Mercurial > repos > artbio > gsc_high_dimensions_visualisation
changeset 4:8e17c31c536a draft
planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_high_dimension_visualization commit 1282ac9de7c926ab251f88afb2453f52c8b14200
| author | artbio |
|---|---|
| date | Thu, 11 Jul 2019 12:31:28 -0400 |
| parents | 8e44c9e18a56 |
| children | 569334568afa |
| files | high_dim_visu.R high_dim_visu.xml test-data/hcpc-2.coord.tab test-data/hcpc-2.labels.pdf test-data/hcpc-3.coord.tab test-data/hcpc-3.labels.pdf test-data/hcpc.clusters.tab test-data/hcpc.factor.extval.txt test-data/hcpc.labels.pdf test-data/hcpc.nolabels.factor.pdf test-data/hcpc.nolabels.pdf test-data/numeric_factor.tsv test-data/pca.2vs3.pdf test-data/pca.coord.pdf test-data/pca.coord.tab test-data/pca.labels.pdf test-data/pca.nolabels.2-lev-factor.pdf test-data/pca.nolabels.factors.pdf test-data/pca.nolabels.numerical-factor.pdf test-data/pca.nolabels.pdf test-data/tsne-2.coord.tab test-data/tsne-2.nolabels.pdf test-data/tsne.labels.factor.pdf test-data/tsne.labels.pdf test-data/tsne.nolabels.pdf |
| diffstat | 25 files changed, 6458 insertions(+), 4208 deletions(-) [+] |
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--- a/high_dim_visu.R Thu Jun 27 06:17:08 2019 -0400 +++ b/high_dim_visu.R Thu Jul 11 12:31:28 2019 -0400 @@ -11,7 +11,7 @@ library(ggplot2) library(ggfortify) library(RColorBrewer) - +library(ClusterR) # Arguments option_list = list( @@ -92,13 +92,13 @@ 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, @@ -128,13 +128,25 @@ 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( + "--PCA_x_axis", + default = 1, + type = 'integer', + help = "PC to plot in the x axis [default : '%default' ]" + ), + make_option( + "--PCA_y_axis", + default = 2, + type = 'integer', + help = "PC to plot in the y axis [default : '%default' ]" + ), make_option( "--HCPC_ncluster", default = -1, @@ -200,7 +212,19 @@ default = -1, type = 'numeric', help = "The maximum number of iterations for the consolidation [default :'%default']" - ) + ), + make_option( + "--HCPC_clust", + default = "", + type = 'character', + help = "Output result of HCPC clustering : two column table (cell identifiers and clusters) [default :'%default']" + ), + make_option( + "--mutual_info", + default = "", + type = "character", + help = "Output file of external validation of HCPC clustering with factor levels [default :'%default']" + ) ) opt = parse_args(OptionParser(option_list = option_list), @@ -211,6 +235,43 @@ if(opt$HCPC_max == -1) {opt$HCPC_max <- NULL} if(opt$HCPC_kk == -1) {opt$HCPC_kk <- Inf} +##Add legend to plot() +legend.col <- function(col, lev){ + +opar <- par + +n <- length(col) + +bx <- par("usr") + +box.cx <- c(bx[2] + (bx[2] - bx[1]) / 1000, +bx[2] + (bx[2] - bx[1]) / 1000 + (bx[2] - bx[1]) / 50) +box.cy <- c(bx[3], bx[3]) +box.sy <- (bx[4] - bx[3]) / n + +xx <- rep(box.cx, each = 2) + +par(xpd = TRUE) +for(i in 1:n){ + +yy <- c(box.cy[1] + (box.sy * (i - 1)), +box.cy[1] + (box.sy * (i)), +box.cy[1] + (box.sy * (i)), +box.cy[1] + (box.sy * (i - 1))) +polygon(xx, yy, col = col[i], border = col[i]) + +} +par(new = TRUE) +plot(0, 0, type = "n", +ylim = c(min(lev), max(lev)), +yaxt = "n", ylab = "", +xaxt = "n", xlab = "", +frame.plot = FALSE) +axis(side = 4, las = 2, tick = FALSE, line = .25) +par <- opar +} + + data = read.table( opt$data, check.names = FALSE, @@ -228,20 +289,24 @@ rownames(contrasting_factor) <- contrasting_factor[,1] contrasting_factor <- contrasting_factor[colnames(data),] colnames(contrasting_factor) <- c("id","factor") - contrasting_factor$factor <- as.factor(contrasting_factor$factor) - if(nlevels(contrasting_factor$factor) == 2){ - colors_labels <- c("#E41A1C", "#377EB8") + if(is.numeric(contrasting_factor$factor)){ + factor_cols <- rev(brewer.pal(n = 11, name = "RdYlGn"))[contrasting_factor$factor] } else { - colors_labels <- brewer.pal(nlevels(contrasting_factor$factor), name = 'Paired') + contrasting_factor$factor <- as.factor(contrasting_factor$factor) + if(nlevels(contrasting_factor$factor) == 2){ + colors_labels <- c("#E41A1C", "#377EB8") + } else { + colors_labels <- brewer.pal(nlevels(contrasting_factor$factor), name = 'Paired') + } + factorColors <- + with(contrasting_factor, + data.frame(factor = levels(contrasting_factor$factor), + color = I(colors_labels) + ) + ) + factor_cols <- factorColors$color[match(contrasting_factor$factor, + factorColors$factor)] } - factorColors <- - with(contrasting_factor, - data.frame(factor = levels(contrasting_factor$factor), - color = I(colors_labels) - ) - ) - factor_cols <- factorColors$color[match(contrasting_factor$factor, - factorColors$factor)] } else { factor_cols <- rep("deepskyblue4", length(rownames(data))) } @@ -272,19 +337,24 @@ ggplot(embedding, aes(x=V1, y=V2)) + geom_point(size=1, color='deepskyblue4') + gg_legend + - xlab("") + - ylab("") + + xlab("t-SNE 1") + + ylab("t-SNE 2") + ggtitle('t-SNE') + if (opt$labels) { geom_text(aes(label=Class),hjust=-0.2, vjust=-0.5, size=1.5, color='deepskyblue4') } } else { - embedding$factor <- as.factor(contrasting_factor$factor) + if(is.numeric(contrasting_factor$factor)){ + embedding$factor <- contrasting_factor$factor + } else { + embedding$factor <- as.factor(contrasting_factor$factor) + } + ggplot(embedding, aes(x=V1, y=V2, color=factor)) + geom_point(size=1) + #, color=factor_cols gg_legend + - xlab("") + - ylab("") + + xlab("t-SNE 1") + + ylab("t-SNE 2") + ggtitle('t-SNE') + if (opt$labels) { geom_text(aes(label=Class, colour=factor),hjust=-0.2, vjust=-0.5, size=1.5) @@ -294,7 +364,7 @@ #save coordinates table if(opt$table_coordinates != ''){ - coord_table <- cbind(rownames(tdf),as.data.frame(tsne_out$Y)) + coord_table <- cbind(rownames(tdf), round(as.data.frame(tsne_out$Y), 6)) colnames(coord_table)=c("Cells",paste0("DIM",(1:opt$Rtsne_dims))) } } @@ -305,20 +375,24 @@ pca <- PCA(t(data), ncp=opt$PCA_npc, graph=FALSE) pdf(opt$pdf_out) if (opt$labels == FALSE) { - plot(pca, label="none" , col.ind = factor_cols) + plot(pca, axes = c(opt$PCA_x_axis,opt$PCA_y_axis), label="none" , col.ind = factor_cols) } else { - plot(pca, cex=0.2 , col.ind = factor_cols) + plot(pca, axes = c(opt$PCA_x_axis,opt$PCA_y_axis), cex=0.2 , col.ind = factor_cols) } if (opt$factor != '') { + if(is.factor(contrasting_factor$factor)) { legend(x = 'topright', legend = as.character(factorColors$factor), col = factorColors$color, pch = 16, bty = 'n', xjust = 1, cex=0.7) + } else { + legend.col(col = rev(brewer.pal(n = 11, name = "RdYlGn")), lev = cut(contrasting_factor$factor, 11, label = FALSE)) + } } dev.off() #save coordinates table if(opt$table_coordinates != ''){ - coord_table <- cbind(rownames(pca$ind$coord),as.data.frame(pca$ind$coord)) + coord_table <- cbind(rownames(pca$ind$coord), round(as.data.frame(pca$ind$coord), 6)) colnames(coord_table)=c("Cells",paste0("DIM",(1:opt$PCA_npc))) } @@ -355,11 +429,25 @@ # user contrasts on the pca if (opt$factor != '') { - plot(pca, label="none", habillage="ind", col.hab=factor_cols) + plot(pca, label="none", col.ind = factor_cols) + if(is.factor(contrasting_factor$factor)) { legend(x = 'topright', legend = as.character(factorColors$factor), col = factorColors$color, pch = 16, bty = 'n', xjust = 1, cex=0.7) - } + + ## Normalized Mutual Information + sink(opt$mutual_info) + res <- external_validation( + true_labels = as.numeric(contrasting_factor$factor), + clusters = as.numeric(res.hcpc$data.clust$clust), + summary_stats = TRUE + ) + sink() + + } else { + legend.col(col = rev(brewer.pal(n = 11, name = "RdYlGn")), lev = cut(contrasting_factor$factor, 11, label = FALSE)) + } +} ## Clusters to which individual observations belong # used ? # Clust <- data.frame(Cluster = res.hcpc$data.clust[, (nrow(data) + 1)], # Observation = rownames(res.hcpc$data.clust)) @@ -380,21 +468,22 @@ # Sil = fviz_silhouette(hc.cut) # sil1 = as.data.frame(Sil$data) -## Normalized Mutual Information # to be implemented later -# sink(opt$mutual_info) -# res = external_validation( -# as.numeric(factor(metadata[, Patient])), -# as.numeric(metadata$Cluster), -# method = "adjusted_rand_index", -# summary_stats = TRUE -# ) -# sink() dev.off() if(opt$table_coordinates != ''){ - coord_table <- cbind(Cell=rownames(res.hcpc$call$X),as.data.frame(res.hcpc$call$X)) + coord_table <- cbind(Cell=rownames(res.hcpc$call$X), + round(as.data.frame(res.hcpc$call$X[, -length(res.hcpc$call$X)]), 6), + as.data.frame(res.hcpc$call$X[, length(res.hcpc$call$X)]) + ) colnames(coord_table)=c("Cells",paste0("DIM",(1:opt$HCPC_npc)),"Cluster") } + + if(opt$HCPC_clust != ""){ + res_clustering <- data.frame(Cell = rownames(res.hcpc$data.clust), + Cluster = res.hcpc$data.clust$clust) + + } + } ## Return coordinates file to user @@ -411,7 +500,16 @@ } - +if(opt$HCPC_clust != ""){ + write.table( + res_clustering, + file = opt$HCPC_clust, + sep = "\t", + quote = F, + col.names = T, + row.names = F + ) +}
--- a/high_dim_visu.xml Thu Jun 27 06:17:08 2019 -0400 +++ b/high_dim_visu.xml Thu Jul 11 12:31:28 2019 -0400 @@ -1,12 +1,12 @@ -<tool id="high_dimensions_visualisation" name="Generate PCA, tSNE and HCPC" version="0.9.3"> +<tool id="high_dimensions_visualisation" name="Generate PCA, tSNE and HCPC" version="0.9.4"> <description>from highly dimensional expression data</description> <requirements> - <requirement type="package" version="1.3.2=r3.3.2_0">r-optparse</requirement> - <requirement type="package" version="1.39=r3.3.2_0">r-factominer</requirement> - <requirement type="package" version="1.0.5=r3.3.2_0">r-factoextra</requirement> - <requirement type="package" version="0.13=r3.3.2_0">r-rtsne</requirement> - <requirement type="package" version="2.2.1=r3.3.2_0">r-ggplot2</requirement> - <requirement type="package" version="0.4.1=r3.3.2_0">r-ggfortify</requirement> + <requirement type="package" version="1.6.2=r35h6115d3f_0">r-optparse</requirement> + <requirement type="package" version="1.42=r35h6115d3f_0">r-factominer</requirement> + <requirement type="package" version="1.0.5">r-factoextra</requirement> + <requirement type="package" version="0.15=r351he1b5a44_0">r-rtsne</requirement> + <requirement type="package" version="0.4.7=r351h6115d3f_0">r-ggfortify</requirement> + <requirement type="package" version="1.1.9=r351h0357c0b_0">r-clusterr</requirement> </requirements> <stdio> <exit_code