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• 15:18, 8 May 2024 Brb talk contribs uploaded File:Rtools44.png
• 15:18, 8 May 2024 Brb talk contribs created page File:Rtools44.png
• 17:31, 23 April 2024 Brb talk contribs uploaded File:Polygon.png (<syntaxhighlight lang='r'> plot(c(1, 9), 1:2, type = "n") polygon(1:9, c(2,1,2,1,NA,2,1,2,1), col = c("red", "blue"), border = c("green", "yellow"), lwd = 3, lty = c("dashed", "solid")) </syntaxhighlight>)
• 17:31, 23 April 2024 Brb talk contribs created page File:Polygon.png (<syntaxhighlight lang='r'> plot(c(1, 9), 1:2, type = "n") polygon(1:9, c(2,1,2,1,NA,2,1,2,1), col = c("red", "blue"), border = c("green", "yellow"), lwd = 3, lty = c("dashed", "solid")) </syntaxhighlight>)
• 14:49, 23 April 2024 Brb talk contribs uploaded File:Venn4.png (<syntaxhighlight lang='r'> library(venn) set.seed(12345) x <- list(First = 1:40, Second = 15:60, Third = sample(25:50, 25), Fourth=sample(15:65, 35)) venn(x, ilabels = "counts", zcolor = "style") </syntaxhighlight>)
• 14:49, 23 April 2024 Brb talk contribs created page File:Venn4.png (<syntaxhighlight lang='r'> library(venn) set.seed(12345) x <- list(First = 1:40, Second = 15:60, Third = sample(25:50, 25), Fourth=sample(15:65, 35)) venn(x, ilabels = "counts", zcolor = "style") </syntaxhighlight>)
• 15:53, 17 April 2024 Brb talk contribs uploaded File:Tiv-demo.png
• 15:53, 17 April 2024 Brb talk contribs created page File:Tiv-demo.png
• 20:17, 12 March 2024 Brb talk contribs uploaded File:Filtered R mean.png (Use sample mean instead of variance for each gene as the filter statistic. <syntaxhighlight lang='r'> # Follow the previous code chunks M2 <- rowMeans(exprs(ALL_bcrneg)) theta <- seq(0, .80, .01) R_BH <- filtered_R(alpha=.10, M2, p2, theta, method="BH") which.max(R_BH) # 10% <---- so theta=0.1 is the optimal; only 10% genes are removed # 11 max(R_BH) # [1] 270 plot(theta, R_BH, type="l", xlab=expression(theta), ylab="Rejections", main="BH cutoff = 0.1") abline(v=.1, lty=2) <...)
• 20:17, 12 March 2024 Brb talk contribs created page File:Filtered R mean.png (Use sample mean instead of variance for each gene as the filter statistic. <syntaxhighlight lang='r'> # Follow the previous code chunks M2 <- rowMeans(exprs(ALL_bcrneg)) theta <- seq(0, .80, .01) R_BH <- filtered_R(alpha=.10, M2, p2, theta, method="BH") which.max(R_BH) # 10% <---- so theta=0.1 is the optimal; only 10% genes are removed # 11 max(R_BH) # [1] 270 plot(theta, R_BH, type="l", xlab=expression(theta), ylab="Rejections", main="BH cutoff = 0.1") abline(v=.1, lty=2) <...)
• 08:54, 12 March 2024 Brb talk contribs uploaded File:Rainbow v05.png
• 08:54, 12 March 2024 Brb talk contribs created page File:Rainbow v05.png
• 08:53, 12 March 2024 Brb talk contribs uploaded File:Rainbow s05.png
• 08:53, 12 March 2024 Brb talk contribs created page File:Rainbow s05.png
• 08:52, 12 March 2024 Brb talk contribs uploaded File:Rainbow default.png (<syntaxhighlight lang='r'> library(shiny) # Define the UI ui <- fluidPage( titlePanel("Rainbow Color Palette"), sidebarLayout( sidebarPanel( sliderInput("s_value", "Saturation (s):", min = 0, max = 1, value = 1, step = 0.01), sliderInput("v_value", "Value (v):", min = 0, max = 1, value = 1, step = 0.01) ), mainPanel( plotOutput("rainbow_plot") ) ) ) # Define the server server <- function(input, output) { output$rainbow_plot <- renderPlot({ s <-...)
