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

R 37.63% Python 55.55% Batchfile 3.48% Shell 3.34%
microarray limma bioinformatics ncbi-geo

geo_microarrays's Introduction

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

analyze_geo_microarrays.py

 python analyze_geo_microarrays.py  -g MCF7_E2_CHX.GSE8597.analysis.txt

geo dataset file format (-g filename, tsv)

parameter value
gse GSE# e.g. GSE8597
gpl GPL# e.g. GPL570
samples filename
contrast treatment-control e.g. E2-EtOH
contrast treatment2-control2
contrast ...

samples file format (tsv)

ID sample condition
GSM213318 MCF7_CHX_E2_24h_rep1 CHX_E2
GSM213322 MCF7_CHX_EtOH_24h_rep1 CHX_EtOH
GSM213326 MCF7_E2_24h_rep1 E2
GSM213330 MCF7_EtOH_24h_rep1 EtOH

Output

  • text version of the differential expression analysis results for each contrast in the DiffExpression folder
  • QC figures in the Figure folder
  • RData file with the ExpressionSet object
  • Excel file with all the differential expression analysis results

results excel

Installation

Python modules

  pip install openpyxl
  pip install pillow

R packages

  • Bioconductor : GEOquery, limma

  • CRAN : corrgram, getopt, gplots

    run R/install_packages.R

Methods

Differential expression analysis using the limma R/Bioconductor package. P-values are adjusted for multiple comparisons with the Benjamini & Hochberg method.

References

  1. W. Huber, V.J. Carey, R. Gentleman, ..., M. Morgan Orchestrating high-throughput genomic analysis with Bioconductor. Nature Methods, 2015:12, 115.

  2. Smyth, GK (2005). Limma: linear models for microarray data. In: 'Bioinformatics and Computational Biology Solutions using R and Bioconductor'. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds), Springer, New York, pages 397-420.

  3. Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57, 289-300.

Version

Python 3.8.2

R 3.6.3

Copyright

David Laperriere [email protected]

geo_microarrays's People

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