The friendly statistics toolbox for microarray analysis (FSPMA) is an R-library that is controlled by a definition file. FSPMA is available under the GPL 2 license. It is free software that comes with NO WARRANTY.
FSPMA, (Sykacek et al. 2005), is an R-library that can be used to analyse microarray data. FSPMA's concept is to base microarray analysis on a definition file that describes the experiment and which analysis steps should be done. The definition file allows analysis without adapting or writing R-scripts. In addition it serves as documantation of the analysis run. FSPMA can be used with data from different platforms (single and two colour arrays) with optional preprocessing steps done before the data gets loaded into FSPMA. The main restriction of FSPMA is that the experiment must be a balanced reference design. Analysis includes handling of bad quality flagged samples, conventional normalization and normalization with spike RNA, calculation of ANOVA tables and variance components and finally gene ranking based on within ANOVA contrasts and by using per gene ANOVA models. FSPMA is wrapped around YASMA, (Wernisch et al. 2003), which it extends by some preprocessing and normalization options and by more general contrasts that allow e.g. analysis of longitudinal studies. To find out more about FSPMA's functionality, it is recommended to inspect FSPMA's tutorial (Sykacek & Furlong 2005) which is part of FSPMA's documentation files.
For all other operating systems, one has to download the source distribution fspmax.zip. After unziping the file, running the command "fspmaxinstall.sh" will install FSPMA and a modified version of the YASMA library, (Wernisch et al. 2003). Note that the source distribution of the library has been tested with Linux (and Windows) only.
To test the installation, one should use the examples provided in FSPMA's online help. See the package overview for details. There are five zip archives containing definition files and the corresponding data files. These examples are meant for evaluation purpouse and contain a small number of genes of a larger study done by R. Furlong of the Dept. of Pathology, University of Cambridge. The run time of each example is thus rather small. Downloading and extracting fspma-tutorial.zip from the package overview page in FSPMA's online help, one can obtain the "fspma.Rnw" Sweave file (see the R help on how to use Sweave) which together with the experiments will generate the LaTex sources of the FSPMA tutorial (Sykacek & Furlong 2005). This step will run all code fragments in the tutorial and requires that all experimental data and the Sweave file to reside unziped in the same directory. Individual experiments can be run by downloading and extracting the relevant archive into a local directory. Analysis is started by invoking "fspma.wrapper" on the R command line using the name of the definition file as parameter, exactly as is shown in the tutorial. Refer to (Sykacek & Furlong 2005) for further details on the output of such analysis runs and how to produce different visualisations of the data and the analysis results.
We provide here an additional documented definition file for a public Affymetrix dataset. This file must be unzipped (e.g. gunzip) and stored in a directory of your choice. The microarray data that will be analysed by this file have been published as (Small et al . 2005) and can be downloaded form the NCBI GEO Datasets server under reference GDS660. These data files must be stored in the same directory as the definition file. Subsequently one has to start R in that directory and type the following commands at the command line. Different to the examples provided with the library, this definition file provides an analysis of realistic size. In particular evaluating base level comparisons which are shortcuts for several pair wise comparisons and k nearest beighbour imputation can be computationally quite demanding. The definition file of this example is discussed in (Sykacek 2005) which is also part of FSPMA's online help.
| >>library(fspma) |
| >>ret <- fspma.wrapper('tstsgd_A.def') |
As soon as the script terminates, there will be several additional files in that directory. These files contain the normalized raw data and a corresponding effects description, a file with an ANOVA table and variance components (although the latter will not show up in this analysis, since there is only one random effect which is captured by the residual noise) and several files that contain the rank lists that correspond to different tests.
FSPMA comes with extensive documentation. There are two tutorial like technical reports, one provides an overview and the second a detailed discussion of definition files. In addition all user level functions of FSPMA are described in detail in the online help.
This work was done at the Department of Pathology and the Department of Genetics, University of Cambridge and funded by the BBSRC's Exploiting Genomics initiative under ref. 8/EGH16106, "Shared Genetic Pathways in Cell Number Control". FSPMA is joint work with Gos Micklem and Rob Furlong and relies heavily on Lorenz Wernisch's YASMA package.