YogiOnBioinformatics / Differential-Gene-Expression-Analysis-with-Machine-Learning-Using-Dexamethasone-Treatment-Data

Worked with fellow peers at University of Pittsburgh under supervision of Dr. Junshu Bao, Department of Statistics, University of Pittsburgh, to do a Differential Gene Expression Analysis on a Dexamethasone treatment data set and incorporated Machine Learning into project.

Differential Gene-Expression Analysis with Machine Learning Using Dexamethasone Treatment Data

Introduction

Worked with Nityam Rathi, Matt Eckelmeyer and Kevin Coleman at University of Pittsburgh under supervision of Dr. Junshu Bao, Department of Statistics, University of Pittsburgh.

The purpose of this project was to do a Differential Gene Expression Analysis on a Dexamethasone Treatment data set and incorporate Machine Learning to predict which genes could be differentially expressed.

The datasets used are found under the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) accession number GSE6711. We specifically used files GSM154262.txt, GSM154263.txt, GSM154264.txt, GSM154277.txt, GSM154278.txt, GSM154279.txt.

Dexamethasone specifically affects cell growth and can cause apoptosis (cell death) as specified here.

Files

📜 Source Code.Rmd

Contains the R Markdown code needed to create the final PDF file.

📜 Dexamethasone Treatment Differential Gene Expression Analysis with Machine Learning.pdf

Final PDF file outputted from knitting the source code. Contains all goals, steps, explanations of procedure for this research.

Contact Information

Yogindra Raghav (YogiOnBioinformatics)

[e-mail]