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  • 09:19, 25 February 2026 Brb talk contribs created page File:Wgcna null power.png
  • 09:19, 25 February 2026 Brb talk contribs uploaded File:Wgcna null power.png
  • 09:19, 25 February 2026 Brb talk contribs created page File:Wgcna null dend.png (<syntaxhighlight lang='r'> library(WGCNA) # Set seed for reproducibility set.seed(123) # 1. Create a "Scaffold" for 3 modules (patterns of expression) nSamples = 50 # 2. Generate 1000 genes based on these patterns + some noise # Module 1: 200 genes correlated with pattern 1 noise = matrix(rnorm(nSamples*1000), nrow=nSamples) # 3. Combine into one expression matrix (Samples as Rows, Genes as Columns) datExpr2 = as.data.frame(noise) colnames(datExpr2) = paste0("Gene", 1:1000) rownames(datEx...)
  • 09:19, 25 February 2026 Brb talk contribs uploaded File:Wgcna null dend.png (<syntaxhighlight lang='r'> library(WGCNA) # Set seed for reproducibility set.seed(123) # 1. Create a "Scaffold" for 3 modules (patterns of expression) nSamples = 50 # 2. Generate 1000 genes based on these patterns + some noise # Module 1: 200 genes correlated with pattern 1 noise = matrix(rnorm(nSamples*1000), nrow=nSamples) # 3. Combine into one expression matrix (Samples as Rows, Genes as Columns) datExpr2 = as.data.frame(noise) colnames(datExpr2) = paste0("Gene", 1:1000) rownames(datEx...)
  • 09:16, 25 February 2026 Brb talk contribs created page File:Wgcna 3clusters power.png
  • 09:16, 25 February 2026 Brb talk contribs uploaded File:Wgcna 3clusters power.png
  • 09:16, 25 February 2026 Brb talk contribs created page File:Wgcna 3clusters dend.png (<syntaxhighlight lang='r'> # WGCNA uses standard hierarchical clustering (specifically the hclust function in R), # but the "magic" that makes it WGCNA is the Topological Overlap Matrix (TOM) used as the distance metric. # https://en.wikipedia.org/wiki/Weighted_correlation_network_analysis # https://cloud.wikis.utexas.edu/wiki/spaces/bioiteam/pages/47714689/Clustering+using+WGCNA # https://ngs101.com/how-to-build-gene-co-expression-networks-from-rna-seq-data-using-wgcna-complete-step-by-ste...)
  • 09:16, 25 February 2026 Brb talk contribs uploaded File:Wgcna 3clusters dend.png (<syntaxhighlight lang='r'> # WGCNA uses standard hierarchical clustering (specifically the hclust function in R), # but the "magic" that makes it WGCNA is the Topological Overlap Matrix (TOM) used as the distance metric. # https://en.wikipedia.org/wiki/Weighted_correlation_network_analysis # https://cloud.wikis.utexas.edu/wiki/spaces/bioiteam/pages/47714689/Clustering+using+WGCNA # https://ngs101.com/how-to-build-gene-co-expression-networks-from-rna-seq-data-using-wgcna-complete-step-by-ste...)