range="1:" level="fatal" description="Tool exception" /> @@ -53,13 +53,21 @@ --HCPC_max '$visualisation.HCPC_max' --HCPC_clusterCA '$visualisation.HCPC_clusterCA' --HCPC_kk '$visualisation.HCPC_kk' + #if $visualisation.res_clustering == "yes": + --HCPC_clust '$HCPC_clust' + #end if #end if #if $visualisation.visu_choice == "PCA": --PCA_npc '$visualisation.PCA_npc' + --PCA_x_axis '$visualisation.PCA_x_axis' + --PCA_y_axis '$visualisation.PCA_y_axis' #end if - + #if $visualisation.visu_choice == "HCPC" and $factor_condition.factor_choice == "Yes": + --mutual_info '$mutual_info' + #end if + --pdf_out '$pdf_out' ]]></command> @@ -111,10 +119,10 @@ <option value="TRUE">Yes</option> <option value="FALSE" selected="true">False</option> </param> - <param name="Rtsne_normalize" type="select" label="Normalisation of data" - help="Should variables (gene expressions) be normalized internally prior to distance calculations? " > - <option value="TRUE" selected="true">Yes</option> - <option value="FALSE">False</option> + <param name="Rtsne_normalize" type="select" label="Normalisation of data" + help="Should variables (gene expressions) be normalized internally prior to distance calculations? " > + <option value="TRUE" selected="true">Yes</option> + <option value="FALSE">False</option> </param> <param name="Rtsne_perplexity" value="10.0" type="float" label="perplexity (t-SNE)" help="should be less than ((nbr observations)-1)/3" /> <param name="Rtsne_theta" value="1.0" type="float" label="theta (t-SNE)"/> @@ -154,9 +162,15 @@ </param> <param name="HCPC_kk" value="-1" type="text" label="kk, Number of clusters used in a Kmeans preprocessing " help="No k-means consolidation is done if a kk value is provided (default=-1)" /> + <param label="Return HCPC clustering table" name="res_clustering" type="select"> + <option value="no" selected="True">No</option> + <option value="yes">Yes</option> + </param> </when> <when value="PCA"> <param name="PCA_npc" value="5" type="integer" label="Number of principal components to keep" help="The number of dimensions which are kept for PCA analysis (default=5)" /> + <param name="PCA_x_axis" value="1" type="integer" label="First principal component to plot" help="X axis for PCA plot (default=1)" /> + <param name="PCA_y_axis" value="2" type="integer" label="Second principal component to plot" help="Y axis for PCA plot (default=2)" /> </when> </conditional> <param label="Return scatter plot table coordinates" name="coord" type="select"> @@ -170,6 +184,12 @@ <data name="table_coordinates" format="tabular" label="Scatter plot coordinates from ${visualisation.visu_choice} of ${on_string}" > <filter>coord == 'yes'</filter> </data> + <data name="mutual_info" format="txt" label="External validation of clustering from ${visualisation.visu_choice} of ${on_string}" > + <filter>visualisation['visu_choice'] == 'HCPC' and factor_condition['factor_choice'] == 'Yes'</filter> + </data> + <data name="HCPC_clust" format="tabular" label="Clustering table from ${visualisation.visu_choice} of ${on_string}" > + <filter>visualisation['visu_choice'] == 'HCPC' and visualisation['res_clustering'] == 'yes'</filter> + </data> </outputs> <tests> <!-- test PCA --> @@ -187,6 +207,17 @@ <param name="factor_choice" value="No" /> <output name="pdf_out" file="pca.nolabels.pdf" ftype="pdf"/> </test> + <!