• 08:52, 12 March 2024 Brb talk contribs created page File:Rainbow default.png (<syntaxhighlight lang='r'> library(shiny) # Define the UI ui <- fluidPage( titlePanel("Rainbow Color Palette"), sidebarLayout( sidebarPanel( sliderInput("s_value", "Saturation (s):", min = 0, max = 1, value = 1, step = 0.01), sliderInput("v_value", "Value (v):", min = 0, max = 1, value = 1, step = 0.01) ), mainPanel( plotOutput("rainbow_plot") ) ) ) # Define the server server <- function(input, output) { output$rainbow_plot <- renderPlot({ s <-...)
• 21:41, 11 March 2024 Brb talk contribs uploaded File:Filtered R.png (<syntaxhighlight lang='r'> theta <- seq(0, .80, .01) R_BH <- filtered_R(alpha=.10, S2, p2, theta, method="BH") which.max(R_BH) # 60% <---- so theta=0.6 is the optimal filtering threshold # 61 max(R_BH) # [1] 380 plot(theta, R_BH, type="l", xlab=expression(theta), ylab="Rejections", main="BH cutoff = 0.1") abline(v=.6, lty=2) </syntaxhighlight>)
• 21:41, 11 March 2024 Brb talk contribs created page File:Filtered R.png (<syntaxhighlight lang='r'> theta <- seq(0, .80, .01) R_BH <- filtered_R(alpha=.10, S2, p2, theta, method="BH") which.max(R_BH) # 60% <---- so theta=0.6 is the optimal filtering threshold # 61 max(R_BH) # [1] 380 plot(theta, R_BH, type="l", xlab=expression(theta), ylab="Rejections", main="BH cutoff = 0.1") abline(v=.6, lty=2) </syntaxhighlight>)
• 21:35, 11 March 2024 Brb talk contribs uploaded File:Filtered p.png (Note: # x-axis "p cutoff" should be "BH cutoff" or "FDR cutoff". # Each curve represents theta (filtering threshold). For example, theta=.1 means 10% of genes are filtered out before we do multiple testing (or BH adjustment). # It is seen the larger the theta, the more hypotheses are rejected at the same FDR cutoff. For example, #* if theta=0, 251 hypotheses are rejected at FDR=.1 #* if theta=.5, 355 hypotheses are rejected at FDR=.1. <syntaxhighlight lang='r'> BiocManager::install("ALL")...)
• 21:35, 11 March 2024 Brb talk contribs created page File:Filtered p.png (Note: # x-axis "p cutoff" should be "BH cutoff" or "FDR cutoff". # Each curve represents theta (filtering threshold). For example, theta=.1 means 10% of genes are filtered out before we do multiple testing (or BH adjustment). # It is seen the larger the theta, the more hypotheses are rejected at the same FDR cutoff. For example, #* if theta=0, 251 hypotheses are rejected at FDR=.1 #* if theta=.5, 355 hypotheses are rejected at FDR=.1. <syntaxhighlight lang='r'> BiocManager::install("ALL")...)
• 16:49, 8 March 2024 Brb talk contribs uploaded File:DataOutliers2.png ({{Pre}} puree <- read.csv("https://gist.githubusercontent.com/arraytools/e851ed88c7456779557fbf3ed67b157a/raw/9971c61fea1db99acbd9de17ea82679ba9811358/dataOutliers2.csv", header=F) plot(puree[,1], puree[, 2], xlab="X", ylab="Y") abline(lm(V2 ~ V1, data = puree)) # robust regression require(MASS) summary(rlm(V2 ~ V1, data = puree)) abline(rr.huber <- rlm(V2 ~ V1, data = puree), col = "blue") # quantile regression library(quantreg) abline(rq(V2 ~ V1, data=puree, tau = 0.5), col = "red") # theil...)
