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Below is a list of the most recent file uploads. See the gallery of new files for a more visual overview.
- 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>)
- 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>)
- 15:53, 17 April 2024 Brb talk contribs uploaded 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) <...)
- 08:54, 12 March 2024 Brb talk contribs uploaded File:Rainbow v05.png
- 08:53, 12 March 2024 Brb talk contribs uploaded 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 <-...)
- 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>)
- 21:35, 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")...)
- 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...)
- 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...)
- 22:41, 10 February 2024 Brb talk contribs uploaded 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...)
- 11:25, 19 January 2024 Brb talk contribs uploaded File:RStudioAbort.png
- 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$trt, levels = c("Treatment 5", "Treatment 4", "Treatment 3", "Treatment 2", "Treatment 1")) p <- ggplot(df, aes(resp, trt)) + geom_point() p + geom_errorbarh(aes(xmax=resp + se, xmin=resp-se),...)
- 17:32, 9 October 2023 Brb talk contribs uploaded File:Calibre.png
- 12:18, 24 August 2023 Brb talk contribs uploaded File:Wheel f400.png
- 12:17, 24 August 2023 Brb talk contribs uploaded File:Wheel f8.png
- 15:59, 22 August 2023 Brb talk contribs uploaded File:Roc asah.png (<pre> par(mfrow=c(1,2)) roc(aSAH$outcome, aSAH$s100b, plot = T) roc(aSAH$outcome2, aSAH$s100b, plot = T) par(mfrow=c(1,1)) </pre>)
- 14:44, 13 August 2023 Brb talk contribs uploaded File:Rotateheatmap.png (<syntaxhighlight lang="rsplus"> library(circlize) set.seed(123) mat = matrix(rnorm(80), 8, 10) rownames(mat) = paste0("R", 1:8) colnames(mat) = paste0("C", 1:10) col_anno = HeatmapAnnotation( df = data.frame(anno1 = 1:10, anno2 = rep(letters[1:3], c(4,3,3))), col = list(anno2 = c("a" = "red", "b" = "blue", "c" = "green"))) row_anno = rowAnnotation( df = data.frame(anno3 = 1:8, anno4 = rep(l...)
- 16:21, 12 August 2023 Brb talk contribs uploaded File:Rotatedend.png
- 16:17, 12 August 2023 Brb talk contribs uploaded File:Dend12.png ({{Pre}} set.seed(123) dat <- matrix(rnorm(20), ncol=2) # perform hierarchical clustering hc <- hclust(dist(dat)) # plot dendrogram plot(hc) # get ordering of leaves ord <- order.dendrogram(as.dendrogram(hc)) ord # [1] 8 3 6 5 10 1 9 7 2 4 # Same as seen on the dendrogram nodes # Rotate the branches (1,9) & (7,2,4) plot(rotate(hc, c("8", "3", "6", "5", "10", "7", "2", "4", "1", "9")), main="Rotated") </pre>)
- 16:39, 6 August 2023 Brb talk contribs uploaded File:Plotly3d.png
- 15:39, 31 July 2023 Brb talk contribs uploaded File:Filter single.png
- 12:47, 27 May 2023 Brb talk contribs uploaded File:Vibrant ink rstheme.png (https://github.com/captaincaed/rstudio/blob/main/vibrant_ink_SB_2.rstheme)
- 13:46, 21 May 2023 Brb talk contribs uploaded File:R2.png (<syntaxhighlight lang='rsplus'> x <- seq(0, 2.5, length=20) y <- sin(x) plot(x, y) abline(lsfit(x, y, intercept = F), col = 'red') summary(fit)$r.squared # [1] 0.8554949 </syntaxhighlight>)
- 16:02, 11 May 2023 Brb talk contribs uploaded File:Paletteggplot2.png
- 12:12, 10 May 2023 Brb talk contribs uploaded File:Paletteshowcol.png