-- test PCA PC2 vs PC3 --> + <test> + <param name="input" value="cpm_input.tsv" ftype="txt"/> + <param name="labels" value="no" /> + <param name="visu_choice" value="PCA" /> + <param name="factor_choice" value="No" /> + <param name="PCA_x_axis" value="2" /> + <param name="PCA_y_axis" value="3" /> + <output name="pdf_out" file="pca.2vs3.pdf" ftype="pdf"/> + </test> + <!-- test Coordinates tables on PCA --> <test> <param name="input" value="cpm_input.tsv" ftype="txt"/> @@ -213,7 +244,16 @@ <param name="visu_choice" value="PCA" /> <param name="factor_choice" value="Yes" /> <param name="factor" value="2-lev_factor.tsv" ftype="txt"/> - <output name="pdf_out" file="pca.nolabels.2-lev-factor.pdf" ftype="pdf"/> + <output name="pdf_out" file="pca.nolabels.2-lev-factor.pdf" compare="sim_size" ftype="pdf"/> + </test> + <!-- test numerical factor contrasting on PCA --> + <test> + <param name="input" value="cpm_input.tsv" ftype="txt"/> + <param name="labels" value="no" /> + <param name="visu_choice" value="PCA" /> + <param name="factor_choice" value="Yes" /> + <param name="factor" value="numeric_factor.tsv" ftype="txt"/> + <output name="pdf_out" file="pca.nolabels.numerical-factor.pdf" compare="sim_size" ftype="pdf"/> </test> <test> <param name="input" value="cpm_input.tsv" ftype="txt"/> @@ -221,7 +261,7 @@ <param name="visu_choice" value="PCA" /> <param name="factor_choice" value="Yes" /> <param name="factor" value="shuffled_factor.tsv" ftype="txt"/> - <output name="pdf_out" file="pca.nolabels.factors.pdf" ftype="pdf"/> + <output name="pdf_out" file="pca.nolabels.factors.pdf" compare="sim_size" ftype="pdf"/> </test> <!-- test HCPC --> <test> @@ -230,7 +270,7 @@ <param name="visu_choice" value="HCPC" /> <param name="HCPC_npc" value="5"/> <param name="HCPC_ncluster" value="-1"/> - <output name="pdf_out" file="hcpc.labels.pdf" ftype="pdf"/> + <output name="pdf_out" file="hcpc.labels.pdf" compare="sim_size" ftype="pdf"/> </test> <!-- test factor contrasting on HCPC --> <test> @@ -239,9 +279,12 @@ <param name="visu_choice" value="HCPC" /> <param name="HCPC_npc" value="5"/> <param name="HCPC_ncluster" value="-1"/> + <param name="res_clustering" value="yes"/> <param name="factor_choice" value="Yes" /> <param name="factor" value="factor.tsv" ftype="txt"/> - <output name="pdf_out" file="hcpc.nolabels.factor.pdf" ftype="pdf"/> + <output name="pdf_out" file="hcpc.nolabels.factor.pdf" compare="sim_size" ftype="pdf"/> + <output name="mutual_info" file="hcpc.factor.extval.txt" ftype="txt"/> + <output name="HCPC_clust" file="hcpc.clusters.tab" ftype="tabular"/> </test> <test> <param name="input" value="cpm_input.tsv" ftype="txt"/> @@ -249,7 +292,7 @@ <param name="HCPC_npc" value="5"/> <param name="HCPC_ncluster" value="-1"/> <param name="visu_choice" value="HCPC" /> - <output name="pdf_out" file="hcpc.nolabels.pdf" ftype="pdf"/> + <output name="pdf_out" file="hcpc.nolabels.pdf" compare="sim_size" ftype="pdf"/> </test> <test> <param name="input" value="cpm_input.tsv" ftype="txt"/> @@ -271,7 +314,7 @@ <param name="HCPC_metric" value="euclidian"/> <param name="HCPC_npc" value="4" /> <param name="HCPC_clusterCA" value="cols" /> - <output name="pdf_out" file="hcpc-3.labels.pdf" ftype="pdf"/> + <output name="pdf_out" file="hcpc-3.labels.pdf" compare="sim_size" ftype="pdf"/> <output name="table_coordinates" file="hcpc-3.coord.tab" ftype="tabular"/> </test> <!