• 16:49, 8 March 2024 Brb talk contribs created page File:DataOutliers2.png ({{Pre}} puree <- read.csv("https://gist.githubusercontent.com/arraytools/e851ed88c7456779557fbf3ed67b157a/raw/9971c61fea1db99acbd9de17ea82679ba9811358/dataOutliers2.csv", header=F) plot(puree[,1], puree[, 2], xlab="X", ylab="Y") abline(lm(V2 ~ V1, data = puree)) # robust regression require(MASS) summary(rlm(V2 ~ V1, data = puree)) abline(rr.huber <- rlm(V2 ~ V1, data = puree), col = "blue") # quantile regression library(quantreg) abline(rq(V2 ~ V1, data=puree, tau = 0.5), col = "red") # theil...)
• 15:27, 7 March 2024 Brb talk contribs uploaded File:DataOutliers.png ({{Pre}} puree <- read.csv("https://gist.githubusercontent.com/arraytools/47d3a46ae1f9a9cd47db350ae2bd2338/raw/b5cccc8e566ff3bef81b1b371e8bfa174c98ef38/dataOutliers.csv", header = FALSE) plot(puree[,1], puree[, 2], xlim=c(0,1), ylim=c(0,1), xlab="X", ylab="Y") abline(0,1, lty=2) abline(lm(V2 ~ V1, data = puree)) # robust regression require(MASS) summary(rlm(V2 ~ V1, data = puree)) abline(rr.huber <- rlm(V2 ~ V1, data = puree), col = "blue") # almost overlapped with lm() # quantile regressio...)
• 15:27, 7 March 2024 Brb talk contribs created page File:DataOutliers.png ({{Pre}} puree <- read.csv("https://gist.githubusercontent.com/arraytools/47d3a46ae1f9a9cd47db350ae2bd2338/raw/b5cccc8e566ff3bef81b1b371e8bfa174c98ef38/dataOutliers.csv", header = FALSE) plot(puree[,1], puree[, 2], xlim=c(0,1), ylim=c(0,1), xlab="X", ylab="Y") abline(0,1, lty=2) abline(lm(V2 ~ V1, data = puree)) # robust regression require(MASS) summary(rlm(V2 ~ V1, data = puree)) abline(rr.huber <- rlm(V2 ~ V1, data = puree), col = "blue") # almost overlapped with lm() # quantile regressio...)
• 22:41, 10 February 2024 Brb talk contribs uploaded File:R162.png
• 22:41, 10 February 2024 Brb talk contribs created page File:R162.png
• 22:53, 8 February 2024 Brb talk contribs uploaded File:Jitterbox.png (<syntaxhighlight lang='r'> nc <- 5 assy <- LETTERS[1:nc] pal <- ggpubr::get_palette("default", nc) set.seed(1) nr <- 5 mat <- matrix(runif(nr*length(assy)), nrow = nr, ncol = length(assy)) set.seed(1) cutoffs <- runif(nc) colnames(mat) <- assy par(mar=c(5,4,1,1)+.1) plot(1, 1, xlim = c(0.5, nc + .5), ylim = c(0,1), type = "n", xlab = "Assay", ylab = "Score", xaxt = 'n') for (i in 1:nc) { rect(i - 0.25, 0, i + 0.25, 1, col = pal[i]) lines(x = c(i - 0.25, i + 0.25), y = c(cutof...)
• 22:53, 8 February 2024 Brb talk contribs created page File:Jitterbox.png (<syntaxhighlight lang='r'> nc <- 5 assy <- LETTERS[1:nc] pal <- ggpubr::get_palette("default", nc) set.seed(1) nr <- 5 mat <- matrix(runif(nr*length(assy)), nrow = nr, ncol = length(assy)) set.seed(1) cutoffs <- runif(nc) colnames(mat) <- assy par(mar=c(5,4,1,1)+.1) plot(1, 1, xlim = c(0.5, nc + .5), ylim = c(0,1), type = "n", xlab = "Assay", ylab = "Score", xaxt = 'n') for (i in 1:nc) { rect(i - 0.25, 0, i + 0.25, 1, col = pal[i]) lines(x = c(i - 0.25, i + 0.25), y = c(cutof...)
