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scDehashR

The purpose of this app is to develop an interactive Shiny portal that can dehash any number of pooled samples from single cell techniques for easy visualization & downstream analysis for Bench scientists.

Table of Contents

Background

Include background on the project, project description, and significance. This will be converted to your team's abstract by the end of the hackathon. This should be updated by Monday, August 1st to include feedback given.

Data

Discuss the data you used and how it can be accessed.

Usage

How will someone not involved in your project be able to run the code or use it.

Installation

If installation is required, please mention how to do so here.

Installation simply requires fetching the source code. Following are required:

  • Git

To fetch source code, change in to directory of your choice and run:

git clone -b main \
    [email protected]:u-brite/team-repo-template.git

Requirements

Note any software used (including Python or R packages), operating system requirements, etc. and its version so that your project is reproducible. It does not have to be in the below format

OS:

Currently works only in Linux OS. Docker versions may need to be explored later to make it useable in Mac (and potentially Windows).

Tools:

  • Anaconda3
    • Tested with version: 2020.02

Activate conda environment

Optional: Depends on project.

Change in to root directory and run the commands below:

# create conda environment. Needed only the first time.
conda env create --file configs/environment.yaml

# if you need to update existing environment
conda env update --file configs/environment.yaml

# activate conda environment
conda activate testing

Steps to run

Optional: Depends on project.

Step 1

python src/data_prep.py -i path/to/file.tsv -O path/to/output_directory

Step 2

python src/model.py -i path/to/parsed_file.tsv -O path/to/output_directory

Output from this step includes -

output_directory/
├── parsed_file.tsv               <--- used for model
├── plot.pdf- Plot to visualize data
└── columns.csv - columns before and after filtering step

Note: The is an example note with a link.

Results

If your project yielded or intends to yield some novel analysis, please include them in your readme. It can be named something other than results as well.

Team Members

Tarun Mamidi | [email protected] | Team Leader
Shaurita Hutchins | [email protected] | Co-leader