-- test t-SNE --> @@ -282,7 +325,7 @@ <param name="Rtsne_seed" value="49"/> <param name="Rtsne_perplexity" value="10"/> <param name="Rtsne_theta" value="1" /> - <output name="pdf_out" file="tsne.labels.pdf" ftype="pdf"/> + <output name="pdf_out" file="tsne.labels.pdf" ftype="pdf" compare="sim_size" delta="500"/> </test> <test> <param name="input" value="cpm_input.tsv" ftype="txt"/> @@ -291,22 +334,9 @@ <param name="Rtsne_seed" value="49"/> <param name="Rtsne_perplexity" value="10"/> <param name="Rtsne_theta" value="1" /> - <output name="pdf_out" file="tsne.nolabels.pdf" ftype="pdf"/> + <output name="pdf_out" file="tsne.nolabels.pdf" ftype="pdf" compare="sim_size" delta="500"/> </test> - <test> - <param name="input" value="cpm_input.tsv" ftype="txt"/> - <param name="labels" value="no" /> - <param name="visu_choice" value="tSNE" /> - <param name="coord" value="yes" /> - <param name="Rtsne_seed" value="42"/> - <param name="Rtsne_perplexity" value="5.0"/> - <param name="Rtsne_theta" value="1.0" /> - <param name="Rtsne_dims" value="3" /> - <param name="Rtsne_exaggeration_factor" value="15.0" /> - <output name="pdf_out" file="tsne-2.nolabels.pdf" ftype="pdf"/> - <output name="table_coordinates" file="tsne-2.coord.tab" ftype="tabular"/> - </test> - <!-- test factor contrasting on t-SNE --> + <!-- test factor contrasting on t-SNE --> <test> <param name="input" value="cpm_input.tsv" ftype="txt"/> <param name="labels" value="yes" /> @@ -316,7 +346,20 @@ <param name="Rtsne_seed" value="49"/> <param name="Rtsne_perplexity" value="10"/> <param name="Rtsne_theta" value="1" /> - <output name="pdf_out" file="tsne.labels.factor.pdf" ftype="pdf"/> + <output name="pdf_out" file="tsne.labels.factor.pdf" ftype="pdf" compare="sim_size" delta="500"/> + </test> + <test> + <param name="input" value="cpm_input.tsv" ftype="txt"/> + <param name="labels" value="no" /> + <param name="visu_choice" value="tSNE" /> + <param name="Rtsne_seed" value="49" /> + <param name="coord" value="yes" /> + <param name="Rtsne_dims" value="3" /> + <param name="Rtsne_perplexity" value="10"/> + <param name="Rtsne_theta" value="1" /> + <param name="Rtsne_normalize" value="FALSE" /> + <output name="pdf_out" file="tsne-2.nolabels.pdf" ftype="pdf" compare="sim_size" delta="500"/> + <output name="table_coordinates" file="tsne-2.coord.tab" ftype="tabular" compare="sim_size" delta="500"/> </test> </tests> <help> @@ -339,19 +382,31 @@ * Principal Components Analysis (PCA) * Hierarchical Clustering of Principal Components (HCPC) -* t-distributed Stochastic Neighbor Embedding +* t-distributed Stochastic Neighbor Embedding (t-SNE) The tool returns in addition the table of the coordinates of the observations (eg RNAseq libraries) in the low dim space, which can be used for post-treatment or to further adjust the provided visualisation. +If HCPC is used, this tool can also return the clustering table. It contains two columns of n observations : + +* Observation labels +* Cluster labels + ** Contrast data with a factor ** The tool offers the possibility to colour data points according to the levels of a factor. To use the option "Factor to contrast data", provide a tabulated-separated, two-column table with first column containing the cell/data library identifiers (same identifiers as those provided as column headers in the input data table) and second column containing the corresponding -factor levels value. This table does not need to be sorted in the same order as in the data +factor levels value (if this vector is numerical, then