getGEO {GEOquery} | R Documentation

## Get a GEO object from NCBI or file

### Description

This function is the main user-level function in the GEOquery package. It directs the download (if no filename is specified) and parsing of a GEO SOFT format file into an R data structure specifically designed to make access to each of the important parts of the GEO SOFT format easily accessible.

### Usage

    getGEO(
      GEO = NULL,
      filename = NULL,
      destdir = tempdir(),
      GSElimits = NULL,
      GSEMatrix = TRUE,
      AnnotGPL = FALSE,
      getGPL = TRUE,
      parseCharacteristics = TRUE
    )

### Arguments

`GEO` | A character string representing a GEO object for download and parsing. (eg., 'GDS505','GSE2','GSM2','GPL96')
`filename` | The filename of a previously downloaded GEO SOFT format file or its gzipped representation (in which case the filename must end in .gz). Either one of GEO or filename may be specified, not both. GEO series matrix files are also handled. Note that since a single file is being parsed, the return value is not a list of esets, but a single eset when GSE matrix files are parsed.
`destdir` | The destination directory for any downloads. Defaults to the architecture-dependent tempdir. You may want to specify a different directory if you want to save the file for later use. Doing so is a good idea if you have a slow connection, as some of the GEO files are HUGE!
`GSElimits` | This argument can be used to load only a contiguous subset of the GSMs from a GSE. It should be specified as a vector of length 2 specifying the start and end (inclusive) GSMs to load. This could be useful for splitting up large GSEs into more manageable parts, for example.
`GSEMatrix` | A boolean telling GEOquery whether or not to use GSE Series Matrix files from GEO. The parsing of these files can be many orders-of-magnitude faster than parsing the GSE SOFT format files. Defaults to TRUE, meaning that the SOFT format parsing will not occur; set to FALSE if you for some reason need other columns from the GSE records.
`AnnotGPL` | A boolean defaulting to FALSE as to whether or not to use the Annotation GPL information. These files are nice to use because they contain up-to-date information remapped from Entrez Gene on a regular basis. However, they do not exist for all GPLs; in general, they are only available for GPLs referenced by a GDS
`getGPL` | A boolean defaulting to TRUE as to whether or not to download and include GPL information when getting a GSEMatrix file. You may want to set this to FALSE if you know that you are going to annotate your featureData using Bioconductor tools rather than relying on information provided through NCBI GEO. Download times can also be greatly reduced by specifying FALSE.
`parseCharacteristics` | A boolean defaulting to TRUE as to whether or not to parse the characteristics information (if available) for a GSE Matrix file. Set this to FALSE if you experience trouble while parsing the characteristics.

### Details

getGEO functions to download and parse information available from NCBI GEO (http://www.ncbi.nlm.nih.gov/geo). Here are some details about what is avaible from GEO. All entity types are handled by getGEO and essentially any information in the GEO SOFT format is reflected in the resulting data structure.

From the GEO website:

The Gene Expression Omnibus (GEO) from NCBI serves as a public repository for a wide range of high-throughput experimental data. These data include single and dual channel microarray-based experiments measuring mRNA, genomic DNA, and protein abundance, as well as non-array techniques such as serial analysis of gene expression (SAGE), and mass spectrometry proteomic data. At the most basic level of organization of GEO, there are three entity types that may be supplied by users: Platforms, Samples, and Series. Additionally, there is a curated entity called a GEO dataset.

A Platform record describes the list of elements on the array (e.g., cDNAs, oligonucleotide probesets, ORFs, antibodies) or the list of elements that may be detected and quantified in that experiment (e.g., SAGE tags, peptides). Each Platform record is assigned a unique and stable GEO accession number (GPLxxx). A Platform may reference many Samples that have been submitted by multiple submitters.

A Sample record describes the conditions under which an individual Sample was handled, the manipulations it underwent, and the abundance measurement of each element derived from it. Each Sample record is assigned a unique and stable GEO accession number (GSMxxx). A Sample entity must reference only one Platform and may be included in multiple Series.

A Series record defines a set of related Samples considered to be part of a group, how the Samples are related, and if and how they are ordered. A Series provides a focal point and description of the experiment as a whole. Series records may also contain tables describing extracted data, summary conclusions, or analyses. Each Series record is assigned a unique and stable GEO accession number (GSExxx).

GEO DataSets (GDSxxx) are curated sets of GEO Sample data. A GDS record represents a collection of biologically and statistically comparable GEO Samples and forms the basis of GEO's suite of data display and analysis tools. Samples within a GDS refer to the same Platform, that is, they share a common set of probe elements. 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.

### Value

An object of the appropriate class (GDS, GPL, GSM, or GSE) is returned. If the GSEMatrix option is used, then a list of ExpressionSet objects is returned, one for each SeriesMatrix file associated with the GSE accesion. If the filename argument is used in combination with a GSEMatrix file, then the return value is a single ExpressionSet.

### Warning

Some of the files that are downloaded, particularly those associated with GSE entries from GEO are absolutely ENORMOUS and parsing them can take quite some time and memory. So, particularly when working with large GSE entries, expect that you may need a good chunk of memory and that coffee may be involved when parsing....

### Author(s)

Sean Davis

### See Also

`getGEOfile`

### Examples

    gds <- getGEO("GDS10")
    gds

    gse <- getGEO('GSE10')
    # Returns a list, so look at first item

    gse[[1]]

[Package GEOquery version 2.62.2 Index]