  • 14:32, 19 February 2026 Brb talk contribs created page File:Plotly 3d cont.png (<syntaxhighlight lang='r'> library(plotly) plot_ly( iris, x = ~Sepal.Length, y = ~Sepal.Width, z = ~Petal.Length, color = ~Petal.Width, # Now using a numeric/continuous variable type = "scatter3d", mode = "markers", marker = list( size = 10, opacity = 0.9, colorbar = list(title = "Petal Width") # Optional: adds a label to the color scale ) ) </syntaxhighlight>)
  • 14:32, 19 February 2026 Brb talk contribs uploaded File:Plotly 3d cont.png (<syntaxhighlight lang='r'> library(plotly) plot_ly( iris, x = ~Sepal.Length, y = ~Sepal.Width, z = ~Petal.Length, color = ~Petal.Width, # Now using a numeric/continuous variable type = "scatter3d", mode = "markers", marker = list( size = 10, opacity = 0.9, colorbar = list(title = "Petal Width") # Optional: adds a label to the color scale ) ) </syntaxhighlight>)
  • 13:51, 19 February 2026 Brb talk contribs created page File:Plotly shiny 3d.png (<syntaxhighlight lang='r'> # method 3: install.packages(c("shiny","plotly")) library(shiny) library(plotly) ui <- fluidPage( titlePanel("3D scatter (plotly)"), sidebarLayout( sidebarPanel( selectInput("x", "X axis", choices = names(iris), selected = "Sepal.Length"), selectInput("y", "Y axis", choices = names(iris), selected = "Sepal.Width"), selectInput("z", "Z axis", choices = names(iris), selected = "Petal.Length") ), mainPanel(plotlyOutput("plot3d")) )...)
  • 13:51, 19 February 2026 Brb talk contribs uploaded File:Plotly shiny 3d.png (<syntaxhighlight lang='r'> # method 3: install.packages(c("shiny","plotly")) library(shiny) library(plotly) ui <- fluidPage( titlePanel("3D scatter (plotly)"), sidebarLayout( sidebarPanel( selectInput("x", "X axis", choices = names(iris), selected = "Sepal.Length"), selectInput("y", "Y axis", choices = names(iris), selected = "Sepal.Width"), selectInput("z", "Z axis", choices = names(iris), selected = "Petal.Length") ), mainPanel(plotlyOutput("plot3d")) )...)
  • 13:50, 19 February 2026 Brb talk contribs created page File:Threejs 3d.png (<syntaxhighlight lang='r'> # method 2. High-performance WebGL: threejs (great for large datasets) library(threejs) # numeric color vector by Species cols <- as.numeric(iris$Species) pal <- rainbow(length(unique(cols))) widget <- scatterplot3js( x = iris$Sepal.Length, y = iris$Sepal.Width, z = iris$Petal.Length, color = pal[cols], size = .8, # control marker sizes grid = TRUE, renderer = "auto", # or "webgl" (webgl recommended for speed) bg = "white", l...)
  • 13:50, 19 February 2026 Brb talk contribs uploaded File:Threejs 3d.png (<syntaxhighlight lang='r'> # method 2. High-performance WebGL: threejs (great for large datasets) library(threejs) # numeric color vector by Species cols <- as.numeric(iris$Species) pal <- rainbow(length(unique(cols))) widget <- scatterplot3js( x = iris$Sepal.Length, y = iris$Sepal.Width, z = iris$Petal.Length, color = pal[cols], size = .8, # control marker sizes grid = TRUE, renderer = "auto", # or "webgl" (webgl recommended for speed) bg = "white", l...)
  • 13:48, 19 February 2026 Brb talk contribs created page File:Plotly 3d.png (<syntaxhighlight lang='r'> library(plotly) p <- plot_ly( iris, x = ~Sepal.Length, y = ~Sepal.Width, z = ~Petal.Length, color = ~Species, type = "scatter3d", mode = "markers", marker = list( size = 10, # fixed size opacity = 0.9 ) ) p # displays in RStudio Viewer or browser # htmlwidgets::saveWidget(p, "iris_3d_plotly.html", selfcontained = TRUE) </syntaxhighlight>)
  • 13:48, 19 February 2026 Brb talk contribs uploaded File:Plotly 3d.png (<syntaxhighlight lang='r'> library(plotly) p <- plot_ly( iris, x = ~Sepal.Length, y = ~Sepal.Width, z = ~Petal.Length, color = ~Species, type = "scatter3d", mode = "markers", marker = list( size = 10, # fixed size opacity = 0.9 ) ) p # displays in RStudio Viewer or browser # htmlwidgets::saveWidget(p, "iris_3d_plotly.html", selfcontained = TRUE) </syntaxhighlight>)
  • 09:57, 26 December 2025 Brb talk contribs created page Man (Created page with "= Resources = * [https://www.howtogeek.com/663440/how-to-use-linuxs-man-command-hidden-secrets-and-basics/ How to Use Linux’s man Command: Hidden Secrets and Basics] * [https://www.maketecheasier.com/read-linux-man-page/ How to Easily Read a Linux Man Page] ** Underlined or Italicized Text: It means you need to replace it with an appropriate argument. ** Ellipses: It means that argument or expression is repeatable. = Navigation = [https://wiki.gentoo.org/wiki/Man_page...")
