Mercurial > repos > artbio > gsc_high_dimensions_visualisation
changeset 9:58aa18e1fe14 draft default tip
planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/gsc_high_dimension_visualization commit 7343be2d3b1b8cb1ba0c4c55767b60dbce8f8b22
| author | artbio |
|---|---|
| date | Thu, 07 Nov 2024 22:43:01 +0000 |
| parents | fe6f76030168 |
| children | |
| files | high_dim_visu.R high_dim_visu.xml |
| diffstat | 2 files changed, 396 insertions(+), 368 deletions(-) [+] |
line wrap: on
line diff
--- a/high_dim_visu.R Wed Nov 29 17:28:18 2023 +0000 +++ b/high_dim_visu.R Thu Nov 07 22:43:01 2024 +0000 @@ -1,8 +1,9 @@ -options(show.error.messages = FALSE, - error = function() { - cat(geterrmessage(), file = stderr()) - q("no", 1, FALSE) - } +options( + show.error.messages = FALSE, + error = function() { + cat(geterrmessage(), file = stderr()) + q("no", 1, FALSE) + } ) loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8") warnings() @@ -19,397 +20,421 @@ library(Polychrome) option_list <- list( - make_option( - "--data", - default = NA, - type = "character", - help = "Input file that contains expression value to visualise" - ), - make_option( - "--labels", - default = FALSE, - type = "logical", - help = "add labels in scatter plots [default : '%default' ]" - ), - make_option( - "--factor", - default = "", - type = "character", - help = "A two column table that specifies factor levels for contrasting data [default : '%default' ]" - ), - make_option( - "--visu_choice", - default = "PCA", - type = "character", - help = "visualisation method ('PCA', 'tSNE', 'HCPC') [default : '%default' ]" - ), - make_option( - "--Rtsne_seed", - default = 42, - type = "integer", - help = "Seed value for reproducibility [default : '%default' ]" - ), - make_option( - "--Rtsne_dims", - default = 2, - type = "integer", - help = "Output dimensionality [default : '%default' ]" - ), - make_option( - "--Rtsne_initial_dims", - default = 50, - type = "integer", - help = "The number of dimensions that should be retained in the initial PCA step [default : '%default' ]" - ), - make_option( - "--Rtsne_perplexity", - default = 5.0, - type = "numeric", - help = "perplexity [default : '%default' ]" - ), - make_option( - "--Rtsne_theta", - default = 1.0, - type = "numeric", - help = "theta [default : '%default' ]" - ), - make_option( - "--Rtsne_max_iter", - default = 1000, - type = "integer", - help = "max_iter [default : '%default' ]" - ), - make_option( - "--Rtsne_pca", - default = TRUE, - type = "logical", - help = "Whether an initial PCA step should be performed [default : '%default' ]" - ), - make_option( - "--Rtsne_pca_center", - default = TRUE, - type = "logical", - help = "Should data be centered before pca is applied? [default : '%default' ]" - ), - make_option( - "--Rtsne_pca_scale", - default = FALSE, - type = "logical", - help = "Should data be scaled before pca is applied? [default : '%default' ]" - ), - make_option( - "--Rtsne_normalize", - default = TRUE, - type = "logical", - help = "Should data be normalized internally prior to distance calculations? [default : '%default' ]" - ), - make_option( - "--Rtsne_exaggeration_factor", - default = 12.0, - type = "numeric", - help = " Exaggeration factor used to multiply the P matrix in the first part of the optimization [default : '%default' ]" - ), - make_option( - "--PCA_npc", - default = 5, - type = "integer", - help = "number of dimensions kept in the results [default : '%default' ]" - ), - make_option( - "--item_size", - default = 1, - type = "numeric", - help = "Size of points/labels in PCA [default : '%default' ]" - ), - make_option( - "--x_axis", - default = 1, - type = "integer", - help = "PC to plot in the x axis [default : '%default' ]" - ), - make_option( - "--y_axis", - default = 2, - type = "integer", - help = "PC to plot in the y axis [default : '%default' ]" - ), - make_option( - "--HCPC_ncluster", - default = -1, - type = "numeric", - help = "nb.clust, number of clusters to consider in the hierarchical clustering. [default : -1 let HCPC to optimize the number]" - ), - make_option( - "--HCPC_npc", - default = 5, - type = "integer", - help = "npc, number of dimensions which are kept for HCPC analysis [default : '%default' ]" - ), - make_option( - "--HCPC_metric", - default = "euclidean", - type = "character", - help = "Metric to be used for calculating dissimilarities between observations, available 'euclidean' or 'manhattan' [default : '%default' ]" - ), - make_option( - "--HCPC_method", - default = "ward", - type = "character", - help = "Clustering method between 'ward','average','single', 'complete', 'weighted' [default :'%default']" - ), - make_option( - "--pdf_out", - default = "out.pdf", - type = "character", - help = "pdf of plots [default : '%default' ]" - ), - make_option( - "--HCPC_consol", - default = "TRUE", - type = "logical", - help = "If TRUE, a k-means consolidation is performed [default :'%default']" - ), - make_option( - "--HCPC_itermax", - default = "10", - type = "integer", - help = "The maximum number of iterations for the consolidation [default :'%default']" - ), - make_option( - "--HCPC_min", - default = "3", - type = "integer", - help = "The least possible number of clusters suggested [default :'%default']" - ), - make_option( - "--HCPC_max", - default = -1, - type = "integer", - help = "The higher possible number of clusters suggested [default :'%default']" - ), - make_option( - "--HCPC_clusterCA", - default = "rows", - type = "character", - help = "A string equals to 'rows' or 'columns' for the clustering of Correspondence Analysis results [default :'%default']" - ), - make_option( - "--HCPC_kk", - default = Inf, - type = "numeric", - help = "The maximum number of iterations for the consolidation [default :'%default']" - ), - make_option( - "--HCPC_mutual_info", - default = "", - type = "character", - help = "Output file of external validation of HCPC clustering with factor levels [default :'%default']" - ), - make_option( - "--HCPC_cell_clust", - default = "", - type = "character", - help = "Lists cells in the clusters generated by HCPC clustering. 2-column table (cell identifiers/clusters) [default :'%default']" - ), - make_option( - "--HCPC_contributions", - default = "", - type = "character", - help = "Table of variables (genes) most contributing to HCPC clustering [default :'%default']" - ) + make_option( + "--data", + default = NA, + type = "character", + help = "Input file that contains expression value to visualise" + ), + make_option( + "--labels", + default = FALSE, + type = "logical", + help = "add labels in scatter plots [default : '%default' ]" + ), + make_option( + "--factor", + default = "", + type = "character", + help = "A two column table that specifies factor levels for contrasting data [default : '%default' ]" + ), + make_option( + "--visu_choice", + default = "PCA", + type = "character", + help = "visualisation method ('PCA', 'tSNE', 'HCPC') [default : '%default' ]" + ), + make_option( + "--Rtsne_seed", + default = 42, + type = "integer", + help = "Seed value for reproducibility [default : '%default' ]" + ), + make_option( + "--Rtsne_dims", + default = 2, + type = "integer", + help = "Output dimensionality [default : '%default' ]" + ), + make_option( + "--Rtsne_initial_dims", + default = 50, + type = "integer", + help = "The number of dimensions that should be retained in the initial PCA step [default : '%default' ]" + ), + make_option( + "--Rtsne_perplexity", + default = 5.0, + type = "numeric", + help = "perplexity [default : '%default' ]" + ), + make_option( + "--Rtsne_theta", + default = 1.0, + type = "numeric", + help = "theta [default : '%default' ]" + ), + make_option( + "--Rtsne_max_iter", + default = 1000, + type = "integer", + help = "max_iter [default : '%default' ]" + ), + make_option( + "--Rtsne_pca", + default = TRUE, + type = "logical", + help = "Whether an initial PCA step should be performed [default : '%default' ]" + ), + make_option( + "--Rtsne_pca_center", + default = TRUE, + type = "logical", + help = "Should data be centered before pca is applied? [default : '%default' ]" + ), + make_option( + "--Rtsne_pca_scale", + default = FALSE, + type = "logical", + help = "Should data be scaled before pca is applied? [default : '%default' ]" + ), + make_option( + "--Rtsne_normalize", + default = TRUE, + type = "logical", + help = "Should data be normalized internally prior to distance calculations? [default : '%default' ]" + ), + make_option( + "--Rtsne_exaggeration_factor", + default = 12.0, + type = "numeric", + help = " Exaggeration factor used to multiply the P matrix in the first part of the optimization [default : '%default' ]" + ), + make_option( + "--PCA_npc", + default = 5, + type = "integer", + help = "number of dimensions kept in the results [default : '%default' ]" + ), + make_option( + "--item_size", + default = 1, + type = "numeric", + help = "Size of points/labels in PCA [default : '%default' ]" + ), + make_option( + "--x_axis", + default = 1, + type = "integer", + help = "PC to plot in the x axis [default : '%default' ]" + ), + make_option( + "--y_axis", + default = 2, + type = "integer", + help = "PC to plot in the y axis [default : '%default' ]" + ), + make_option( + "--HCPC_ncluster", + default = -1, + type = "numeric", + help = "nb.clust, number of clusters to consider in the hierarchical clustering. [default : -1 let HCPC to optimize the number]" + ), + make_option( + "--HCPC_npc", + default = 5, + type = "integer", + help = "npc, number of dimensions which are kept for HCPC analysis [default : '%default' ]" + ), + make_option( + "--HCPC_metric", + default = "euclidean", + type = "character", + help = "Metric to be used for calculating dissimilarities between observations, available 'euclidean' or 'manhattan' [default : '%default' ]" + ), + make_option( + "--HCPC_method", + default = "ward", + type = "character", + help = "Clustering method between 'ward','average','single', 'complete', 'weighted' [default :'%default']" + ), + make_option( + "--pdf_out", + default = "out.pdf", + type = "character", + help = "pdf of plots [default : '%default' ]" + ), + make_option( + "--HCPC_consol", + default = "TRUE", + type = "logical", + help = "If TRUE, a k-means consolidation is performed [default :'%default']" + ), + make_option( + "--HCPC_itermax", + default = "10", + type = "integer", + help = "The maximum number of iterations for the consolidation [default :'%default']" + ), + make_option( + "--HCPC_min", + default = "3", + type = "integer", + help = "The least possible number of clusters suggested [default :'%default']" + ), + make_option( + "--HCPC_max", + default = -1, + type = "integer", + help = "The higher possible number of clusters suggested [default :'%default']" + ), + make_option( + "--HCPC_clusterCA", + default = "rows", + type = "character", + help = "A string equals to 'rows' or 'columns' for the clustering of Correspondence Analysis results [default :'%default']" + ), + make_option( + "--HCPC_kk", + default = Inf, + type = "numeric", + help = "The maximum number of iterations for the consolidation [default :'%default']" + ), + make_option( + "--HCPC_mutual_info", + default = "", + type = "character", + help = "Output file of external validation of HCPC clustering with factor levels [default :'%default']" + ), + make_option( + "--HCPC_cell_clust", + default = "", + type = "character", + help = "Lists cells in the clusters generated by HCPC clustering. 