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This repository contains the differential expression analysis and normalized count matrices of the GEO dataset GSE94464. The analysis aims to visualize gene expression abundance and identify significant changes in gene expression across conditions.

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DishaNashier/GEO-data-analysis

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GEO-data-analysis

This repository contains a comprehensive analysis of the GEO dataset GSE94464, focusing on differential expression analysis and the normalization of count matrices. The primary aim of this project is to visualize gene expression abundance and identify significant changes in gene expression across various conditions within the dataset. This analysis is crucial for understanding the molecular mechanisms and potential biomarkers associated with the conditions studied in GSE94464. More details about GEO data set- https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE94464

The repository is organized to provide a clear and reproducible workflow. It includes the following key components:

Raw Data: The original count matrices from the GEO GSE94464 dataset are stored here. This allows users to trace back to the original data source, compare results, or perform alternative analyses if needed. Raw data can be downloaded from- https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE94464

Scripts: All R scripts used for the analysis are included in this folder. These scripts are well-documented, with comments explaining each step of the process. Users can run these scripts to reproduce the analysis or modify them to suit different datasets or research questions.

Results: The results folder includes visualizations, figures, and tables that summarize the key findings of the analysis. This may include heat maps, volcano plots, and other graphical representations highlighting differentially expressed genes and their significance.

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This repository contains the differential expression analysis and normalized count matrices of the GEO dataset GSE94464. The analysis aims to visualize gene expression abundance and identify significant changes in gene expression across conditions.

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