L, 3963L, 5202L, 3482L, 3344L, 3069L, 3482L, 5890L, 4170L, 4032L, 5202L, 5064L, 5340L, 4445L, 5890L, 4720L, 3207L, 3275L, 5821L, 6440L, 4032L, 4101L, 4170L, 3757L, 4101L, 3826L, 3826L, 3963L, 4308L, 4101L, 3275L, 4651L, 5408L, 5546L, 6028L, 4514L, 4307L, 5684L, 4445L, 3757L, 5202L, 3964L, 5202L, 5546L, 3344L, 3688L, 4032L, 4858L, 3482L, 6028L, 4376L, 3413L, 4376L, 5959L, 3482L, 4032L, 3826L, 6165L, 3207L, 5340L, 5615L, 6372L, 5202L, 3138L, 4927L, 5959L, 4720L, 3138L, 4445L, 5890L, 4239L, 6165L, 4858L, 3688L, 3688L, 4376L, 3688L, 6234L, 4101L, 5546L, 3688L, 5133L, 4996L, 4445L, 3895L, 3482L, 3895L, 5202L, 3551L, 3207L, 3344L, 3413L, 5408L, 5477L, 6234L, 4789L, 3688L, 4307L, 3757L, 5477L, 6096L, 5064L, 4789L, 3895L, 3895L, 3551L, 4858L, 4720L, 4514L, 4445L, 4583L, 5202L, 3413L, 3207L, 3619L, 3207L, 3551L, 3069L, 3963L, 6096L, 5615L, 4445L, 3069L, 5684L, 5064L, 4858L, 3895L, 3620L, 3482L, 4858L, 4996L, 5133L, 4927L, 3482L, 5821L, 4789L, 5133L, 4239L, 3895L, 3688L, 4789L, 4927L, 4583L, 5408L, 4927L, 3276L, 5202L, 6303L, 5615L, 5202L, 4376L, 5340L, 5684L, 3619L, 4720L, 4514L, 4583L, 4308L, 6234L, 6165L, 4101L, 3551L, 3207L, 6028L, 3688L, 5133L, 5340L, 5064L, 4514L, 5959L, 3551L, 6372L, 3069L, 5615L, 6440L, 4858L, 3413L, 3413L, 5959L, 5477L, 4308L, 3344L, 5408L, 3207L, 5546L, 6303L, 4032L, 3138L, 6303L, 5064L, 3964L, 5821L, 4170L, 4927L, 3963L, 5615L, 3551L, 4789L, 3757L, 4307L, 3207L, 5752L, 5890L, 5684L, 6234L, 4445L, 4927L, 4927L, 4652L, 4032L, 3551L, 5340L, 6303L, 5821L, 6440L, 6028L, 5821L, 4032L, 6028L, 4927L, 5752L, 4032L, 4376L, 5890L, 4032L, 6441L, 3826L, 4720L, 3069L, 6234L, 6165L, 4996L, 3482L, 5133L, 3276L, 4858L, 6234L, 3688L, 4170L, 5064L, 4996L, 4239L, 6440L, 5890L, 5959L, 4789L, 3688L, 3138L, 3138L, 5615L, 3482L, 5340L, 3620L, 5271L, 6372L, 4514L, 6372L, 6372L, 4583L, 4239L, 4308L, 3207L, 3413L, 4239L, 3551L, 5408L, 3620L, 4652L, 6441L, 4445L, 3482L, 5271L, 6165L, 5133L, 5133L, 4996L, 3757L, 6028L, 6441L, 4996L, 6303L, 5202L, 5340L, 5615L), sdimy = c(-4125L, -2808L, -5202L, -5322L, -4604L, -3047L, -4724L, -2928L, -3047L, -4125L, -2688L, -3047L, -4125L, -4005L, -4125L, -2569L, -3167L, -3646L, -4724L, -4484L, -5442L, -4604L, -4125L, -4843L, -3526L, -5202L, -2808L, -4005L, -4245L, -3526L, -2568L, -5082L, -3885L, -4723L, -4125L, -2928L, -4125L, -3526L, -4245L, -4963L, -4484L, -3766L, -3287L, -4843L, -2808L, -4604L, -5083L, -4244L, -5202L, -3287L, -5322L, -3047L, -5202L, -3287L, -2928L, -4245L, -2928L, -4244L, -3167L, -4484L, -5202L, -4843L, -2808L, -2928L, -4484L, -4723L, -4604L, -4484L, -3886L, -4364L, -4604L, -3167L, -4125L, -5083L, -3047L, -4723L, -3766L, -4484L, -4963L, -3885L, -3047L, -3287L, -4245L, -2808L, -3167L, -3167L, -4604L, -3047L, -4364L, -3287L, -5442L, -5202L, -2808L, -2688L, 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