johnsonra

# Load necessary libraries
library(airway)
library(DESeq2)
library(org.Hs.eg.db)
library(dplyr)

# 1. Load the dataset
data("airway")

# 2. Build the DESeq2 object

johnsonra

# This is a version of the Shiny app found at https://github.com/BIFX547-26/ShinyHood for running with `shiny::runGist()`

library(shiny)
library(dplyr)
library(bslib)
library(DT)

# Load course data
courses <- read.csv("https://github.com/BIFX547-26/ShinyHood/raw/refs/heads/main/data/courses.csv", stringsAsFactors = FALSE)

johnsonra

library(dplyr)

# example data
dat <- tibble(sid = c('SAC001', 'SAC002', 'SAC003', 'SAC004', 'SAC005',
                      'SAC006', 'SAC007', 'SAC008', 'SAC009', 'SAC010'),
              theoretical_lab = c(1, 1, 1, 1, 2, 2, 2, 3, 3, 3),
              lab1 = c(2, 1, 1, 3, 2, 1, 3, 3, 3, 3),
              lab2 = c(1, 1, 2, 3, 2, 2, 1, 3, 2, 2),
              lab3 = c(1, 1, 1, 2, 2, 2, 2, 3, 3, 2))

johnsonra

############ nice colorblind pallete found at http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/ ###############
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")


############ another nice pallete from http://mkweb.bcgsc.ca/colorblind/img/colorblindness.palettes.simple.png #############

## ordering from dark to light

# indistinguishible pairs for tritanopia:
#                  *--------------------*          *------------------------------------------------------*          *---------------------*

johnsonra

Safest way to clean up boot partition - Ubuntu 14.04LTS-x64

Reference

Case I: if /boot is not 100% full and apt is working

1. Check the current kernel version

$ uname -r 

johnsonra

if(!require(tidyverse))
  install.packages('tidyverse', dependencies = TRUE)
  
if(!require(seqplots))
{
  source("https://bioconductor.org/biocLite.R")
  biocLite("seqplots", suppressUpdates = TRUE)
}
  
if(!require(ggbio))

johnsonra

# Practice Transformations
# after sourcing this gist, try to figure out what the best transformation is to make the association linear (without looking at the source code)

require(tidyverse)

N <- 500

# log normal association
set.seed(293847)
dat1 <- data_frame(x = rnorm(N),

johnsonra

# practice data for interactions
# this creates 3 data sets, dat1, dat2 and dat3


library(tidyverse)
set.seed(23478)

n <- 100

#################### interaction with a linear spline

johnsonra

# simulated glomerular filtration rate as a function of age, gender and blood pressure

# linearity assumption may be violated, depending on the model
# multivariate normal assumption may be violated
# homoscedasticity assumption may be violated

require(tidyverse)

n <- 500
set.seed(239847)

johnsonra

# clt.R
# function for exploring the Central Limit Theorem
# Randy Johnson

# distn = any function returning random numbers
#         the first argument is assumed to be sample size!
#         Sample size argument should be 'n'
# sample_size = number of random variables to draw for each mean
# ... = arguments to be passed to distn
clt.test <- function(distn, sample_size, seed=NULL, ...)