2-column table (cell identifiers/clusters) [default :'%default']" + ), + make_option( + "--HCPC_contributions", + default = "", + type = "character", + help = "Table of variables (genes) most contributing to HCPC clustering [default :'%default']" + ) ) opt <- parse_args(OptionParser(option_list = option_list), - args = commandArgs(trailingOnly = TRUE)) + args = commandArgs(trailingOnly = TRUE) +) if (opt$HCPC_max == -1) { - opt$HCPC_max <- NULL + opt$HCPC_max <- NULL } if (opt$HCPC_kk == -1) { - opt$HCPC_kk <- Inf + opt$HCPC_kk <- Inf } #### We treat data once, at the beginning of the script #### data <- read.delim( - opt$data, - check.names = FALSE, - header = TRUE, - row.names = 1, - sep = "\t" + opt$data, + check.names = FALSE, + header = TRUE, + row.names = 1, + sep = "\t" ) # we transpose immediately, because this is the common data structure data <- as.data.frame(t(data)) # we treat the factor for usage in 3 methods if (opt$factor != "") { - contrasting_factor <- read.delim(opt$factor, header = TRUE) - rownames(contrasting_factor) <- contrasting_factor[, 1] - # we pick only the relevant values of the contrasting factor - contrasting_factor <- contrasting_factor[rownames(data), ] - sup <- colnames(contrasting_factor)[2] - if (!is.numeric(contrasting_factor[, 2])) { - contrasting_factor[, 2] <- as.factor(contrasting_factor[, 2]) - } + contrasting_factor <- read.delim(opt$factor, header = TRUE) + rownames(contrasting_factor) <- contrasting_factor[, 1] + # we pick only the relevant values of the contrasting factor + contrasting_factor <- contrasting_factor[rownames(data), ] + sup <- colnames(contrasting_factor)[2] + if (!is.numeric(contrasting_factor[, 2])) { + contrasting_factor[, 2] <- as.factor(contrasting_factor[, 2]) + } } ######### make PCA with FactoMineR ################# if (opt$visu_choice == "PCA") { - if (opt$labels) { - labels <- "ind" - } else { - labels <- "none" - } - pdf(opt$pdf_out) - if (opt$factor != "") { - data <- cbind(data, contrasting_factor[, 2]) - colnames(data)[length(data)] <- sup - if (is.numeric(contrasting_factor[, 2])) { - res_pca <- PCA(X = data, quanti.sup = sup, graph = FALSE) - pca_plot <- plot(res_pca, habillage = sup, label = labels, - title = "PCA graph of cells", cex = opt$item_size, - axes = c(opt$x_axis, opt$y_axis)) + if (opt$labels) { + labels <- "ind" } else { - res_pca <- PCA(X = data, quali.sup = sup, graph = FALSE) - pca_plot <- plot(res_pca, habillage = sup, label = labels, - title = "PCA graph of cells", cex = opt$item_size, - axes = c(opt$x_axis, opt$y_axis)) + labels <- "none" } - } else { - res_pca <- PCA(X = data, graph = FALSE) - pca_plot <- plot(res_pca, label = labels, - title = "PCA graph of cells", cex = opt$item_size, - axes = c(opt$x_axis, opt$y_axis), col.ind = "deepskyblue4") - } - print(pca_plot) - dev.off() + pdf(opt$pdf_out) + if (opt$factor != "") { + data <- cbind(data, contrasting_factor[, 2]) + colnames(data)[length(data)] <- sup + if (is.numeric(contrasting_factor[, 2])) { + res_pca <- PCA(X = data, quanti.sup = sup, graph = FALSE) + pca_plot <- plot(res_pca, + habillage = sup, label = labels, + title = "PCA graph of cells", cex = opt$item_size, + axes = c(opt$x_axis, opt$y_axis) + ) + } else { + res_pca <- PCA(X = data, quali.sup = sup, graph = FALSE) + pca_plot <- plot(res_pca, + habillage = sup, label = labels, + title = "PCA graph of cells", cex = opt$item_size, + axes = c(opt$x_axis, opt$y_axis) + ) + } + } else { + res_pca <- PCA(X = data, graph = FALSE) + pca_plot <- plot(res_pca, + label = labels, + title = "PCA graph of cells", cex = opt$item_size, + axes = c(opt$x_axis, opt$y_axis), col.ind = "deepskyblue4" + ) + } + print(pca_plot) + dev.off() } ########### make HCPC with FactoMineR ########## if (opt$visu_choice == "HCPC") { - pdf(opt$pdf_out) - # HCPC starts with a PCA - pca <- PCA(X = data, ncp = opt$HCPC_npc, graph = FALSE) - pca_ind_coord <- as.data.frame(pca$ind$coord) # coordinates of observations in PCA - # Hierarchical Clustering On Principal Components Followed By Kmean Clustering - res_hcpc <- HCPC(pca, - nb.clust = opt$HCPC_ncluster, - metric = opt$HCPC_metric, - method = opt$HCPC_method, - graph = FALSE, - consol = opt$HCPC_consol, - iter.max = opt$HCPC_itermax, - min = opt$HCPC_min, - max = opt$HCPC_max, - cluster.CA = opt$HCPC_clusterCA, - kk = opt$HCPC_kk) - # HCPC plots - dims <- head(as.data.frame(res_hcpc$call$t$res$eig), 2) # dims variances in column 2 - plot(res_hcpc, choice = "tree") - plot(res_hcpc, choice = "bar") - if (opt$labels == FALSE) { - plot(res_hcpc, choice = "3D.map", ind.names = FALSE) - plot(res_hcpc, choice = "map", label = "none") - } else { - plot(res_hcpc, choice = "3D.map") - plot(res_hcpc, choice = "map") - } - ## Normalized Mutual Information - if (opt$factor != "") { - sink(opt$HCPC_mutual_info) - cat("Relationship between input factor and its levels and the HCPC clusters") - res <- external_validation(true_labels = as.numeric(contrasting_factor[, 2]), - clusters = as.numeric(res_hcpc$data.clust$clust), - summary_stats = TRUE) - sink() - } - dev.off() + pdf(opt$pdf_out) + # HCPC starts with a PCA + pca <- PCA(X = data, ncp = opt$HCPC_npc, graph = FALSE) + pca_ind_coord <- as.data.frame(pca$ind$coord) # coordinates of observations in PCA + # Hierarchical Clustering On Principal Components Followed By Kmean Clustering + res_hcpc <- HCPC(pca, + nb.clust = opt$HCPC_ncluster, + metric = opt$HCPC_metric, + method = opt$HCPC_method, + graph = FALSE, + consol = opt$HCPC_consol, + iter.max = opt$HCPC_itermax, + min = opt$HCPC_min, + max = opt$HCPC_max, + cluster.CA = opt$HCPC_clusterCA, + kk = opt$HCPC_kk + ) + # HCPC plots + dims <- head(as.data.frame(res_hcpc$call$t$res$eig), 2) # dims variances in column 2 + plot(res_hcpc, choice = "tree") + plot(res_hcpc, choice = "bar") + if (opt$labels == FALSE) { + plot(res_hcpc, choice = "3D.map", ind.names = FALSE) + plot(res_hcpc, choice = "map", label = "none") + } else { + plot(res_hcpc, choice = "3D.map") + plot(res_hcpc, choice = "map") + } + ## Normalized Mutual Information + if (opt$factor != "") { + sink(opt$HCPC_mutual_info) + cat("Relationship between input factor and its levels and the HCPC clusters") + res <- external_validation( + true_labels = as.numeric(contrasting_factor[, 2]), + clusters = as.numeric(res_hcpc$data.clust$clust), + summary_stats = TRUE + ) + sink() + } + dev.off() - res_clustering <- data.frame(Cell = rownames(res_hcpc$data.clust), - Cluster = res_hcpc$data.clust$clust) - # Description of cluster by most contributing variables / gene expressions - # first transform list of vectors in a list of dataframes - extract_description <- lapply(res_hcpc$desc.var$quanti, as.data.frame) - # second, transfer rownames (genes) to column in the dataframe, before rbinding - extract_description_w_genes <- Map(cbind, - extract_description, - genes = lapply(extract_description, rownames)) - # Then collapse all dataframes with cluster_id in 1st column using {data.table} rbindlist() - cluster_description <- rbindlist(extract_description_w_genes, idcol = "cluster_id") - cluster_description <- cluster_description[, c(8, 1, 2, 3, 4, 5, 6, 7)] # swap columns - cluster_description <- cluster_description[order(cluster_description[[2]], - cluster_description[[8]]), ] # sort by cluster then by pval - # Finally, output cluster description data frame - write.table(cluster_description, - file = opt$HCPC_contributions, - sep = "\t", - quote = FALSE, - col.names = TRUE, - row.names = FALSE) + res_clustering <- data.frame( + Cell = rownames(res_hcpc$data.clust), + Cluster = res_hcpc$data.clust$clust + ) + # Description of cluster by most contributing variables / gene expressions + # first transform list of vectors in a list of dataframes + extract_description <- lapply(res_hcpc$desc.var$quanti, as.data.frame) + # second, transfer rownames (genes) to column in the dataframe, before rbinding + extract_description_w_genes <- Map(cbind, + extract_description, + genes = lapply(extract_description, rownames) + ) + # Then collapse all dataframes with cluster_id in 1st column using {data.table} rbindlist() + cluster_description <- rbindlist(extract_description_w_genes, idcol = "cluster_id") + cluster_description <- cluster_description[, c(8, 1, 2, 3, 4, 5, 6, 7)] # swap columns + cluster_description <- cluster_description[order( + cluster_description[[2]], + cluster_description[[8]] + ), ] # sort by cluster then by pval + # Finally, output cluster description data frame + write.table(cluster_description, + file = opt$HCPC_contributions, + sep = "\t", + quote = FALSE, + col.names = TRUE, + row.names = FALSE + ) - ## Return cluster table to user - write.table(res_clustering, - file = opt$HCPC_cell_clust, - sep = "\t", - quote = FALSE, - col.names = TRUE, - row.names = FALSE) + ## Return cluster table to user + write.table(res_clustering, + file = opt$HCPC_cell_clust, + sep = "\t", + quote = FALSE, + col.names = TRUE, + row.names = FALSE + ) } ################ t-SNE #################### if (opt$visu_choice == "tSNE") { - set.seed(opt$Rtsne_seed) ## Sets seed for reproducibility - tsne_out <- Rtsne(data, - dims = opt$Rtsne_dims, - initial_dims = opt$Rtsne_initial_dims, - perplexity = opt$Rtsne_perplexity, - theta = opt$Rtsne_theta, - max_iter = opt$Rtsne_max_iter, - pca = opt$Rtsne_pca, - pca_center = opt$Rtsne_pca_center, - pca_scale = opt$Rtsne_pca_scale, - normalize = opt$Rtsne_normalize, - exaggeration_factor = opt$Rtsne_exaggeration_factor) - embedding <- as.data.frame(tsne_out$Y[, 1:2]) - embedding$Class <- as.factor(rownames(data)) - gg_legend <- theme(legend.position = "right") - pointcolor <- "#E70000" - pointsize <- opt$item_size * 1.5 - the_theme <- theme( - panel.background = element_rect(fill = "gray100", colour = "#6D9EC1", - size = 2, linetype = "solid"), - panel.grid.major = element_line(size = 0.5, linetype = "solid", - colour = "#6D9EC1"), - panel.grid.minor = element_line(size = 0.25, linetype = "solid", - colour = "darkslategray3") - ) - if (opt$factor == "") { - p <- ggplot(embedding, aes(x = V1, y = V2)) + - geom_point(size = pointsize * 0.25, color = pointcolor) + - gg_legend + - xlab("t-SNE 1") + - ylab("t-SNE 2") + - ggtitle("t-SNE") + - the_theme + - if (opt$labels) { - geom_text(aes(label = Class), hjust = -0.2, vjust = -0.5, size = pointsize, color = pointcolor) - } - } else { - if (is.numeric(contrasting_factor[, 2])) { - embedding$factor <- contrasting_factor[, 2] + set.seed(opt$Rtsne_seed) ## Sets seed for reproducibility + tsne_out <- Rtsne(data, + dims = opt$Rtsne_dims, + initial_dims = opt$Rtsne_initial_dims, + perplexity = opt$Rtsne_perplexity, + theta = opt$Rtsne_theta, + max_iter = opt$Rtsne_max_iter, + pca = opt$Rtsne_pca, + pca_center = opt$Rtsne_pca_center, + pca_scale = opt$Rtsne_pca_scale, + normalize = opt$Rtsne_normalize, + exaggeration_factor = opt$Rtsne_exaggeration_factor + ) + embedding <- as.data.frame(tsne_out$Y[, 1:2]) + embedding$Class <- as.factor(rownames(data)) + gg_legend <- theme(legend.position = "right") + pointcolor <- "#E70000" + pointsize <- opt$item_size * 1.5 + the_theme <- theme( + panel.background = element_rect( + fill = "gray100", colour = "#6D9EC1", + size = 2, linetype = "solid" + ), + panel.grid.major = element_line( + size = 0.5, linetype = "solid", + colour = "#6D9EC1" + ), + panel.grid.minor = element_line( + size = 0.25, linetype = "solid", + colour = "darkslategray3" + ) + ) + if (opt$factor == "") { + p <- ggplot(embedding, aes(x = V1, y = V2)) + + geom_point(size = pointsize * 0.25, color = pointcolor) + + gg_legend + + xlab("t-SNE 1") + + ylab("t-SNE 2") + + ggtitle("t-SNE") + + the_theme + + if (opt$labels) { + geom_text(aes(label = Class), hjust = -0.2, vjust = -0.5, size = pointsize, color = pointcolor) + } } else { - embedding$factor <- as.factor(contrasting_factor[, 2]) + if (is.numeric(contrasting_factor[, 2])) { + embedding$factor <- contrasting_factor[, 2] + } else { + embedding$factor <- as.factor(contrasting_factor[, 2]) + } + p <- ggplot(embedding, aes(x = V1, y = V2, color = factor)) + + geom_point(size = pointsize * 0.25) + + gg_legend + + xlab("t-SNE 1") + + ylab("t-SNE 2") + + ggtitle("t-SNE") + + the_theme + + if (opt$labels) { + geom_text(aes(label = Class, colour = factor), hjust = -0.2, vjust = -0.5, size = pointsize) + } } - p <- ggplot(embedding, aes(x = V1, y = V2, color = factor)) + - geom_point(size = pointsize * 0.25) + - gg_legend + - xlab("t-SNE 1") + - ylab("t-SNE 2") + - ggtitle("t-SNE") + - the_theme + - if (opt$labels) { - geom_text(aes(label = Class, colour = factor), hjust = -0.2, vjust = -0.5, size = pointsize) - } - } - pdf(opt$pdf_out) - print(p) - dev.off() + pdf(opt$pdf_out) + print(p) + dev.off() }
--- a/high_dim_visu.xml Wed Nov 29 17:28:18 2023 +0000 +++ b/high_dim_visu.xml Thu Nov 07 22:43:01 2024 +0000 @@ -1,5 +1,8 @@ -<tool id="high_dimensions_visualisation" name="Generate PCA, tSNE and HCPC" version="4.3+galaxy0" profile="20.01"> +<tool id="high_dimensions_visualisation" name="Generate PCA, tSNE and HCPC" version="4.3+galaxy1" profile="20.01"> <description>from highly dimensional expression data</description> + <xrefs> + <xref type="bio.tools">galaxy_single_cell_suite</xref> + </xrefs> <requirements> <requirement type="package" version="1.7.3=r43hc72bb7e_2">r-optparse</requirement> <requirement type="package" version="2.9=r43h57805ef_0">r-factominer</requirement> @@ -236,7 +239,7 @@ <param name="visu_choice" value="PCA" /> <param name="factor_choice" value="No" /> <param name="item_size" value="0.5" /> - <output name="pdf_out" file="pca.1.pdf" ftype="pdf"/> + <output name="pdf_out" file="pca.1.pdf" ftype="pdf" compare="sim_size" delta="1000"/> </test> <!-- test PCA PC2 vs PC3 --> <test expect_num_outputs="1"> @@ -246,7 +249,7 @@ <param name="factor_choice" value="No" /> <param name="x_axis" value="2" /> <param name="y_axis" value="3" /> - <output name="pdf_out" file="pca.2.pdf" ftype="pdf"/> + <output name="pdf_out" file="pca.2.pdf" ftype="pdf" compare="sim_size" delta="1000"/> </test> <!-- test factor contrasting on PCA --> <test expect_num_outputs="1"> @@ -255,7 +258,7 @@ <param name="visu_choice" value="PCA" /> <param name="factor_choice" value="Yes" /> <param name="factor" value="factor.tsv" ftype="txt"/> - <output name="pdf_out" file="pca.3.pdf" ftype="pdf"/> + <output name="pdf_out" file="pca.3.pdf" ftype="pdf" compare="sim_size" delta="1000"/> </test> <!-- test numerical factor contrasting on PCA --> <test expect_num_outputs="1">