stemangiola / hpcell Goto Github PK

• you can drop this

Clone the repository

git clone [email protected]:susansjy22/HPCell.git

• you should point to the main repo in my github

replace this

remote::install_github("[email protected]:susansjy22/HPCell.git")

with

remote::install_github("stemangiola/HPCell")

• Move this, below, where you give another example of execution with reference. The base example should not include this

# Load reference data 
input_reference_path <- "reference_azimuth.rds"
reference_url<- "https://atlas.fredhutch.org/data/nygc/multimodal/pbmc_multimodal.h5seurat"
download.file(reference_url, input_reference_path)
LoadH5Seurat(input_reference_path) |> saveRDS(input_reference_path)

• store should be "./" , because this is already the default, you can drop the store variable

store <- "~/HPCell/pipeline_store"

• Drop the default arguments, let's keep it simple

preprocessed_seurat = run_targets_pipeline(
    input_data = file_path, 
    tissue = tissue,
    filter_input = TRUE, 
    RNA_assay_name 
)

It is better to split the pseudobulk generation be sample, that can be run in parallel, and leave an overall step just for the merging.

Here there are some additional explanation regarding the last branches I created:

1. renv-optional: in this branch the library rent was deactivated (renv::deactivate). This was done due to the many problems performing the Jascap pipeline for the Jian/Ajith project. The following branches were created also based on the rent::deactivate mode.

2. Jian_no azimuth_annotation: Jian project contains mouse-data, thus the reference azimuth was not useful. Even though we left the other human annotations in, the azimuth was giving problem, thus, the command lines containing it were momentarily removed. In this branch, something went wrong, and can therefore be deleted! The correct branch without the azimuth reference is the below one: Jian_2.

3. Jian_2: the commands in this branch works without stopping until the pre-processing script. The next step is the pseudobulk scripts, and there are still some bugs to correct.
   This branch doesn't include renv() library, as mentioned above it was deactivated, and doesn't contain any azimuth reference.

Next steps are:

• fixing bugs from the pseudobulk R script
• adding reports to pipeline

this line

has a default value of input_reference_path, while it should have value NULL.

This is because input_reference_path does not exist anywhere.

@susansjy22 , here @aodainic7 will be able to provide some code.

Rather than only testing if a function has worked and is a S3 object, each test should test specific properties of the output of a function. For example

• The annotation step, produces a tibble output with 3 column, if seurat_reference is set
• The annotation step, produces a tibble output with 2 column, if seurat_reference is NOT set
• The empty droplet filtering step, produce an output with less cells (rows) than the seurat input (cols)
• and so on..

Some alternatives are

• HPCell
• cellScheduler

• exhaustion and residency scores
• Cell type consensus
• Cell communication
• ...

Take as example dplyr

https://github.com/tidyverse/dplyr/blob/main/R/arrange.R

You don't have to be that comprehensive, just add the main components

Propose and build reporting for each analysis step

https://docs.ropensci.org/tarchetypes/reference/tar_render.html

Some comments that may be useful in the instructions:

1. If no metadata.rds is available: create a data frame with 2 columns (sample | batch) with the samples matching the sample name in the input file. Set Batch as Sample_name.

2. add the module load R/4.2.1 before calling the renv1 or Rstudio … function