Prediction of Experimental Metadata from Gene Expression

Pipeline

• Data collection from [GEO] library
• Annotations of Platforms

GEOparse

Installation:

pip install GEOparse

[Usage:]

gse = GEOparse.get_GEO(geo="GSE1563", destdir="./")

gse.gsms.items()  # pairs of `gsm_name` and `gsm`
gse.gpls.items()  # pairs of `gpl_name` and `gpl`

GEO Terms

• GSE (Series) is a record that summarizes whole study including samples and platforms
  • GPL (Platform) contains mappings from probe IDs to RefSeq IDs
  • GSM (Sample) contains information about the conditions and preparation of a Sample

[source of image: http://slideplayer.com/slide/4814436/]

Methods

• Quantile Normalization [wiki] [machinelearning.ru]

References

• Improving the value of public RNA-seq expression data by phenotype prediction [bioRxiv]
• MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets [BioMed Central]
• A Python module to normalize microarray data by the quantile adjustment method [PubMed Central]

License

Copyright © 2017, BioinfoGroup. Released under the GNU AGPLv3.