- 11:15, 10 May 2023 Brb talk contribs uploaded File:Palettebarplot.png (<syntaxhighlight lang='rsplus'> pal <- c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00") # pal <- sample(colors(), 10) # randomly pick 10 colors barplot(rep(1, length(pal)), col = pal, space = 0, axes = FALSE, border = NA) </syntaxhighlight>)
- 11:14, 10 May 2023 Brb talk contribs uploaded File:Paletteheatmap.png (<syntaxhighlight lang='rsplus'> pal <- c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00") pal <- matrix(pal, nr=2) # acknowledge a nice warning message pal_matrix <- matrix(seq_along(pal), nr=nrow(pal), nc=ncol(pal)) heatmap(pal_matrix, col = pal, Rowv = NA, Colv = NA, scale = "none", ylab = "", xlab = "", main = "", margins = c(5, 5)) # 2 rows, 3 columns with labeling on two axes </syntaxhighlight>)
- 11:08, 10 May 2023 Brb talk contribs uploaded File:Rpalette.png (<syntaxhighlight lang='rsplus'> pal <- palette() # [1] "black" "#DF536B" "#61D04F" "#2297E6" "#28E2E5" "#CD0BBC" "#F5C710" # [8] "gray62" pal_matrix <- matrix(seq_along(pal), nr=1) image(pal_matrix, col = pal, axes = FALSE) # 8 rows, 1 column, but no labeling # Starting from bottom, left. par()$usr # change with the data dim text(0, (par()$usr[4]-par()$usr[3])/8*c(0:7), labels = pal) </syntaxhighlight>)
- 13:52, 9 May 2023 Brb talk contribs uploaded File:Ggplotbarplot.png
- 11:40, 9 May 2023 Brb talk contribs uploaded File:Cbioportal cptac.png
- 21:07, 25 April 2023 Brb talk contribs uploaded File:Losslesscut.png
- 17:47, 23 April 2023 Brb talk contribs uploaded File:Sleepstudy.png (<syntaxhighlight lang='rsplus'> sleepstudy %>% ggplot(aes(x=Days, y = Reaction)) + geom_point() + geom_smooth(method = "lm", se = FALSE) + facet_wrap(~Subject) </syntaxhighlight>)
- 16:58, 17 March 2023 Brb talk contribs uploaded File:Svg4.svg (<pre> svg("svg4.svg", width=4, height=4) plot(1:10, main="width=4, height=4") dev.off() </pre>)
- 10:44, 11 March 2023 Brb talk contribs uploaded File:RStudioVisualMode.png
- 16:01, 8 March 2023 Brb talk contribs uploaded File:Pca directly2.png
- 11:11, 11 February 2023 Brb talk contribs uploaded File:MultipleProbes.PNG
- 11:10, 11 February 2023 Brb talk contribs uploaded File:ClassPredictionOptions.PNG
- 11:08, 11 February 2023 Brb talk contribs uploaded File:ClassPrediction.PNG
- 21:01, 28 January 2023 Brb talk contribs uploaded File:Pca factoextra.png
- 20:34, 28 January 2023 Brb talk contribs uploaded File:Pca autoplot.png
- 20:34, 28 January 2023 Brb talk contribs uploaded File:Pca directly.png
- 10:21, 25 January 2023 Brb talk contribs uploaded File:VscodeEnergy.png
- 22:26, 14 January 2023 Brb talk contribs uploaded File:RbdGeom.png (<pre> require(ggplot2) aggregate( .~ treatment +block,FUN=median, data = data) |> ggplot(aes(treatment, yield)) + geom_line(aes(group = block, color = block), linewidth = 1.2) + geom_point(aes(color = block), shape = 15, size=2.6) </pre>)
- 21:51, 14 January 2023 Brb talk contribs uploaded File:RbdBlock.png (<pre> set.seed(1234) block <- as.factor(rep(1:5, each=6)) treatment <- rep(c("A","B","C"),5) block_shift <- rnorm(5, mean = 0, sd = 2) treatment_shift <- c(A=0, B=4, C=2) random_effect <- rnorm(30, mean = 0, sd = 1) yield <- rnorm(30, mean = 10, sd = 2) + treatment_shift[as.integer(factor(treatment))] + block_shift[as.numeric(block)] + random_effect data <- data.frame(block, treatment, yield) summary(fm1 <- aov(yield ~ treatment + block, data = data)) # Df Sum Sq Mean Sq...)
