https://github.com/mikelamiki/thesis_gender_bias
This Repo contains all of the files and the code used in my undergraduate thesis: Gender Bias in Clinical Studies: A Statistical Approach
https://github.com/mikelamiki/thesis_gender_bias
applied applied-statistics contrast-matrix design-matrix differentially-expressed-genes empirical-bayes gender-bias gene-expression geo-datasets limma linear mean-reference-model means-model medical-research microarray-data-analysis r rstudio statistical-bias t-test thesis
Last synced: 10 months ago
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This Repo contains all of the files and the code used in my undergraduate thesis: Gender Bias in Clinical Studies: A Statistical Approach
- Host: GitHub
- URL: https://github.com/mikelamiki/thesis_gender_bias
- Owner: MikelaMiki
- Created: 2025-09-23T15:06:17.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-09-23T16:04:45.000Z (10 months ago)
- Last Synced: 2025-09-23T17:22:58.814Z (10 months ago)
- Topics: applied, applied-statistics, contrast-matrix, design-matrix, differentially-expressed-genes, empirical-bayes, gender-bias, gene-expression, geo-datasets, limma, linear, mean-reference-model, means-model, medical-research, microarray-data-analysis, r, rstudio, statistical-bias, t-test, thesis
- Language: R
- Homepage:
- Size: 5.41 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Thesis_Gender_Bias
This Repo contains all of the files, images, datasets and code used in my undergraduate thesis: _**Gender Bias in Clinical Studies: A Statistical Approach**_.
**Institution:** University of Crete, Deparment of Mathematics and Applied Mathematics
**Supervisor:** Supervisor: Pavlos Pavlidis, Associate Professor, Department of Biology UoC and Affiliated Researcher, ICS-FORTH
_Heraklion 2025_
**Abstract:**
This thesis aims to investigate the impact of gender bias in clinical research, with a
focus on differences in gene expression. The historical exclusion of females in clinical
trials and drug testing has led to significant disparities in healthcare outcomes. To explore
this issue, this thesis statistically analyzes gender-based differences in gene expression
across various conditions. A few basic theoretical concepts are explained before presenting
the methodology used. Using datasets from the GEO database, hypothesis testing is
conducted separately for each dataset. Linear models, applied through the limma package
in R, identify significantly differentially expressed genes between genders. The results are
presented and visualized, highlighting the extent of gender-specific variations in gene
expression.
Keywords: Medical research, gender bias, gene expression, differentially expressed gene
(DEG), statistical bias, microarray, t-test, linear model, limma, design matrix, contrast
matrix, means model, mean-reference model, factor, level, empirical Bayes, GEO datasets
**Datasets:** Below is the list with all of the GEO Datasets used and links to them. Those were not uploaded onto this repositorie due to the size limitations of GitHub.
https://docs.google.com/spreadsheets/d/1zdT-pYzlnM5JREDoc6iLs5po7ZMVYkvogP1svIat6ws/edit?usp=sharing