  • 10:08, 22 December 2025 Brb talk contribs created page File:Annotation legend param.png (<syntaxhighlight lang='r'> library(RColorBrewer) library(ComplexHeatmap) set.seed(123) n <- 100 df <- data.frame( Subtype = sample(c("Hyperdiploid", "Ph-like", "DUX4", "Ph"), n, replace = TRUE), Sex = sample(c("Male", "Female"), n, replace = TRUE), Age_Group = sample(c("Childhood", "Adult"), n, replace = TRUE) ) # 1. Define distinct palettes # Subtype: Using "Set3" or "Paired" for many categories subtype_cols <- setNames( colorRampPalette(brewer.pal(12, "Paired"))(length(unique(df$S...)
  • 10:08, 22 December 2025 Brb talk contribs uploaded File:Annotation legend param.png (<syntaxhighlight lang='r'> library(RColorBrewer) library(ComplexHeatmap) set.seed(123) n <- 100 df <- data.frame( Subtype = sample(c("Hyperdiploid", "Ph-like", "DUX4", "Ph"), n, replace = TRUE), Sex = sample(c("Male", "Female"), n, replace = TRUE), Age_Group = sample(c("Childhood", "Adult"), n, replace = TRUE) ) # 1. Define distinct palettes # Subtype: Using "Set3" or "Paired" for many categories subtype_cols <- setNames( colorRampPalette(brewer.pal(12, "Paired"))(length(unique(df$S...)
  • 13:33, 19 December 2025 Brb talk contribs created page File:Uwot.png (<syntaxhighlight lang='r'> # Prepare data library(uwot) library(ggplot2) library(dplyr) # training and test sets (as given) iris_train <- iris[c(1:10, 51:60), ] iris_test <- iris[100:110, ] # numeric feature matrices x_train <- as.matrix(iris_train[, 1:4]) x_test <- as.matrix(iris_test[, 1:4]) # Fit UMAP on training data (keep model) set.seed(123) umap_model <- umap( x_train, n_neighbors = 5, min_dist = 0.1, n_components = 2, ret_model = TRUE ) # training embedding train_embe...)
  • 13:33, 19 December 2025 Brb talk contribs uploaded File:Uwot.png (<syntaxhighlight lang='r'> # Prepare data library(uwot) library(ggplot2) library(dplyr) # training and test sets (as given) iris_train <- iris[c(1:10, 51:60), ] iris_test <- iris[100:110, ] # numeric feature matrices x_train <- as.matrix(iris_train[, 1:4]) x_test <- as.matrix(iris_test[, 1:4]) # Fit UMAP on training data (keep model) set.seed(123) umap_model <- umap( x_train, n_neighbors = 5, min_dist = 0.1, n_components = 2, ret_model = TRUE ) # training embedding train_embe...)
