arraytools

# References:
# https://cran.r-project.org/bin/linux/debian/
# https://rviews.rstudio.com/2018/03/21/multiple-versions-of-r/
# 2023/12/30

sudo nano /etc/apt/sources.list # deb-src http://deb.debian.org/debian/ unstable main
sudo apt update
sudo apt build-dep r-base

wget https://cran.r-project.org/src/base/R-4/R-4.3.2.tar.gz

arraytools

arraytools

arraytools

Download Raw counts
```{r}
library(TCGAbiolinks)

proj <- "TCGA-GBM"
query <- GDCquery(
    project = proj,
    data.category = "Transcriptome Profiling", 
    data.type = "Gene Expression Quantification",
    workflow.type = "STAR - Counts"

arraytools

sudo singularity build rnaseq3 rnaseq3.def
./rnaseq3 cat /etc/os-release  # debian 11, host=ubuntu 2204
./rnaseq3 samtools --version # 1.10
./rnaseq3 STAR --version # 2.5.2b
./rnaseq3 fastqc -v # v0.12.1
./rnaseq3 kallisto | head 1 # 0.50.1
./rnaseq3 subread  # command not found
./rnaseq3 featureCounts -v | head # v2.0.6  
./rnaseq3 ls /opt/conda/bin
./rnaseq3 which gcc  # 10.2.1

arraytools

library(survival)

# Function to generate survival data
generate_data <- function(sample_size, event_rate_group1, event_rate_group2) {
  group <- c(rep(1, sample_size), rep(0, sample_size))
  event_times <- c(rexp(sample_size, rate = event_rate_group1),
                   rexp(sample_size, rate = event_rate_group2))
  observed <- rep(1, sample_size * 2)  # Assuming all events are observed
  data <- data.frame(group = group, event_times = event_times, observed = observed)
  return(data)

arraytools

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)
    ),

arraytools

import requests
from requests.auth import HTTPBasicAuth

username = "USERNAME"
app_password = "API_PASSWORD"
base_url = f"https://api.bitbucket.org/2.0/repositories/{username}"

def fetch_repositories(url):
    response = requests.get(url, auth=HTTPBasicAuth(username, app_password))
    response.raise_for_status()  # Raises an error for bad status codes

arraytools

calculate_total <- function(items) {
  subtotal <- sum(items)
  tax_rate <- getOption("TAX_RATE", default = 0.1)  # Default 10% tax
  total <- subtotal * (1 + tax_rate)
  return(round(total, 2))
}

# Test the function with default tax rate
cart1 <- c(10, 20, 30)
print(calculate_total(cart1))  # Output: 66.00

arraytools

1. The Elevator Pitch Summary
Prompt: "Summarise this paper in 3–5 sentences, as if you're explaining it to a colleague in an elevator ride."

2. Key Findings Extraction
Prompt: "List the top 5 key findings or conclusions from this paper, along with a brief explanation of each."

3. Methodology Breakdown
Prompt: "Explain the methodology used in this study in simple terms. What are its strengths and potential limitations?"

4. Literature Review Assistant