the color palette used is quantitative). +This table does not need to be sorted in the same order as in the data table. It may also contain more identifiers than those provided in the data table. +If HCPC visualisation and constrasting data are chosen, an additional text file is given. It contains +several metrics of external validation of clustering. It will compare the capacity of HCPC clustering +to recreate classes contained in the factor data file. If the constrasting factor is quantitative, +the file will be empty. + + </help> <citations> <citation type="bibtex">@Article{, @@ -381,5 +436,14 @@ url = {https://github.com/jkrijthe/Rtsne}, } </citation> + <citation type="bibtex">@Manual{, + title = {{ClusterR}: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, + K-Medoids and Affinity Propagation Clustering}, + author = {Lampros Mouselimis}, + year = {2019}, + note = {R package version 1.1.9}, + url = {https://github.com/mlampros/ClusterR}, + } + </citation> </citations> </tool>
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--- a/test-data/hcpc-3.coord.tab Thu Jun 27 06:17:08 2019 -0400 +++ b/test-data/hcpc-3.coord.tab Thu Jul 11 12:31:28 2019 -0400 @@ -1,1034 +1,1034 @@ Cells DIM1 DIM2 DIM3 DIM4 Cluster -1001000015.B2 -11.5652657867278 -1.31380578757931 -0.0153340976851289 -0.248070111266024 1 -1001000037.D3 -11.1705305355622 -0.449542101719973 -0.55102593401654 -0.709583544937752 1 -1001000031.F2 -10.9034865985644 0.356152065721901 -1.17963279262154 0.684246722251061 1 -1001000037.A4 -10.8022433351516 -0.173306230238628 -1.10393520337389 -2.29592847917511 1 -1001000250.B4 -10.7583812375057 -2.52739546376501 -0.162642559907488 -2.6771648328183 1 -1001000174.C6 -10.5475015195375 -0.548848108446587 -1.0068785965758 -1.26609484454219 1 -1001000012.G9 -10.5212024621584 1.40339679845456 -0.61743868486739 -0.233496247865159 1 -1001000017.F7 -10.2602641871214 -1.02343788487437 0.731991442159092 -0.0631679225716421 1 -1001000259.G12 -10.2480311840355 0.259957606293954 0.616282421244151 -2.52535933167469 1 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/hcpc.clusters.tab Thu Jul 11 12:31:28 2019 -0400 @@ -0,0 +1,1034 @@ +Cell Cluster +1001000173.G8 1 +1001000173.B4 3 +1001000173.A2 2 +1001000173.E2 1 +1001000173.F6 2 +1001000173.B12 3 +1001000173.H1 2 +1001000173.E4 1 +1001000173.C2 3 +1001000173.F4 3 +1001000173.E10 3 +1001000173.E6 2 +1001000173.C4 1 +1001000173.B10 2 +1001000173.A7 1 +1001000173.B8 3 +1001000173.F9 3 +1001000173.G6 1 +1001000173.A9 2 +1001000173.D2 3 +1001000173.F2 2 +1001000173.H9 3 +1001000173.G1 2 +1001000173.B3 3 +1001000173.D7 3 +1001000173.A12 2 +1001000173.C1 3 +1001000173.H7 2 +1001000173.H5 2 +1001000173.G2 3 +1001000173.A3 3 +1001000173.D3 3 +1001000173.E1 3 +1001000173.G12 3 +1001000173.C10 2 +1001000173.C3 1 +1001000173.A5 3 +1001000173.G4 3 +1001000173.B11 3 +1001000173.G9 2 +1001000173.F1 2 +1001000173.D8 3 +1001000173.E12 3 +1001000173.F11 3 +1001000173.E5 3 +1001000173.G10 2 +1001000173.B9 3 +1001000173.C7 1 +1001000173.B2 2 +1001000173.H4 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/hcpc.factor.extval.txt Thu Jul 11 12:31:28 2019 -0400 @@ -0,0 +1,20 @@ + +---------------------------------------- +purity : 0.515 +entropy : 0.459 +normalized mutual information : 0.2573 +variation of information : 2.7326 +normalized var. of information : 0.8523 +---------------------------------------- +specificity : 0.709 +sensitivity : 0.5529 +precision : 0.368 +recall : 0.5529 +F-measure : 0.4419 +---------------------------------------- +accuracy OR rand-index : 0.6724 +adjusted-rand-index : 0.2231 +jaccard-index : 0.2836 +fowlkes-mallows-index : 0.4511 +mirkin-metric : 349208 +----------------------------------------
