PC3 Cell Analysis

This is a micro services based web application for analysing the prostrate cancer cells. This application gets data from FCS file uploaded by the user and then using Flow Cytometry Tools in Python the results are shown visually on a web application.

The following tools and technologies have been used in this project.

Python	Docker

DEMO APP

LINK 1

LINK 2

screenshot1	screenshot2

Add SSH key to Github

ssh-keygen
cat ~/.ssh/id_rsa.pub
Copy and paste to github

Installation

Docker

sudo apt update
sudo apt install apt-transport-https ca-certificates curl software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu bionic stable"
sudo apt update
apt-cache policy docker-ce
sudo apt install docker-ce
sudo systemctl status docker

Docker compose

sudo curl -L https://github.com/docker/compose/releases/download/1.21.2/docker-compose-`uname -s`-`uname -m` -o /usr/local/bin/docker-compose

sudo chmod +x /usr/local/bin/docker-compose
docker-compose --version

Clone github repository and run application

Clone repo git clone git@github.com:iloveyii/sdg-project.git
cd sdg-project
Pull some images from hub.docker.com

    docker pull node:8
    docker pull alikth/basic_cell:latest
    docker pull alikth/ml_cell:latest

Run docker-compose sudo docker-compose up
After the containers are up browse to: http://localhost

Directory structure

This application has been divided into two major modules, one for frontend and the second for backend. The front end provides user interface and has been developed in React. The backend provides the different APIs for analysis of the FCS files and has been developed in Python, Django, Node.

Frontend

The frontend has further subdirectories for components, pages, context and themes.
The component directory contains all the React components used
The pages provide different containers
The context is using React hook context api.
Themes contains different themes available for the frontend.

Backend

The directories (basicanalysis, classification, machinelearning, node_auth_server, plotting) are the microservices developed in Python.
Each service has basically two files, i.e api.py and service_name.py (e.g basic.py, classification.py etc)
The api.py is a restful service in Flask, which gets data from the related class and sends it to API gateway service.
The main API gateway is contained in djangoweb directory and written in Python Django framework.
The sub directory djangoweb/api is where the code from API gateway resides and has all the models, migrations, templates, router (url.py) and main controller(views.py).
The request coming from browser is first checked in the url.py. If it matches a path then the associated method in the views.py is called.
The methods in views.py in turn calls the different services and sends their response to the frontend.
The shared directory contains all the plots generated by all the services, which are served to the browser.

How it works ?

The backend is based on micro services architecture using Docker containers.
Each sub directory e.g (classification, machinelearning) correspond to a docker container. Each container provides a service to the application.
For example the db container provide service about database. All the services of the application are listed and configured in the docker-compose.yml.
This separation and organization of code into different directories give the freedom of easily maintaining and deployment, plus stability in the code. It also provide easy scalability by popping up more containers and services in an easy and independent way.
The backend has a gateway service which handle each request from the frontend. The inner containers/services are not directly accessible from outside which makes it more secure.
When the user first login to the system the gateway service routes the request to the node auth server which returns json web token to the gateway. The gateway sends the token to the user browser which stores it in cookies and uses it for further communication with the gateway.
After the login the user can call the gateway for any service for example the frontend sends request for basic analysis, which the gateway receives and send it to the basicanalysis service internally.
As soon as the gateway gets the response from any service it is then returned to the frontend/browser. This process is illustrated in the figure above (section: system architecture in final_report.pdf).

Contributions

Two docker images prepare for this project and hosted on Docker Hub
A premium droplet deployed on Digital Ocean.
A premium EC2 deployed on AWS.
An open source project for Cancer cell analysis using Flow Cytometry on github.

iloveyii / sdg-project