- 21:50, 14 January 2023 Brb talk contribs uploaded File:RbdTreat.png (<pre> set.seed(1234) block <- as.factor(rep(1:5, each=6)) treatment <- rep(c("A","B","C"),5) block_shift <- rnorm(5, mean = 0, sd = 2) treatment_shift <- c(A=0, B=4, C=2) random_effect <- rnorm(30, mean = 0, sd = 1) yield <- rnorm(30, mean = 10, sd = 2) + treatment_shift[as.integer(factor(treatment))] + block_shift[as.numeric(block)] + random_effect data <- data.frame(block, treatment, yield) summary(fm1 <- aov(yield ~ treatment + block, data = data)) # Df Sum Sq Mean Sq...)
- 12:18, 12 January 2023 Brb talk contribs uploaded File:GseaTable2.png (An example of a plot from 10 non-enriched pathways. <pre> data(examplePathways) data(exampleRanks) fgseaRes <- fgsea(examplePathways, exampleRanks, nperm=1000, minSize=15, maxSize=100) fgseaRes[order(pval, decreasing = T),][1:10, c('NES', 'pval')] # NES pval # 1: -0.4050950 1.0000000 # 2: -0.4050950 1.0000000 # 3: -0.4966664 0.9932584 # 4: 0.4804114 0.9870610 # 5: 0.4804114 0.9870610 # 6: 0.4804114 0.9870610 # 7: 0.4804114 0.9870610 # 8: 0.4955139 0.9854...)
- 12:01, 12 January 2023 Brb talk contribs uploaded File:GseaTable.png (<pre> data(examplePathways) data(exampleRanks) fgseaRes <- fgsea(examplePathways, exampleRanks, nperm=1000, minSize=15, maxSize=100) # I pick 5 pathways with + NES and 5 pathways with - NES. fgseaRes[order(pval), ][62:71, c('pathway', 'NES')] # pathway NES # 1: 5992282_ECM_proteoglycans 1.984081 # 2: 5992219_Regulation_of_cholesterol_biosynthesis_by_SREBP_SREBF_ 1.95...)
- 12:15, 8 January 2023 Brb talk contribs uploaded File:Reorder.dendrogram.png (<pre> set.seed(123) x <- rnorm(20) hc <- hclust(dist(x)) dd <- as.dendrogram(hc) par(mfrow=c(3, 1)) plot(dd, main = "random dendrogram 'dd'") # not the same as reorder(dd, 1:20) plot(reorder(dd, 20:1), main = 'reorder(dd, 20:1, sum)') plot(reorder(dd, 20:1, mean), main = 'reorder(dd, 20:1, mean)') </pre>)
- 21:37, 7 January 2023 Brb talk contribs uploaded File:ComplexHeatmap2.png (<syntaxhighlight lang="rsplus"> # Simulate data library(ComplexHeatmap) ng <- 30; ns <- 20 set.seed(1) mat <- matrix(rnorm(ng*ns), nr=ng, nc=ns) colnames(mat) <- 1:ns rownames(mat) <- 1:ng # color bar on RHS ind_e <- 1:round(ng/3) ind_m <- (1+round(ng/3)):ng epimes <- rep(c("epi", "mes"), c(length(ind_e), length(ind_m))) row_ha <- rowAnnotation(epimes = epimes, col = list(epimes = c("epi" = "orange", "mes" = "darkgreen"))) # color bar on Top tumortype <- rep(c("carcinoma", "sarcoma"...)
