Start wilms analysis

[maud-p]

Purpose/implementation Section

In this module 1, I create 2 metadata tables to compile from the literature information on marker genes and known genetic alterations, that will be used later to validate annotations of the Wilms tumor dataset.

Please link to the GitHub issue that this pull request addresses.

https://github.com/AlexsLemonade/OpenScPCA-analysis/issues/671 https://github.com/AlexsLemonade/OpenScPCA-analysis/discussions/635#discussioncomment-10140478

What is the goal of this pull request?

Wilms tumor (WT) is the most common pediatric kidney cancer characterized by an exacerbated intra- and inter- tumor heterogeneity. The genetic landscape of WT is very diverse in each of the histological contingents. The COG classifies WT patients into two groups: the favorable histology and diffuse anaplasia. Each of these groups is composed of the blastemal, epithelial, and stromal populations of cancer cells in different proportions, as well as cells from the normal kidney, mostly kidney epithelial cells, endothelial cells, immune cells and normal stromal cells (fibroblast).

In this module, we reviewed the literature to compile a table of marker genes for each of the expected cell types in the dataset. Additionally, we provide a table of know genetic alterations in Wilms tumor that can be useful to validate CNV profiles obtained after running inferCNV function.

Briefly describe the general approach you took to achieve this goal.

The table CellType_metadata.csv contains the following column and information:

• "gene_symbol" contains the symbol of the described gene, using the HUGO Gene Nomenclature
• ENSEMBL_ID contains the stable identifier from the ENSEMBL database
• cell_class is either "malignant" for marker genes specific to malignant population, or "non-malignant" for markers genes specific to non-malignant tissue or "both" for marker genes that can be found in malignant as well as non-malignant tissue but are still informative in respect to the cell type.
• cell_type contains the list of the cell types that are attributed to the marker gene
• DOI contains the list of main publication identifiers supporting the choice of the marker gene
• comment can be empty or contains any additional information

The table GeneticAlterations_metadata.csv contains the following column and information:

• alteration contains the number and portion of the affected chromosome
• gain_loss contains the information regarding the gain or loss of the corresponding genetic alteration
• cell_class is "malignant"
• cell_type contains the list of the malignant cell types that are attributed to the marker gene, either blastemal, stromal, epithelial or NA if none of the three histology is more prone to the described genetic alteration
• DOI contains the list of main publication identifiers supporting the choice of the genetic alteration
• comment can be empty or contains any additional information

If known, do you anticipate filing additional pull requests to complete this analysis module?

This module will be used for later validation of the annotations and results from inferCNV.

What is the name of your results bucket on S3?

Results should be uploaded to your bucket so they are available during review. See here for instructions on how to upload to your bucket: https://openscpca.readthedocs.io/en/latest/software-platforms/aws/working-with-s3-buckets/

What types of results does your code produce (e.g., table, figure)?

2 tables

Provide directions for reviewers

This section had 2 aims:

• learn how to build the github repository, perform issue, pull request
• gather literature information into a metadata file for later use for validation of the annotations

• What are the software and computational requirements needed to be able to run the code in this PR?

Are there particularly areas you'd like reviewers to have a close look at?

Is there anything that you want to discuss further?

Author checklists

Check all those that apply. Note that you may find it easier to check off these items after the pull request is actually filed.

Analysis module and review

• [ ] This analysis module uses the analysis template and has the expected directory structure.
• [x ] The analysis module README.md has been updated to reflect code changes in this pull request.
• [ ] The analytical code is documented and contains comments.
• [ ] Any results and/or plots this code produces have been added to your S3 bucket for review.

Reproducibility checklist

• [ ] Code in this pull request has been added to the GitHub Action workflow that runs this module.
• [x] The dependencies required to run the code in this pull request have been added to the analysis module Dockerfile.
• [ ] If applicable, the dependencies required to run the code in this pull request have been added to the analysis module conda environment.yml file.
• [x] If applicable, R package dependencies required to run the code in this pull request have been added to the analysis module renv.lock file.

[jaclyn-taroni]

This looks great! I'm going to close #672 and start my review here.

[maud-p]

Hi @maud-p - I have to head to some meetings, so I will return the feedback I have so far here. 😄

Some high-level thoughts:

• I would move to use the data download script as part of your development process because I think it will make it easier for other people (e.g., reviewers) to run your code over the project's lifecycle.
• Were you able to build this Docker image successfully locally? If so, what kind of machine are you on? I ran into a problem locally, and I think your answers might help us narrow down what's going on here.

Please let us know if you have any questions. Thank you!

Thank you for your feedback @jaclyn-taroni ! I will try to answer.

• I will try to move to the data download script, thanks for the advice below!
• I build this Docker image on my machine. It is our group's compute server, when I log into it say:

"Welcome to Ubuntu 22.04.2 LTS (GNU/Linux 5.15.0-101-generic x86_64)"

System load: 0.79736328125 Processes: 1245 Usage of /: 44.1% of 196.30GB Users logged in: 0 Memory usage: 25%
Swap usage: 51%

I used podman to build the image podman build -t cancerbits/dockr:maudp_ScPCAOpen_podman -f Dockerfile.Dockerfile .

I will try to work on the improvment you suggested below for the Dockerfile, it might also solve the issue.

[maud-p]

Thank you very much!!! I am really happy about it and looking forward the next step of the analysis :)

Thank you for your help setting up all of this!