Docker

 

These exercises follow the material in the Docker section of Reproducible R.

1. Build a Docker image using a Dockerfile with the following components:

• RStudio with R4.4

• The following packages from CRAN: ggplot2 and BiocManager

• The following packages from Bioconductor: Herper

• the pseudo aligner ‘salmon’ from conda

• Confirm that this image is available on your computer

# the dockerfile to build this image is at './data/docker_exercise/Dockerfile_exercise' within the course folders
docker build -t rstudio_4.4.0_salmon -f ./data/docker_exercise/Dockerfile_exercise ./data/docker_exercise/

# Confirm that this image is available on your computer
docker images

2. Launch a container from this image and mount the ‘data’ folder within the course folder structure onto the docker image. Launch RStudio from your browser

• make sure that the files in the ‘data’ folder are visible within the RStudio session within the docker image

# launch from r_course directory in course matrials
cd ~/Downloads/RU_reproducibleR-master/r_course
docker run --rm \
          -v ./data:/home/rstudio \
          -p 8787:8787 \
           -e PASSWORD=password123 \
           rstudio_4.4.0_salmon
           
# go to browser and go to http://localhost:8787/

3. Activate the conda environment that contains salmon and confirm the version that you have installed

Herper::local_CondaEnv("pipe_env", "/home/miniconda")
system("salmon --version")

4. Use salmon to get counts for the fastq files present in the ‘data/docker_exercise’ folder within the course files.

• code for indexing salmon: salmon index -t /path/to/fasta_file.fa -i /path/to/index_destination
• code for pseudo-alignment: salmon quant -i /path/to/index_destination -l A -1 path/to/reads_1.fastq -2 path/to/reads_2.fastq –output path/to/output_dir
• learn more with the salmon manual

system("salmon index -t docker_exercise/transcripts.fasta -i docker_exercise/transcripts_index")
system("salmon quant -i docker_exercise/transcripts_index -l A -1 docker_exercise/reads_1.fastq -2 docker_exercise/reads_2.fastq --output docker_exercise/sample_counts")

5. Read in the quant.sf result file into R and plot a bar graph showing the number of reads for each of the genes in the fasta file.

library(ggplot2)
counts <- read.table("docker_exercise/sample_counts/quant.sf", header = T)
ggplot(counts, aes(x = Name, y = NumReads)) + 
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

6. Push this image to Docker Hub

• HINT: Make sure you create a repository on Docker Hub before pushing.

# Code in terminal 

# log in and provide credentials used to sign into Docker Hub
# this will prompt you to enter username and password
docker login -u username

# get image ID to tag and push 
docker images

# tag the image you want to push with your Docker Hub username and a tag name after the colon
# the ID is from the 'docker images' command
docker tag 2c5152e60109 rubrc/rstudio_4.4.0_salmon:topush

# push to Docker Hub
docker push rubrc/rstudio_4.4.0_salmon:topush