- 12:37, 6 January 2023 Brb talk contribs uploaded File:Fgsea3plots.png (<pre> par(mfrow=c(1,3)) with(fgseaRes, plot(abs(ES), pval)) with(fgseaRes, plot(abs(NES), pval)) with(fgseaRes, plot(ES, NES)) </pre>)
- 14:58, 31 December 2022 Brb talk contribs uploaded File:Filebrowser.png
- 20:07, 25 December 2022 Brb talk contribs uploaded File:DisableDropbox4pm.png
- 15:40, 15 December 2022 Brb talk contribs uploaded File:Geom smooth ex.png (<pre> library(dplyr) #group the data by cyl and create the plots mpg %>% group_by(cyl) %>% ggplot(aes(x=displ, y=hwy, color=factor(cyl))) + geom_point() + geom_smooth(method = "lm", se = FALSE) + theme(legend.position="none") </pre>)
- 15:14, 15 December 2022 Brb talk contribs uploaded File:Geom bar4.png (<pre> ggplot(mpg, aes(x = class)) + geom_vline(xintercept = mpg$class, color = "grey", linetype = "dashed", size = 1) + geom_bar() + theme_classic() + coord_flip() </pre>)
- 15:08, 15 December 2022 Brb talk contribs uploaded File:Geom bar3.png (<pre> ggplot(mpg, aes(x=manufacturer)) + geom_bar() + theme(panel.grid.major.x = element_blank(), panel.grid.minor = element_blank()) </pre>)
- 14:41, 15 December 2022 Brb talk contribs uploaded File:Geom bar2.png (<pre> library(ggplot2) library(scales) library(patchwork) dtf <- data.frame(x = c("ETB", "PMA", "PER", "KON", "TRA", "DDR", "BUM", "MAT", "HED", "EXP"), y = c(.02, .11, -.01, -.03, -.03, .02, .1, -.01, -.02, 0.06)) set.seed(1) dtf2 <- data.frame(x = dtf[, 1], y = sample(dtf[, 2])) g1 <- ggplot(dtf, aes(x, y)) + geom_bar(stat = "identity", fill = "#F8767D") + geom_text(aes(label = paste0(y * 100, "%"), hjust = ifelse(y >= 0, 0, 1))) +...)
- 14:39, 15 December 2022 Brb talk contribs uploaded File:Geom bar1.png (<pre> library(ggplot2) library(scales) library(patchwork) dtf <- data.frame(x = c("ETB", "PMA", "PER", "KON", "TRA", "DDR", "BUM", "MAT", "HED", "EXP"), y = c(.02, .11, -.01, -.03, -.03, .02, .1, -.01, -.02, 0.06)) set.seed(1) dtf2 <- data.frame(x = dtf[, 1], y = sample(dtf[, 2])) g1 <- ggplot(dtf, aes(x, y)) + geom_bar(stat = "identity", aes(fill = x)) + geom_text(aes(label = paste0(y * 100, "%"), hjust = ifelse(y >= 0,...)
- 18:00, 5 December 2022 Brb talk contribs uploaded File:GpartedinfoSanDisk.png
- 15:55, 29 October 2022 Brb talk contribs uploaded File:ExampleRanks.png (<pre> plot(exampleRanks) </pre>)
- 15:39, 29 October 2022 Brb talk contribs uploaded File:FgseaPlotSmallm.png
- 15:15, 27 October 2022 Brb talk contribs uploaded File:DHdialog2.png
- 15:15, 27 October 2022 Brb talk contribs uploaded File:DHdialog1.png
- 09:46, 27 October 2022 Brb talk contribs uploaded File:HC single.png
- 10:48, 20 October 2022 Brb talk contribs uploaded File:1NN better NC.png
- 17:26, 19 October 2022 Brb talk contribs uploaded File:NC better kNN.png (The green color is a new observation (Sensitive). By using the kNN method, it will be assigned to Resistant b/c it is closer to the Resistant group. However, using the NC, the distance of it to the Resistant group centroid is 8.42 which is larger than the distance of it to the Sensitive groups centroid 7.31. So NC classified it to Sensitive. Color annotation: green=LOO obs, black=centroid in each class.)
