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Working with Bioconductor
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Bioconductor is an archive containing a wide range of packages for bioinformatics analysis.
Installation of Bioconductor packages is done using the BiocManager::install() function, rather than through the usual install.packages()
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Importing processed microarray data into R from GEO
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GEO data types have enough similarities to allow data access, but enough differences to require specific type-specific steps. and analysis.
The ExpressionSet class of object contains slots for different information associated with a microarray experiment.
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Importing raw (unprocessed) Affymetrix microarray data
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GEO data types have enough similarities to allow data access, but enough differences to require specific type-specific steps.
The ExpressionSet class of object contains slots for different information associated with a microarray experiment.
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Working with experimental metadata
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GEO metadata can be cast into R data objects for analysis. The details are up to the user.
Using proper phenoData to describe an experiment helps to ensure reproducibility and avoid reading in files out of order
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Microarray Data processing with RMA
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RMA, the most widely used processing algorithm for Affymetrix data, is implemented in R using the rma() function in the oligo or affy packages, depending on how the data was imported.
The steps of background correction, quantile normalisation, and summarisation are performed in order to obtain feature-level data
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Identifying differentially expressed genes using linear models (part 1)
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Identifying differentially expressed genes using linear models (part 2, factorial designs)
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From features to annotated gene lists
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BioConductor has a rich annotation infrastructure, with different data type being stored in different annotation packages.
The select() function allows us to efficiently query annotation databases.
Using topTable() in conjunction with rownames() allows us to retrieve all the probes which are differentially expressed between our experimental conditions.
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Basic downstream analysis of microarray data
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Volcano plots can be used to infer relationships between fold changes and statstical confidence.
Heatmaps can be used to visualize distinct trends in gene expression patterns between different experimental conditions by clustering.
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