Geoquery downloaded file store

You may also want to mine publicly available data to build on an existing hypothesis or simply to find additional support for your favorite gene in a different animal model or experimental condition. In this post, we will go over how to use the GEOquery package to download a data matrix or eset object directly into R and append specific probe annotation information to this matrix for it to be exported as a csv file for easy manipulation in Excel or spreadsheet tools.

This is especially useful for sharing data with collaborators who are not familiar with R and would rather look up there favorite genes in a spreadsheet format.

This is now a data. A single TAR archive was downloaded. You can then read the 6 CEL files into R using functions from affy or oligo. Dependencies This document has the following dependencies: library GEOquery Use the following commands to install these packages in R. R" biocLite c "GEOquery". Corrections Improvements and corrections to this document can be submitted on its GitHub in its repository.

Note that in the following, I use a file packaged with the GEOquery package. In general, you will use only the GEO accession, as noted in the code comments. The GEOquery data structures really come in two forms.

I will explain the first three together first. There is also a show method for each class. For example, using the gsm from above:. However, the GDS class has a bit more information associated with the Columns method:. A GSE entry can represent an arbitrary number of samples run on an arbitrary number of platforms. The GSE class has a metadata section, just like the other classes.

To show an example:. GEO Series are collections of related experiments. The data structure returned from this parsing is a list of ExpressionSets. As an example, we download and parse GSE Now, eset is an ExpressionSet that contains the same information as in the GEO dataset, including the sample information, which we can see here:.

No annotation information called platform information by GEO was retrieved from because ExpressionSet does not contain slots for gene information, typically. However, it is easy to obtain this information. First, we need to know what platform this GDS used. Then, another call to getGEO will get us what we need. Value measurements for each Sample within a GDS are assumed to be calculated in an equivalent manner, that is, considerations such as background processing and normalization are consistent across the dataset.

Information reflecting experimental design is provided through GDS subsets. Getting data from GEO is really quite easy. There is only one command that is needed, getGEO. This one function interprets its input to determine how to get the data from GEO and then parse the data into useful R data structures. Usage is quite simple. This loads the GEOquery library. Now, we are free to access any GEO accession. Note that in the following, I use a file packaged with the GEOquery package.

In general, you will use only the GEO accession, as noted in the code comments. The GEOquery data structures really come in two forms. I will explain the first three together first. There is also a show method for each class. For example, using the gsm from above:.

However, the GDS class has a bit more information associated with the Columns method:. A GSE entry can represent an arbitrary number of samples run on an arbitrary number of platforms. The GSE class has a metadata section, just like the other classes. To show an example:. GEO Series are collections of related experiments.

The data structure returned from this parsing is a list of ExpressionSets.