• 11:25, 19 January 2024 Brb talk contribs uploaded File:RStudioAbort.png
• 11:25, 19 January 2024 Brb talk contribs created page File:RStudioAbort.png
• 21:39, 25 December 2023 Brb talk contribs created page Linux shell (Created page with "= What’s the Difference Between Bash, Zsh, and Other Linux Shells = https://www.howtogeek.com/68563/htg-explains-what-are-the-differences-between-linux-shells/ = sh (Bourne shell, 1977) = * Basic shell * POSIX-compliant shell = Bash shell = * Can be mostly POSIX compliant * Expose array indices * Regular expression conditionals * Increment assignment operator * Current version: Bash 4 [http://bash.cyberciti.biz/guide/Main_Page Bash shell programming] = Dash = * Mos...")
• 06:22, 22 December 2023 Brb talk contribs created page R Docker (Created page with "= Use with R (r-base) & RStudio IDE: Rocker = <ul> <li>[https://solutions.rstudio.com/environments/docker/ Docker 101 for Data Scientists] by RStudio </li> <li>[https://hub.docker.com/_/r-base r-base] (Official image, R version is tagged), [https://hub.docker.com/r/rocker/rstudio/tags RStudio] <ul> <li>The oldest version of R is 3.1.2 (2014-10-31). '''docke run -it --rm r-base:3.1.2''' <li>[https://www.rocker-project.org/use/managing_users/ Managing Users] </li> <li>[htt...")
• 20:01, 14 November 2023 Brb talk contribs created page Orange pi (Created page with "= Orange pi 5 plus = * [http://www.orangepi.org/html/hardWare/computerAndMicrocontrollers/details/Orange-Pi-5-plus.html Orange Pi 5 Plus (4GB/8GB/16GB)] * Orange pi 5 plus vs Orange pi 5. Orange pi 5 uses RK3588S & Gigabit ethernet but Orange Pi 5 plus uses RK3588 & 2.5G ethernet.")
• 20:38, 15 October 2023 Brb talk contribs uploaded File:Geomerrorbarh.png (<syntaxhighlight lang='rsplus'> df <- data.frame( trt = factor(c("Treatment 1", "Treatment 2", "Treatment 3", "Treatment 4", "Treatment 5")), # treatment resp = c(1, 5, 3, 4, 2), # response se = c(0.1, 0.3, 0.3, 0.2, 0.2) # standard error ) # make 'Treatment 1' shown at the top df$trt <- factor(df$trt, levels = c("Treatment 5", "Treatment 4", "Treatment 3", "Treatment 2", "Treatment 1")) p <- ggplot(df, aes(resp, trt)) + geom_point() p + geom_errorbarh(aes(xmax=resp + se, xmin=resp-se),...)
• 20:38, 15 October 2023 Brb talk contribs created page File:Geomerrorbarh.png (<syntaxhighlight lang='rsplus'> df <- data.frame( trt = factor(c("Treatment 1", "Treatment 2", "Treatment 3", "Treatment 4", "Treatment 5")), # treatment resp = c(1, 5, 3, 4, 2), # response se = c(0.1, 0.3, 0.3, 0.2, 0.2) # standard error ) # make 'Treatment 1' shown at the top df$trt <- factor(df$trt, levels = c("Treatment 5", "Treatment 4", "Treatment 3", "Treatment 2", "Treatment 1")) p <- ggplot(df, aes(resp, trt)) + geom_point() p + geom_errorbarh(aes(xmax=resp + se, xmin=resp-se),...)
• 18:44, 10 October 2023 Brb talk contribs created page Drupal (Created page with "* https://www.drupal.org/home * [https://www.drupal.org/docs/develop/local-server-setup/linux-development-environments/set-up-a-local-development-drupal-site-on-the-latest-ubuntu-lts-version-and-more Advertising sustains the DA. Set up a local development Drupal site on the latest Ubuntu LTS version and more] * [https://www.digitalocean.com/community/tutorials/how-to-develop-a-drupal-9-website-on-your-local-machine-using-docker-and-ddev How To Develop a Drupal 9 Website...")
