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

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 dlaperriere@outlook.com