Thank You Kevin, Actually i want to create the app for non- bioinformatician ppl, so that they can just upload their annotation file and expression data and get the result without performing anything else. iam stuck at this point that how probeIDs from any file will map to GeneSymbols irrespective of their platform. can you also please suggest whatcan be the main (must required) packages or libraries that can facilitate these conversions and work on differnt microarray sample data.
Hii, I am working on a automated app for microarray data analysis. In app we can provide input as annotation file and expression matrix file but iam not able to manage the automation of probeIDs to gene symbols mapping using R programming. I wanted to know if there can be a possible solution or code available for this where we provide expression data and it converts it into gene symbol from any required package or library in R and give an output file. Looking forward to your responses. Thank you
1 answer
Hi,
I would create a R Shiny app that can do this. It would be quite simple, with:
- input text box where the user would paste the IDs to convert and/or import button to upload a file
- drop-down box where the user selects the array manufacturer
- drop-down box where the user selects the array version
- output label box where the user can copy the converted IDs and/or export button to export to file
I would do this for Affymetrix, Agilent, and Illumina arrays. Bioconductor has many packages that facilitate these conversions, for example, Affymetrix arrays: https://www.bioconductor.org/packages/release/BiocViews.html#___AffymetrixChip
For example code, I show how one uses these packages, here:
- Translating gene names to entrez id's
- How to map Affymetrix IDs to Gene Symbols using hgu133a.db R package?
Kevin
Hi, you should still have an option to select the array manufacturer and array version, for those users who may have this information. For those who don't, you would have to implement a check to test which is the most likely array manufacturer and version by doing one mapping after the next and gauging the percent overlap of matching.
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