• 17:32, 9 October 2023 Brb talk contribs uploaded File:Calibre.png
• 17:32, 9 October 2023 Brb talk contribs created page File:Calibre.png
• 09:07, 11 September 2023 Brb talk contribs created page Longitudinal (Created page with "= Mixed Effect Model = * Paper by [http://www.stat.cmu.edu/~brian/463/week07/laird-ware-biometrics-1982.pdf Laird and Ware 1982] * [https://vitalflux.com/fixed-vs-random-vs-mixed-effects-models-examples/ Random effects vs fixed effects model]: There may be factors related to country/region (random variable) which may result in different patients’ responses to the vaccine, and, ''not all countries are included in the study''. * [http://cran.r-project.org/doc/contrib/F...")
• 12:18, 24 August 2023 Brb talk contribs uploaded File:Wheel f400.png
• 12:18, 24 August 2023 Brb talk contribs created page File:Wheel f400.png
• 12:17, 24 August 2023 Brb talk contribs uploaded File:Wheel f8.png
• 12:17, 24 August 2023 Brb talk contribs created page File:Wheel f8.png
• 15:59, 22 August 2023 Brb talk contribs uploaded File:Roc asah.png (<pre> par(mfrow=c(1,2)) roc(aSAH$outcome, aSAH$s100b, plot = T) roc(aSAH$outcome2, aSAH$s100b, plot = T) par(mfrow=c(1,1)) </pre>)
• 15:59, 22 August 2023 Brb talk contribs created page File:Roc asah.png (<pre> par(mfrow=c(1,2)) roc(aSAH$outcome, aSAH$s100b, plot = T) roc(aSAH$outcome2, aSAH$s100b, plot = T) par(mfrow=c(1,1)) </pre>)
• 14:44, 13 August 2023 Brb talk contribs uploaded File:Rotateheatmap.png (<syntaxhighlight lang="rsplus"> library(circlize) set.seed(123) mat = matrix(rnorm(80), 8, 10) rownames(mat) = paste0("R", 1:8) colnames(mat) = paste0("C", 1:10) col_anno = HeatmapAnnotation( df = data.frame(anno1 = 1:10, anno2 = rep(letters[1:3], c(4,3,3))), col = list(anno2 = c("a" = "red", "b" = "blue", "c" = "green"))) row_anno = rowAnnotation( df = data.frame(anno3 = 1:8, anno4 = rep(l...)
• 14:44, 13 August 2023 Brb talk contribs created page File:Rotateheatmap.png (<syntaxhighlight lang="rsplus"> library(circlize) set.seed(123) mat = matrix(rnorm(80), 8, 10) rownames(mat) = paste0("R", 1:8) colnames(mat) = paste0("C", 1:10) col_anno = HeatmapAnnotation( df = data.frame(anno1 = 1:10, anno2 = rep(letters[1:3], c(4,3,3))), col = list(anno2 = c("a" = "red", "b" = "blue", "c" = "green"))) row_anno = rowAnnotation( df = data.frame(anno3 = 1:8, anno4 = rep(l...)
• 16:21, 12 August 2023 Brb talk contribs uploaded File:Rotatedend.png
• 16:21, 12 August 2023 Brb talk contribs created page File:Rotatedend.png
• 16:17, 12 August 2023 Brb talk contribs uploaded File:Dend12.png ({{Pre}} set.seed(123) dat <- matrix(rnorm(20), ncol=2) # perform hierarchical clustering hc <- hclust(dist(dat)) # plot dendrogram plot(hc) # get ordering of leaves ord <- order.dendrogram(as.dendrogram(hc)) ord # [1] 8 3 6 5 10 1 9 7 2 4 # Same as seen on the dendrogram nodes # Rotate the branches (1,9) & (7,2,4) plot(rotate(hc, c("8", "3", "6", "5", "10", "7", "2", "4", "1", "9")), main="Rotated") </pre>)