If you intend to use results obtained with ArrayMining.net for a publication, please acknowledge/cite our publication:

E. Glaab, J. Garibaldi, N. Krasnogor
ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization
BMC Bioinformatics, Vol. 10, No. 1. (2009), 358.

BibTeX-item:

@article{glaab2009arraymining,
title={{ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization}},
author={Glaab, E. and Garibaldi, J.M. and Krasnogor, N.},
journal={BMC Bioinformatics},
volume={10},
number={1},
pages={358},
year={2009},
publisher={BioMed Central Ltd}
}

• This is ArrayMining version 1.0
• This service is free for academic and non-commercial use.
• If you intend to use results obtained with ArrayMining.net for a publication, please acknowledge/cite our paper:
  
  E. Glaab, J. Garibaldi, N. Krasnogor
  ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization
  BMC Bioinformatics, Vol. 10, No. 1. (2009), 358.
• We cannot give any guarantee that ArrayMining is free of errors or bugs, but we perform integretiy checks and provide validition measures for each analysis module.
• If you have any comments or experience problems accessing the server, please do not hesitate to contact us.

• We take reasonable measures to protect your data, which includes your dataset, tasks, results and all further information that you provide us, and we will not make them available to any third party.
• We collect some data for statistical purposes, which includes your IP address and the tasks performed. This data is never forwarded to third parties.
• All your data, apart from data that we keep for statistical purposes, will be deleted after the expiration time.

• Uploading your own data: In order to use ArrayMining.net with your own data there are two possibilities:
  
  Option 1: You can upload a tab- or space-delimited text-file containing pre-normalized Microarray data in the following simple matrix-format (see Fig. 1):
  
  You can download an example data file here (use right-click and "Save as"). The columns must correspond to the samples and the rows to the genes. The first column contains the gene identifiers (a unique label per gene) and the last row the class information for the samples (multiple samples can have the same class label). The rest of the matrix should contain normalized expression values obtained using any of the common Microarray normalization methods (e.g. VSN, RMA, GCRMA, MAS, dChip, etc.). The gene identifiers can be any one of the following: Affymetrix ID, ENTREZ ID, GENBANK ID. You can also use your own identifiers; however, in this case you won't obtain any links to functional annotation data bases. The class labels can be any alphanumeric strings or symbols (e.g. "tumour" and "healthy", or "1","2", "3", or "leukemia1", "leukemia2", "leukemia3", etc.). Samples belonging to the same class need to have exactly the same class label. The last row containing the class labels has to begin with a user-defined "sample type"-label, e.g. "phenotypes", "tumours" or just "labels". Optionally, unique IDs per sample can be specified in the first row (if this line is missing, the samples will be numbered consecutively).
  
  Option 2: You can upload a compressed ZIP-archive containing Affymetrix CEL-files and a txt-file containing tab-delimited numerical sample labels (specifying replicates by the same number, i.e. "1 1 1 2 2 2" for an experiment with 6 samples, two classes and three samples for both class 1 and class 2)
  Please contact us, should you experience any kind of problems when uploading or analyzing your data.
  

• 1) When I upload my own data, why do I get an error message saying the input format is wrong?
  If you haven't downloaded the example input file in the "Data Set" section, please try this first. A typical problem is that users forget to specify the class labels in the last row of the input; however, this is only required on the supervised analysis modules and not on the Class Discovery module. Moreover, please note that ArrayMining.net currently does not provide missing value imputation for your data (this is mainly because we don't know whether these values are missing completely at random in your data or depend on other variables). To specify the class labels either integers or strings can be used, only continuous values are not supported (not that on the Class Discovery module the last input row will be interpreted as an additional data row, if it contains continuous values, and no adjusted rand indices will be computed).
  
  
  
• 2) Why do I only obtain annotations for a subset of the genes on the Gene Selection Analysis module?
  For many probes on a microarray a corresponding gene is either not (yet) annotated in public data bases or does not exist (probes do not necessarily only represent genes). Moreover, different array platforms use different gene identifiers and ArrayMining.net does not yet support automatic gene name normalization. If you use standard identifiers (e.g. ENTREZ ID, GENBANK ACCESSION, etc.) most of the gene names should be recognized - otherwise, you can use an external public gene name conversion service on the web. We recommend the DAVID service (http://david.abcc.ncifcrf.gov ), the CNIO Clone/Gene ID converter (http://idconverter.bioin fo.cnio.es) and the MIPS CRONOS service (http://mips. gsf.de/genre/proj/cronos/batch.html).
  
  
  
• 3) Which platforms are supported by ArrayMining.net?
  Our system supports pre-normalized from any human microarray-platform - the only condition is that your data is submitted as matrix with columns corresponding to samples and rows corresponding to genes. However, if you want to use the functional annotation features, your data must contain one gene identifiers in one of the supported formats (see Instructions and question 2).
  
  
  
• 4) On the Gene Set Analysis module, why do I always obtain the message that none of the gene sets are enriched within my data or that the gene labels don't match?
  If you don't get any results on the Gene Set Analysis module this can have multiple reasons: Your gene labels might not be in one of the standard formats (ENTREZ ID, GENBANK ACCESSION, etc.), the genes might not be contained in the gene sets (this is unlikely in the case of the KEGG and GO gene sets, but very likely in the case of the cancer-related gene sets) or no gene set has passed the test for statistical significance of enrichment in your data (default: q-value < 0.05).
  In order to rule out that the wrong gene identifiers have been used, you can submit your gene names to an external public gene name conversion tool (see question 2 for examples).
  
  
  
• 5) Why can I use the GEO data sets only on the Class Discovery module?
  Supervised analysis is currently not supported for the GEO data base, although some GEO entries contain labelled data. The reason for this is simply that the label information is not standardized and can not be extracted automatically. Thus, for a supervised analysis of GEO data sets, the users needs to download the data first on his own computer and specify the class information manually before uploading it on ArrayMining.net.
  
  
  
• 6) On the Class Assignment Analysis module, why are the standard deviations so high?
  This is a usual and hard to solve problem for small-sample microarray data, especially if leave-one- out or 10-fold cross-validation is used for validation. If you don't have access to data sets with larger number of samples, you might want to try out the ensemble feature selection and prediction methods to increase robustness. Advanced users might consider to combine similar data sets together based on cross-study normalizaton techniques to increase the number of samples. The simplest approach is to try out different feature selection and prediction methods and see, whether a certain combination provides consistently lower standard deviations (but be aware of the multiple testing problem!). However, depending on the size and quality of your data, even very sophisticated algorithms might fail to overcome or to sufficiently alleviate this problem.