- 11:03, 12 October 2022 Brb talk contribs uploaded File:Foldchangefilter.png (<pre> LFC <- log2(1.5) x <- c(0, 0, 0, 0, 0, 0, 0, 0, 3.22, 0, 0, 0, 8.09, 0, 0.65, 0, 0, 0, 0, 0, 3.38, 0, 5.63, 7.46, 0, 0, 4.38, 0) plot(x, y = 1:28, xlab="log2 intensity", ylab="samples") abline(v=LFC, lty="dashed") axis(side=3,at=LFC, labels="LFC", tick=FALSE, line=0) </pre>)
- 16:39, 11 October 2022 Brb talk contribs uploaded File:Cvglmnetplot.png (<pre> n <- 100 set.seed(1) x1 <- rnorm(n) e <- rnorm(n)*.01 y <- x1 + e x4 <- x fit <- cv.glmnet(x=cbind(x1, x4, matrix(rnorm(n*10), nr=n)), y=y) plot (fit) </pre>)
- 14:12, 6 October 2022 Brb talk contribs uploaded File:Greedypairs.png
- 11:11, 6 October 2022 Brb talk contribs uploaded a new version of File:Barplot base.png
- 11:06, 6 October 2022 Brb talk contribs uploaded File:Barplot ggplot2.png
- 11:06, 6 October 2022 Brb talk contribs uploaded File:Barplot base.png
- 11:50, 30 August 2022 Brb talk contribs uploaded File:Geomcolviridis.png (Modify the example from https://datavizpyr.com/re-ordering-bars-in-barplot-in-r/ to allow filled colors and facet. <pre> library(tidyverse) library(gapminder) library(viridis) theme_set(theme_bw(base_size=16)) pop_df <- gapminder %>% filter(year==2007)%>% group_by(continent) %>% summarize(pop_in_millions=sum(pop)/1e06) pop_df2 <- tibble(class=rbinom(nrow(pop_df), 1, .5), pop_df) pop_df2 <- pop_df2 |> mutate(pop_in_millions = pop_in_millions-1900) pop_df2 %>% ggplot(aes...)
- 10:31, 30 August 2022 Brb talk contribs uploaded File:ViridisDefault.png (<pre> library(viridis) n = 200 image( 1:n, 1, as.matrix(1:n), col = viridis(n, option = "D"), xlab = "viridis n", ylab = "", xaxt = "n", yaxt = "n", bty = "n" ) </pre>)
- 10:08, 30 August 2022 Brb talk contribs uploaded File:ScaleFillViridisDiscrete.png (See https://r-graph-gallery.com/79-levelplot-with-ggplot2.html <pre> library(ggplot2) # library(hrbrthemes) # Dummy data x <- LETTERS[1:20] y <- paste0("var", seq(1,20)) data <- expand.grid(X=x, Y=y) data$Z <- runif(400, 0, 5) library(viridis) ggplot(data, aes(X, Y, fill= Z)) + geom_tile() + scale_fill_viridis(discrete=FALSE) </pre>)
- 14:19, 29 August 2022 Brb talk contribs uploaded File:Rbiomirgs barall.png
- 14:17, 29 August 2022 Brb talk contribs uploaded File:Rbiomirgs bar.png
- 14:17, 29 August 2022 Brb talk contribs uploaded File:Rbiomirgs volcano.png
- 07:08, 27 August 2022 Brb talk contribs uploaded File:FgseaPlotTop.png
- 06:34, 27 August 2022 Brb talk contribs uploaded File:FgseaPlotSmall2.png
- 06:34, 27 August 2022 Brb talk contribs uploaded File:FgseaPlotSmall.png
- 06:33, 27 August 2022 Brb talk contribs uploaded File:FgseaPlot.png
- 15:02, 23 August 2022 Brb talk contribs uploaded File:ComplexHeatmap1.png (<pre> library(ComplexHeatmap) set.seed(123) ng <- 10; n <- 10 mat = matrix(rnorm(ng * n), n) rownames(mat) = paste0("R", 1:ng) colnames(mat) = paste0("C", 1:n) bin <- sample(c("resistant", "sensitive"), n, replace = TRUE) tgi <- runif(n) # sort the columns by tgi ord <- order(tgi) col_fun = circlize::colorRamp2(range(tgi), c("#DEEBF7", "#084594")) column_ha = HeatmapAnnotation(tgi = tgi[ord], bin = bin[ord], col = list(tgi = col_fun,...)
