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# Note: using the devel versions of both packages! | |
library(DESeq) # version 1.9.11 | |
library(edgeR) # version 2.99.8 | |
library(VennDiagram) | |
# Read in data ------------------------------------------------------------ | |
## Use pasilla data | |
datafile = system.file( "extdata/pasilla_gene_counts.tsv", package="pasilla" ) | |
datafile |
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## I came across a Chinese science blog which discusses an interesting stuff that girls should reject | |
## the first 37% (for 1/e = 36.79%) pursuers in her life. A little R simulation as outlined in the | |
## gist is shown below just for fun. In the snippet, m is the total number of boys you can choose. | |
## In other words, they are potential husband for you. Larger number means high "quality" but these | |
## numbers are randomly sampled, i.e., you don’t know whether the one who just walks into your life | |
## is your Mr. Right or not. You reject the first m/e people (probably without any reason...), where | |
## e is the Euler’s number, and choose from the rest of the m people. If you find any one of the | |
## remaning boys better than the best boy you saw earlier in the first m/e ones, then you choose that | |
## guy; otherwise, you have to make do with the last guy for the rest of your life... |
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# A simple approach to visually-weighted regression plots | |
doInstall <- TRUE # Change to FALSE if you don't want packages installed. | |
toInstall <- c("ggplot2", "reshape2", "MASS") | |
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")} | |
lapply(toInstall, library, character.only = TRUE) | |
# Generate some data: | |
nn <- 1000 | |
myData <- data.frame(X = rnorm(nn), |
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# Simple ggplot2 heatmap | |
# with colorBrewer "spectral" palette | |
doInstall <- TRUE # Change to FALSE if you don't want packages installed. | |
toInstall <- c("ggplot2", "reshape2", "RColorBrewer") | |
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")} | |
lapply(toInstall, library, character.only = TRUE) | |
# Generate a random matrix | |
# This can be any type of numeric matrix, |
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# Randomly allocating observations into groups, for, e.g. cross-validation | |
kk <- 10 # Number of partitions, as in "kk-fold cross-validation." | |
# Here is a data.frame full of good data: | |
nn <- 1003 | |
myData <- data.frame(matrix(rnorm(nn * 3), ncol = 3)) | |
colnames(myData) <- LETTERS[1:3] | |
# This does not work: | |
whichK <- sample(LETTERS[1:kk], nrow(myData), replace = T) |
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#' Constructor | |
EmailClass <- function(name, email) { | |
nc = list( | |
name = name, | |
email = email, | |
get = function(x) nc[[x]], | |
set = function(x, value) nc[[x]] <<- value, | |
props = list(), | |
history = list(), | |
getHistory = function() return(nc$history), |
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# Simple ggplot2 heatmap, with optimal seriation | |
doInstall <- TRUE # Change to FALSE if you don't want packages installed. | |
toInstall <- c("ggplot2", "reshape2", "RColorBrewer", "seriation") | |
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")} | |
lapply(toInstall, library, character.only = TRUE) | |
# Using U.S. Judge Rating Data | |
myData <- as.matrix(USJudgeRatings) |
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heatmap.3=function (x, Rowv = TRUE, Colv = if (symm) "Rowv" else TRUE, | |
distfun = dist, hclustfun = hclust, dendrogram = c("both", | |
"row", "column", "none"), symm = FALSE, scale = c("none", | |
"row", "column"), na.rm = TRUE, revC = identical(Colv, | |
"Rowv"), add.expr, breaks, symbreaks = max(x < 0, na.rm = TRUE) || | |
scale != "none", col = "heat.colors", colsep, rowsep, | |
sepcolor = "white", sepwidth = c(0.05, 0.05), cellnote, notecex = 1, | |
notecol = "cyan", na.color = par("bg"), trace = c("column", | |
"row", "both", "none"), tracecol = "cyan", hline = median(breaks), | |
vline = median(breaks), linecol = tracecol, margins = c(5, |
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# http://www.r-bloggers.com/example-10-7-fisher-vs-pearson/ | |
# Author: Ken Kleinman | |
# In R, we'll simulate observations from a multinomial distribution | |
# with the desired cell probabilities, and assemble the result into | |
# a table to calculate the p-values. This will make it easier to simulate | |
# tables under the alternative, as we need to do to assess power. | |
# If there are empty rows or columns, the chisq.test() function produces | |
# a p-value of "NaN", which will create problems later. To avoid this, | |
# we'll put the table generation inside a while() function. This operates |
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doInstall <- TRUE # Change to FALSE if you don't want packages installed. | |
toInstall <- c("ReadImages", "reshape", "ggplot2") | |
if(doInstall){install.packages(toInstall, repos = "http://cran.r-project.org")} | |
lapply(toInstall, library, character.only = TRUE) | |
# Image URL: | |
allImageURLs <- c("http://media.charlesleifer.com/blog/photos/thumbnails/akira_940x700.jpg", | |
"http://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Mona_Lisa%2C_by_Leonardo_da_Vinci%2C_from_C2RMF_retouched.jpg/402px-Mona_Lisa%2C_by_Leonardo_da_Vinci%2C_from_C2RMF_retouched.jpg", | |
"http://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Official_portrait_of_Barack_Obama.jpg/441px-Official_portrait_of_Barack_Obama.jpg", | |
"http://cache.boston.com/universal/site_graphics/blogs/bigpicture/obama_11_05/obama22_16604051.jpg", |
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