AMIC@ - All MIcroarray Cluterings @ once

AMIC@ - All Microarray Clusterings @ once, is a web application aiming to provide users with a common user interface to a wide range of microarray gene expression data clustering algorithms.
AMIC@ allows you to:
  • run several algorithms (and different configurations of them) on the same data set
  • view all the resulting clusterings on-line by means of heatmaps
  • view the clustering heatmap, its homogeneity and the column dendogram (Note: the columns can be reorganized with respect to the dendogram, on user request)
  • visualize de expression level of each heatmap cell
  • download the outcome of the clustering activities as a standard clustered data files (CDT), or the hierarchycal dendogram in case of HAC (when executed without k, for obtaining the whole hierarchy)
  • receive via email the clustering results by means of a wizard clustering, that allows you to execute simultaneously all the algorithms on a given data set
How to pronounce AMIC@: [a:meeka:]
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Enter AMIC@

How to use AMIC@
You can upload an ASCII file containing microarray gene expression data (space or tab delimited) or use one of the data files provided as default:
  1. Cho et al. data set, with 6601 genes under 17 conditions
  2. Eisen et set, with 2467 genes under 79 conditions
  3. Spellman et al. data set, with 6178 genes under 82 conditions
Note:  Due to server upload size limitation, for files larger than 10MB we recommend to upload a zipped file.
AMIC@ firstly try to interpret the uploaded ascii file in order to automatically distinguish the label columns from the numerical columns, asking your confirmation; you can also decide to filter out some experiments; furthermore, AMIC@ identifies and removes column comments (starting with #), and put 0s or the row average value (it depends on the user's choice), in place of missing values; after that, you enter AMIC@' main page, where you can configure and run all the clustering algorithms provided.
After successfully uploaded the data file, AMIC@ automatically starts with determining the number of clusters for the given data set (computed with the K-Boost algorthm*).
In the meanwhile you can:
  1. wait for the suggested number of clusters provided by AMIC@ or choose yours
  2. choose the clustering algorithm to apply to the data set, among:
  3. choose one of the provided metrics to apply during clustering, among the:
    • Euclidean Distance
    • Pearson Correlation Coefficient
    • City-Block Distance
    • Cosine Similarity
    • Spearman's Rank Correlation
    • Kendall's Similarity
  4. view the clustering results by means of heatmap, and visualize the expression level of each cell
  5. view the clustering homogeneity
  6. download the clustering results by saving them as CDT files, useful for further -off line- inspection
Instead of executing the clusterings on-line, you can decide to enter the Clustering Wizard, which allows you to set all the clustering algorithms at once, provide an email address, and receive the results by email. This feature is especially useful when input data is too big, or some of the clustering algorithms take too long to be executed (it could happen, for instance, with HAC, under certain parameter configurations).
* Note that the automatically determination of the number of clusters is the same in K-Boost and FPF-SB

A genome-wide transcriptional analysis of the mitotic cell cycle
Cho, R., Campbell, M., Winzeler, E., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T., Gabrielian, A., Landsman, D., Lockhart, D., and Davis, R.
Molecular Cell, 2, pp 65-73, 1998.
Cluster analysis and display of genome-wide expression patterns
Eisen, M., Spellman, P., Brown, P., and Botstein, D.
In Proc. of National Academy of Science of the United States of America, pp 14863-14868, 1998.
K-Boost: a Scalable Algorithm for High-Quality Clustering of Microarray Gene Expression Data
Filippo Geraci, Mauro Leoncini, Manuela Montangero, Marco Pellegrini, M. Elena Renda
Tech. Rep. n. 2007-TR-15, Istituto di Informatica e Telematica - CNR, Pisa, December 2007.
FPF-SB: a Scalable Algorithm for Microarray Gene Expression Data Clustering
Filippo Geraci, Mauro Leoncini, Manuela Montangero, Marco Pellegrini, M. Elena Renda
in Proc. 12th International Conference on Human-Computer Interaction (HCI'07), Beijing, P.R. China, July 2007.
Tech. Rep. n. 2007-TR-1, Istituto di Informatica e Telematica - CNR, Pisa, February 2007.
Open Source Clustering Software
M.J.L. de Hoon, S. Imoto, J. Nolan, and S. Miyano
Bioinformatics, 20(9), pp 1453-1454, 2004.
Comprehensive identification of cell cycleregulated genes of the yeast saccharomyces cerevisiae by microarray hybridization
Spellman, P., Sherlock, G., Zhang, M., Iyer, V., Anders, K., Eisen, M., Brown, P., Botstein, D., and Futcher, B.
Mol Biol Cell, 9(12), pp 3273-3297, 1998.

Enter AMIC@

Styled and mantained by:
M. Elena Renda