User contributions for Brb
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17 March 2024
16 March 2024
- 20:4920:49, 16 March 2024 diff hist +332 Rmarkdown →Dropdown menu
- 15:3915:39, 16 March 2024 diff hist +500 Kodi →Youtube
- 13:3413:34, 16 March 2024 diff hist +58 Docker Applications →Dashy
- 13:3313:33, 16 March 2024 diff hist +64 Docker Applications →Dashy
- 12:3312:33, 16 March 2024 diff hist +139 Health →喝茶 (tea) 的注意事項
- 12:3112:31, 16 March 2024 diff hist +189 Docker Applications →Bioinformatics analyses
- 12:3012:30, 16 March 2024 diff hist +188 Backup →Snapshot
- 10:4510:45, 16 March 2024 diff hist +1,046 Rmarkdown →Download button/embed files
15 March 2024
- 16:2816:28, 15 March 2024 diff hist +159 Rmarkdown →Scrollable code/output
- 15:5415:54, 15 March 2024 diff hist +18 Statistics →Agreement
- 15:1115:11, 15 March 2024 diff hist +197 Venn diagram No edit summary
- 14:4914:49, 15 March 2024 diff hist +84 R →15 Questions All R Users Have About Plots
- 07:2007:20, 15 March 2024 diff hist +101 Rmarkdown →Quarto dashboard
14 March 2024
- 08:3708:37, 14 March 2024 diff hist +138 ICC →Ordinal data
13 March 2024
- 21:2621:26, 13 March 2024 diff hist +221 Rmarkdown →Quarto
- 21:2321:23, 13 March 2024 diff hist +84 Genome →edgeR::filterByExpr
12 March 2024
- 19:2019:20, 12 March 2024 diff hist +168 Genome →Independent Filtering
- 19:1719:17, 12 March 2024 diff hist +528 N 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) <... current
- 16:0716:07, 12 March 2024 diff hist +192 Ggplot2 →Scatterplot with large number of points: alpha
- 12:0812:08, 12 March 2024 diff hist +206 Genome →Independent Filtering
- 10:4110:41, 12 March 2024 diff hist +434 Browser →Privacy-focused browsers
- 10:3810:38, 12 March 2024 diff hist +194 Genome →Ensembl to gene symbol
- 09:4509:45, 12 March 2024 diff hist +97 Javascript →npm install
- 09:4309:43, 12 March 2024 diff hist +111 Linux →login shell (.bash_profile) vs interactive shell (.bashrc)
- 09:4209:42, 12 March 2024 diff hist +112 NAS →Zimablade
- 09:3909:39, 12 March 2024 diff hist +134 Rstudio →Server
- 09:3509:35, 12 March 2024 diff hist +112 Shiny →Deploy on Github Pages
- 09:2809:28, 12 March 2024 diff hist +142 Self hosting →Network
- 09:2109:21, 12 March 2024 diff hist +705 Raspberry →Share internet
- 09:1109:11, 12 March 2024 diff hist +134 Raspberry →Share internet
- 08:0708:07, 12 March 2024 diff hist +8 Office →Status/presence icons
- 08:0408:04, 12 March 2024 diff hist +777 Ggplot2 →palette()
- 07:5407:54, 12 March 2024 diff hist 0 N File:Rainbow v05.png No edit summary current
- 07:5307:53, 12 March 2024 diff hist 0 N File:Rainbow s05.png No edit summary current
- 07:5207:52, 12 March 2024 diff hist +774 N 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 <-... current
- 07:2907:29, 12 March 2024 diff hist +412 Genome →Independent Filtering
11 March 2024
- 21:3821:38, 11 March 2024 diff hist −12 Genome →Independent Filtering
- 20:5320:53, 11 March 2024 diff hist +922 Genome →Low read count and filtering
- 20:4120:41, 11 March 2024 diff hist +375 N 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> current
- 20:3520:35, 11 March 2024 diff hist +3,823 N 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")... current
- 16:2016:20, 11 March 2024 diff hist +366 Statistics →Correlated data
- 11:3711:37, 11 March 2024 diff hist +555 Genome →Low read count and filtering
- 10:1710:17, 11 March 2024 diff hist +1,447 Genome →DNA methylation, Epigenetics
10 March 2024
- 19:1019:10, 10 March 2024 diff hist +227 Genome →TCGAbiolinks
9 March 2024
- 21:5721:57, 9 March 2024 diff hist +55 NAS →Zimablade
- 21:5321:53, 9 March 2024 diff hist +834 NAS →ODroid xu4
- 18:5718:57, 9 March 2024 diff hist +23 Docker →CasaOS
- 18:5218:52, 9 March 2024 diff hist +137 Docker →CasaOS