Samples per series/dataset in the NCBI GEO database

January 7, 2010
By

(This article was first published on What You're Doing Is Rather Desperate » R, and kindly contributed to R-bloggers)

Andrew asks:

I want to get an NCBI GEO report showing the number of samples per series or data set. Short of downloading all of GEO, anyone know how to do this? Is there a table of just metadata hidden somewhere?

At work, we joke that GEO is the only database where data goes in, but it won’t come out. However, there is an alternative: the GEOmetadb package, available from Bioconductor.

The R code first, then some explanation:

# install GEOmetadb
source("http://bioconductor.org/biocLite.R")
biocLite("GEOmetadb")
library(GEOmetadb)

# connect to database
getSQLiteFile()
con <- dbConnect(SQLite(), "GEOmetadb.sqlite")

# count samples per GDS
gds.count <- dbGetQuery(con, "select gds,sample_count from gds")
gds.count[1:5,]
# first 5 results
     gds sample_count
1   GDS5            5
2   GDS6           29
3  GDS10           28
4  GDS12            8
5  GDS15            6
# count samples per GSE
gse <- dbGetQuery(con, "select series_id from gsm")
gse.count <- as.data.frame(table(gse$series_id))
gse.count[1:10,]
# first 10 results
                Var1 Freq
1               GSE1   38
2              GSE10    4
3             GSE100    4
4           GSE10000   29
5           GSE10001   12
6           GSE10002    8
7           GSE10003    4
8  GSE10004,GSE10114    3
9           GSE10005   48
10          GSE10006   75

We install GEOmetadb (lines 2-4), then download and unpack the SQLite database (line 7). This generates the file ~/GEOmetadb.sqlite, which is currently a little over 1 GB.

Next, we connect to the database via RSQLite (lines 7-8). The gds table contains GDS dataset accession and sample count, so extracting that information is very easy (line 11).

GSE series are a little different. The gsm table contains GSM sample accession and GSE series accession (in the series_id field). We can count up the samples per series using table(), on line 22. However, this generates some odd-looking results, such as:

          Var1          Freq
15    GSE10011,GSE10026 45
14652 GSE9973,GSE10026   9
14654 GSE9975,GSE10026  36
14656 GSE9977,GSE10026  24

Fear not. In this case, GSE10026 is a super-series comprised from the series GSE10011 (45 samples), GSE9973 (9 samples), GSE9975 (36 samples) and GSE9977 (24 samples), total = 114 samples.


Posted in bioinformatics, computing, R, statistics Tagged: database, geo, microarray, ncbi

To leave a comment for the author, please follow the link and comment on his blog: What You're Doing Is Rather Desperate » R.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Tags: , , , , , , ,

Comments are closed.