  • 19:21, 6 December 2025 Brb talk contribs created page File:Youtube-embed.png
  • 19:21, 6 December 2025 Brb talk contribs uploaded File:Youtube-embed.png
  • 10:48, 1 December 2025 Brb talk contribs created page File:Dendro colorbars.png (<syntaxhighlight lang='r'> library(ComplexHeatmap) library(circlize) library(RColorBrewer) set.seed(123) # --- Example data (10 samples) --- mat <- matrix(rnorm(30), nrow = 10) rownames(mat) <- paste0("Sample", 1:10) mat[1:5, ] <- mat[1:5, ] mat[6:10, ] <- mat[6:10, ] + 2 # Cluster on samples (rows) hc <- hclust(dist(mat)) # Annotations in ORIGINAL sample order groups <- rep(c("A", "B"), each = 5) score <- seq(0, 1, length = 10) cl2 <- cutree(hc, 2) cl3 <- cutree(hc, 3) cl4 <- cutree(hc,...)
  • 10:48, 1 December 2025 Brb talk contribs uploaded File:Dendro colorbars.png (<syntaxhighlight lang='r'> library(ComplexHeatmap) library(circlize) library(RColorBrewer) set.seed(123) # --- Example data (10 samples) --- mat <- matrix(rnorm(30), nrow = 10) rownames(mat) <- paste0("Sample", 1:10) mat[1:5, ] <- mat[1:5, ] mat[6:10, ] <- mat[6:10, ] + 2 # Cluster on samples (rows) hc <- hclust(dist(mat)) # Annotations in ORIGINAL sample order groups <- rep(c("A", "B"), each = 5) score <- seq(0, 1, length = 10) cl2 <- cutree(hc, 2) cl3 <- cutree(hc, 3) cl4 <- cutree(hc,...)
  • 17:16, 10 November 2025 Brb talk contribs created page File:Umap-iris.png (<syntaxhighlight lang='r'> library(umap) # Load the built-in Iris dataset data(iris) # Separate the features (variables) from the species labels iris_features <- iris[, 1:4] iris_species <- iris[, 5] # Run UMAP, reducing the 4 dimensions down to 2 # n_neighbors: Controls how UMAP balances local vs. global structure. # min_dist: Controls how tightly points are packed together. iris_umap_results <- umap(iris_features, random_state = 42) # The result is a list. The actual 2D coordinates are...)
  • 17:16, 10 November 2025 Brb talk contribs uploaded File:Umap-iris.png (<syntaxhighlight lang='r'> library(umap) # Load the built-in Iris dataset data(iris) # Separate the features (variables) from the species labels iris_features <- iris[, 1:4] iris_species <- iris[, 5] # Run UMAP, reducing the 4 dimensions down to 2 # n_neighbors: Controls how UMAP balances local vs. global structure. # min_dist: Controls how tightly points are packed together. iris_umap_results <- umap(iris_features, random_state = 42) # The result is a list. The actual 2D coordinates are...)
  • 19:07, 28 October 2025 Brb talk contribs created page File:Paletteer d.png (<syntaxhighlight lang='r'> paletteer_d("RColorBrewer::RdBu") #<colors> #67001FFF #B2182BFF #D6604DFF #F4A582FF #FDDBC7FF #F7F7F7FF #D1E5F0FF #92C5DEFF #4393C3FF #2166ACFF #053061FF paletteer_d("ggsci::uniform_startrek") # <colors> #CC0C00FF #5C88DAFF #84BD00FF #FFCD00FF #7C878EFF #00B5E2FF #00AF66FF </syntaxhighlight>)
  • 19:07, 28 October 2025 Brb talk contribs uploaded File:Paletteer d.png (<syntaxhighlight lang='r'> paletteer_d("RColorBrewer::RdBu") #<colors> #67001FFF #B2182BFF #D6604DFF #F4A582FF #FDDBC7FF #F7F7F7FF #D1E5F0FF #92C5DEFF #4393C3FF #2166ACFF #053061FF paletteer_d("ggsci::uniform_startrek") # <colors> #CC0C00FF #5C88DAFF #84BD00FF #FFCD00FF #7C878EFF #00B5E2FF #00AF66FF </syntaxhighlight>)
  • 11:26, 9 October 2025 Brb talk contribs created page Mediawiki-docker (Created page with "= Directories = * Backup directory '''~/backups''' * Mediawiki directory '''~/mediawiki-docker''' = Accounts = * Note the mediawiki admin and user accounts are embedded in the SQL database. * DB account (defined in compose.yml and LocalSettings.php): ** database: wikidb ($wgDBname) ** user: ($wgDBuser) ** password: ($wgDBpassword) * Mediawiki admin: * Mediawiki user: = Backup = Assume the mediawiki directory on the host is '''/var/www/html/wiki''' <ul> <li>MySQL <...")
