Differential expression analysis of published microarrays datasets from the NCBI Gene Expression Omnibus (GEO)
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 |
- 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
Python modules
pip install openpyxl
pip install pillow
R packages
-
Bioconductor : GEOquery, limma
-
CRAN : corrgram, getopt, gplots
Differential expression analysis using the limma R/Bioconductor package. P-values are adjusted for multiple comparisons with the Benjamini & Hochberg method.
References
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W. Huber, V.J. Carey, R. Gentleman, ..., M. Morgan Orchestrating high-throughput genomic analysis with Bioconductor. Nature Methods, 2015:12, 115.
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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.
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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.
Python 3.8.2
R 3.6.3
David Laperriere dlaperriere@outlook.com