Download and Installation

R and Java Runtime Environment

In order to run SEURAT the latest JRE (or JDK) and the latest R version need to be installed. In addition, to perform any clustering and seriation analysis, the R packages Rserve, amap , seriation and biclust need to be installed.

This can be done by typing the following into the R console:

  install.packages(c("Rserve","amap","seriation","biclust"))

Download SEURAT

By downloading any version of SEURAT, you accept the following license:

This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 3 of the License, or (at
your option) any later version.

This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program; if not, see http://www.gnu.org/licenses/.

Version 1.01 September 03, 2011

Seurat (Application for Mac)
Seurat (Windows batch and Jar file)
Seurat.jar (Jar file for Unix)

The source code is available from the Subversion-Repository

Installation and Run

To install just download the appropriate file, and:

Windows: extract the zip archive and double click the Seurat.bat or type Seurat.bat in the command line.
Linux: launch the Seurat.jar file via the terminal by:
```
java -jar Seurat.jar
```

MacOS X: extract the zip archive and double click the Seurat.app in the Seurat folder

If SEURAT returns an "calculation failed" error message after running any clustering or seriation algorithm, the software could not connect to Rserve. In this case you have to lauch Rserve manually by typing the following into the R console:

library(Rserve)
library(amap)
library(seriation)
library(biclust)
Rserve()

Sample Data

Here are some sample data sets, which are ready to load and test with SEURAT:

GeneExpression.txt    gene expression values of 7.705 genes of 50 samples

ClinicalData.txt     16 clinicial variables collected from the 50 samples

GeneAnnotations.txt     gene annotations of the 7.705 genes

aCGHData.txt     CGH data for 7.873 clones as measured by the 50 samples

The mapping of the 7.873 CGH clones to the 7.705 genes is time consuming, thus loading the array-CGH file will last some time (depending on your machine).

SampleDataI.zip

Most of the seriation algorithms (e.g. ARSA, BBURCG, BBWRCG, TSP, Chen) will not run with data sets of dimension 7000. To test the seriation algorithms, please try the short version of the data files:

GeneExpression.txt    gene expression values of 299 genes of 50 samples

ClinicalData.txt     16 clinicial variables collected from the 50 samples

GeneAnnotations.txt     gene annotations of the 299 genes

aCGHData.txt     CGH data for 1.144 clones (chromosome 1) as measured by the 50 samples

SampleDataShort.zip

The second example data set is based on Affymetrix exon (GeneChip Human Exon 1.0 ST Arrays) and SNP arrays (Genome-Wide Human SNP Arrays 6.0). Most of the preprocessing of the Affymetrix chips has been done using the R-package aroma.affymetrix. Help regarding this package and the implemented statistical methods can be found here. In addition we provide a R-script that describes the preprocessing of the raw data:

GeneExpression.txt    gene expression values of 17.502 genes of 8 samples

ClinicalData.txt     16 clinicial variables collected from the 8 samples

GeneAnnotations.txt     gene annotations of the 17.502 genes

SNPdata.txt     198.965 SNPs measured by the 8 samples

SampleDataAffy.zip

preprocessing.R