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/numeric_factor.tsv Thu Jul 11 12:31:28 2019 -0400 @@ -0,0 +1,1034 @@ +cells TREX1 +1001000173.G8 0 +1001000173.B4 0 +1001000173.A2 0 +1001000173.E2 0 +1001000173.F6 0 +1001000173.B12 0 +1001000173.H1 0 +1001000173.E4 0 +1001000173.C2 0 +1001000173.F4 6.7756266151172 +1001000173.E10 0 +1001000173.E6 0 +1001000173.C4 0 +1001000173.B10 10.3552872832884 +1001000173.A7 2.60615781912942 +1001000173.B8 6.82790116769414 +1001000173.F9 7.25506392294518 +1001000173.G6 0 +1001000173.A9 0 +1001000173.D2 7.93813782236303 +1001000173.F2 8.61377881582071 +1001000173.H9 0 +1001000173.G1 0 +1001000173.B3 0 +1001000173.D7 6.86788976182893 +1001000173.A12 0 +1001000173.C1 4.21926690643781 +1001000173.H7 7.4680260213321 +1001000173.H5 0 +1001000173.G2 0 +1001000173.A3 0 +1001000173.D3 0 +1001000173.E1 3.83340613276625 +1001000173.G12 0 +1001000173.C10 0 +1001000173.C3 0 +1001000173.A5 6.42147360754871 +1001000173.G4 5.5544383115236 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--- a/test-data/pca.coord.tab Thu Jun 27 06:17:08 2019 -0400 +++ b/test-data/pca.coord.tab Thu Jul 11 12:31:28 2019 -0400 @@ -1,1034 +1,1034 @@ Cells DIM1 DIM2 DIM3 DIM4 DIM5 -1001000173.G8 -5.31693701953163 -3.46194654365546 1.74316013071281 -3.83334321406266 -0.425688901476677 -1001000173.B4 10.0361145342526 -6.4001376231127 1.82042981334608 -2.10784366673874 0.613700200499314 -1001000173.A2 3.79246289405376 2.13924734561231 -1.76540854841281 3.94878676664593 -0.308618844431403 -1001000173.E2 -4.32884658571041 0.402848740812616 -3.44065122508972 -0.93706795519332 -0.939747754792572 -1001000173.F6 -1.57651890662075 0.365062762744466 -3.33929898056208 -0.114850347546217 -1.11602793358278 -1001000173.B12 9.70073898225409 -6.03006818252483 5.28179285855865 0.167774846420523 0.56098251541239 -1001000173.H1 5.89792373696629 0.770986928316156 -3.40404737253666 -0.578943784171644 1.16374825389916 -1001000173.E4 -1.98714053195272 -3.915954034033 2.61563019157785 -1.01526684041459 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--- a/test-data/tsne-2.coord.tab Thu Jun 27 06:17:08 2019 -0400 +++ b/test-data/tsne-2.coord.tab Thu Jul 11 12:31:28 2019 -0400 @@ -1,1034 +1,1034 @@ Cells DIM1 DIM2 DIM3 -1001000173.G8 0.0314847317025074 0.456640351303022 -33.2971577182824 -1001000173.B4 -21.8881887393771 4.57696408464925 -13.0119035772348 -1001000173.A2 -14.2039734324191 14.6222128662589 8.02505946905919 -1001000173.E2 -3.36637182277452 -10.7560446547318 -7.47586195483765 -1001000173.F6 -6.96652753021878 -10.2440623165652 -5.21964076691587 -1001000173.B12 -25.7489672086917 -1.48916207590966 -14.8456356380068 -1001000173.H1 -32.3728793286476 4.18099819383561 -3.78840931039314 -1001000173.E4 0.0210509473288782 0.438415390427877 -33.3530437192527 -1001000173.C2 -15.7354979211835 14.0994068405047 0.344328731608186 -1001000173.F4 -21.3383335266939 14.0072953822047 4.02927338237204 -1001000173.E10 -19.7207999581954 22.0640682714116 -0.581150772003373 -1001000173.E6 -16.3590781382543 20.3108251765366 -2.35893791309731 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