ncbi-geo

The bridge between the NCBI Gene Expression Omnibus and Bioconductor

Microarray Analysis Pipeline in Python

Subclassification of disease states based on the intersection of literature and expression

analyze_geo_microarrays.py : Differential expression analysis of published microarrays datasets from the NCBI Gene Expression Omnibus (GEO)

A small utility to quickly tag studies and samples from the NCBI Gene Expression Omnibus (GEO) with a curses interface.

Bioinformatic analysis of gene expression microarray profiles and DGEs on R and R studio

useful little tools & code snippets without a home; mostly genomics-oriented and one-liners

Scripts using GEOquery package to fetch expression matrices and phenotypic data associated with GSE datasets

Windows desktop app for downloading ENA FASTQ and GEO supplementary files.

Automated Bioconductor pipeline for programmatic retrieval, differential gene expression analysis, and GSEA pathway enrichment of public NCBI GEO transcriptomic datasets.

Volcano Plot for Gene Expression Analysis

A list of publicly available Microarray Gene Expression datasets with proper attribution and associated toolkits.

Automatic NGS data analysis from SRA download to the quantification of RNAs.

This repository contains a Jupyter Notebook for the analysis of Non-Small Cell Lung Cancer (NSCLC) data using various machine learning techniques.

This repository includes scripts, data, and documentation for analyzing Bulk RNA-seq data from NCBI GEO, supporting a class project in alignment with course requirements.

This repository contains scripts and detailed information for microarray-based transcriptomic analysis using NCBI GEO data for the course project.

Extract and harmonize RNA-seq metadata from NCBI GEO

Differential gene expression analysis of vehicle and sunitinib-treated renal cancer xenograft samples using GEO2R.

This repository contains scripts for single-cell transcriptomic analysis using NCBI GEO data for the purpose of completing Masters in Biomedical Informatics Practicum project at MSBMI, UTHealth, Houston.

Label-driven comparison of gene expression regions across measurement technologies. Microarray, bulk RNA-seq and single-cell in one question, local first, no API keys.