HNSCPred :- A Tool for Identification of HNSCC from Single Cell Genome using Deep Learning

A computational approach tool to predict Head and Neck Cancer affected patients from their single cell RNA seq data.

Introduction

Head and neck cancer, which encompasses a range of malignancies affecting the respiratory tract and upper digestive tract and also the seventh most common cancer in the world.

This tool aims to use Artifical Neural Network (Deep Learning) model to classify Normal Control(NC) patients and Head and Neck Cancer (HNSCC) patients from their single cell RNA seq data. The tool takes 10x single cell genomics data as input and predicts whether the patient is diseased or healthy with the help of highly trained model.

An excellent feature selection method called mRMR (Minimum Redundancy Maximum Relevance) was used to find out top 100 features which act as promising biomarkers in classification and prediction of Normal and Diseased patients. Also further classified diseased patients into HPV+ and HPV-.

Reference

Reference: Jarwal A., Dhall A., Arora A., Patiyal S., Srivastava A. and Raghava GPS (2024) A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics. Frontiers in Molecular Biosciences DOI=10.3389/fmolb.2024.1395721

Installation

Install my-project with pip

  pip install HNSCPred

You if previously installed please update the python package to the latest version using the command below

  pip3 install --upgrade HNSCPred

Usage/Examples

After installation of the HNSCPred package in your python enviornment. Import the library using the below code.

import HNSCPred

The HNSCPred comes with 1 inbuilt module.

• Predict Please import only 1 module in your python enviornment using the code below.

from HNSCPred import Validation

After importing all the important pre requisites. You can follow the demo below for your case.

import pandas as pd
df = pd.read_csv("Your file path here")

Validation.predict(df)

Authors

• Akanksha Jarwal.
• Aman Srivastava.
• Anjali Dhall.
• Sumeet Patiyal.
• Prof. G.P.S. Raghava