  • 15:12, 4 October 2025 Brb talk contribs created page File:Massage.png
  • 15:12, 4 October 2025 Brb talk contribs uploaded File:Massage.png
  • 14:57, 8 September 2025 Brb talk contribs created page File:MacSettingKeyboardShortcut.png
  • 14:57, 8 September 2025 Brb talk contribs uploaded File:MacSettingKeyboardShortcut.png
  • 14:56, 8 September 2025 Brb talk contribs created page File:MacSettingLiveCaption.png
  • 14:56, 8 September 2025 Brb talk contribs uploaded File:MacSettingLiveCaption.png
  • 09:17, 7 August 2025 Brb talk contribs created page File:Heatmap-gaps.png (<syntaxhighlight lang='r'> # 1. Prepare the data set.seed(42) mat = matrix(rnorm(30, mean = 0, sd = 1.2), nrow = 5, ncol = 6) mat[2, 1] = -2.5 mat[2, 6] = 2.5 mat[1, 2] = -1.8 mat[4, 4] = 1.8 # 2. Define the color palette library(circlize) col_fun = colorRamp2(c(-2.5, 0, 2.5), c("steelblue", "white", "darkred")) # 3. Create the heatmap object library(ComplexHeatmap) ht = Heatmap( mat, col = col_fun, cluster_rows = FALSE, cluster_columns = FALSE, show_row_names = FALSE, show_colu...)
  • 09:17, 7 August 2025 Brb talk contribs uploaded File:Heatmap-gaps.png (<syntaxhighlight lang='r'> # 1. Prepare the data set.seed(42) mat = matrix(rnorm(30, mean = 0, sd = 1.2), nrow = 5, ncol = 6) mat[2, 1] = -2.5 mat[2, 6] = 2.5 mat[1, 2] = -1.8 mat[4, 4] = 1.8 # 2. Define the color palette library(circlize) col_fun = colorRamp2(c(-2.5, 0, 2.5), c("steelblue", "white", "darkred")) # 3. Create the heatmap object library(ComplexHeatmap) ht = Heatmap( mat, col = col_fun, cluster_rows = FALSE, cluster_columns = FALSE, show_row_names = FALSE, show_colu...)
  • 14:18, 29 July 2025 Brb talk contribs created page File:Inkscape-heatmap.svg
  • 14:18, 29 July 2025 Brb talk contribs uploaded File:Inkscape-heatmap.svg
  • 08:16, 19 July 2025 Brb talk contribs created page File:NemoCaja.png
  • 08:16, 19 July 2025 Brb talk contribs uploaded File:NemoCaja.png
  • 16:25, 5 July 2025 Brb talk contribs created page File:Cinnamon.png
  • 16:25, 5 July 2025 Brb talk contribs uploaded File:Cinnamon.png
  • 16:18, 5 July 2025 Brb talk contribs created page File:MATE.png
  • 16:18, 5 July 2025 Brb talk contribs uploaded File:MATE.png
  • 16:13, 5 July 2025 Brb talk contribs created page File:GNOME.png
  • 16:13, 5 July 2025 Brb talk contribs uploaded File:GNOME.png
  • 16:06, 5 July 2025 Brb talk contribs created page File:KDE.png
  • 16:06, 5 July 2025 Brb talk contribs uploaded File:KDE.png
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