namini94 / scbalf_hackathon

Biomarker identification for COVID-19 Severity in BALF cells Single-cell RNA-seq data

scBALF Covid-19 dataset Analysis

Here is the Github page that has the codes for the bioinformatics pipeline described in the paper COVID-Datathon: Biomarker identification for COVID-19 severity based on BALF scRNA-seq data.

Biomarker Identification By Monocle:

The scBALF_Monocle.md file from the Monocle folder explains how one can extract the list of potential COVID-19 severity biomarkers using Monocle. The R markdown files are also available in the same folder.

BALF Cells Classification:

In the second step of our proposed pipeline we run multiple linear and non-linear machine learning classification algorithms to perform cell classification ( One lable vs the rest). Here we explain how one can run these classification algorithms:

1. Lindear Discriminant Aanlysis (LDA):

The LDA.md file from the LDA folder explains how one can run perform cell classification using LDA. The R code files are also available in the same folder.

2. Quadratic Discriminant Aanlysis (QDA):

The QDA.md file from the QDA folder explains how one can run perform cell classification using QDA. The R code files are also available in the same folder.

3. Fleaxible Discriminant Aanlysis (FDA):

The FDA.md file from the FDA folder explains how one can run perform cell classification using FDA. The R code files are also available in the same folder.

4. Support Vector Machine (SVM) with RBF kernerl

The SVM.md file from the SVM folder explains how one can run perform cell classification using SVM. The R code files are also available in the same folder.

5. Random Forest (RF)

The RF.md file from the RF folder explains how one can run perform cell classification using RF. The R code files are also available in the same folder.