- 14:19, 22 August 2022 Brb talk contribs uploaded File:Doubledip.png (<pre> ng <- 1000 # number of genes ns <- 100 # number of samples k <- 2 # number of groups alpha <- .001 # cutoff of selecting sig genes set.seed(1) x = matrix(rnorm(ng * ns), nr= ns) # samples x features hc = hclust(dist(x)) plot(hc) grp = cutree(hc, k=k) # vector of group membership ex <- t(x) r1 <- genefilter::rowttests(ex, factor(grp)) sum(r1$p.value < alpha) # 2 hist(r1$p.value) i <- which(r1$p.value < alpha) i1 <- i[1] ; i2 <- i[2] plot(x[, i1], x[, i2], col = grp, pch= 16, cex=1...)
- 10:21, 10 August 2022 Brb talk contribs uploaded File:Ruspini.png (library(cluster) # ruspini is 75 x 2 data(ruspini) hc <- hclust(dist(ruspini), "ave"); plot(hc) # si <- silhouette(groups, dist(ruspini)) # plot(si, main = paste("k = ", 4)) op <- par(mfrow= c(3,2), oma= c(0,0, 3, 0), mgp= c(1.6,.8,0), mar= .1+c(4,2,2,2)) plot(hc) for(k in 2:6) { groups<- cutree(hc, k=k) si <- silhouette(groups, dist(ruspini)) plot(si, main = paste("k = ", k)) } par(op))
- 19:55, 23 June 2022 Brb talk contribs uploaded File:Tidyheatmap.png
- 15:22, 21 June 2022 Brb talk contribs uploaded File:BatchqcPCA.png
- 15:22, 21 June 2022 Brb talk contribs uploaded File:BatchqcDE.png
- 15:21, 21 June 2022 Brb talk contribs uploaded File:BatchqcVariation.png
- 15:20, 21 June 2022 Brb talk contribs uploaded File:BatchqcSummary.png
- 18:02, 4 June 2022 Brb talk contribs uploaded File:Heatmap rdylbu.png
- 10:06, 27 May 2022 Brb talk contribs uploaded File:Inter gg2.png
- 09:51, 27 May 2022 Brb talk contribs uploaded File:Inter gg.png
- 09:51, 27 May 2022 Brb talk contribs uploaded File:Inter base.png
- 09:16, 27 May 2022 Brb talk contribs uploaded a new version of File:Inter base.svg
- 09:04, 27 May 2022 Brb talk contribs uploaded File:Inter base.svg
- 10:08, 26 May 2022 Brb talk contribs uploaded File:LogisticFail.svg (> set.seed(1234); n <- 16; mu=3; x <- c(rnorm(n), rnorm(n, mu)); y <- rep(0:1, each=n) > summary(glm(y ~ x, family = binomial)); plot(x, y))
- 12:56, 10 May 2022 Brb talk contribs uploaded File:ColorRampBlueRed.png