cluster3 is a multipurpose open-source library of C routines, callable from other C and C++programs. It implements k-means clustering, hierarchical clustering and self-organizing maps and provides several unique analytical approaches.

• cluster3 Home page
• cluster3_Manual

• Module Name: cluster3 (see the modules page for more information)
• Unusual environment variables set
  • CLUSTER3_HOME  installation directory
  • CLUSTER3_BIN       executable directory
  • CLUSTER3_SRC       source code directory
  • CLUSTER3_DATA  sample data directory

Allocate an interactive session and run the program. Sample session:

[user@biowulf]$ sinteractive --cpus-per-task=16 --mem=32g --gres=lscratch:10
[user@cn3200 ~]$module load cluster3/1.59 
+] Loading cluster3  1.59   
[user@cn3200 ~]$ cluster3 -h
Cluster 3.0, command-line version.
USAGE: cluster [options]
options:
  -v, --version Version information
  -f filename   File loading
  -l            Specifies to log-transform the data before clustering
                (default is no log-transform)
  -cg a|m       Specifies whether to center each row (gene)
                in the data
                a: Subtract the mean of each row
                m: Subtract the median of each row
                (default is no centering)
  -ng           Specifies to normalize each row (gene) in the data
                (default is no normalization)
  -ca a|m       Specifies whether to center each column (microarray)
                in the data
                a: Subtract the mean of each column
                m: Subtract the median of each column
                (default is no centering)
  -na           Specifies to normalize each column (microarray) in the data
                (default is no normalization)
  -u jobname    Allows you to specify a different name for the output files
                (default is derived from the input file name)
  -g [0..8]     Specifies the distance measure for gene clustering
                0: No gene clustering
                1: Uncentered correlation
                2: Pearson correlation
                3: Uncentered correlation, absolute value
                4: Pearson correlation, absolute value
                5: Spearman's rank correlation
                6: Kendall's tau
                7: Euclidean distance
                8: City-block distance
                (default: 0)
  -e [0..8]     Specifies the distance measure for microarray clustering
                0: No clustering
                1: Uncentered correlation
                2: Pearson correlation
                3: Uncentered correlation, absolute value
                4: Pearson correlation, absolute value
                5: Spearman's rank correlation
                6: Kendall's tau
                7: Euclidean distance
                8: City-block distance
                (default: 0)
  -m [msca]     Specifies which hierarchical clustering method to use
                m: Pairwise complete-linkage
                s: Pairwise single-linkage
                c: Pairwise centroid-linkage
                a: Pairwise average-linkage
                (default: m)
  -k number     Specifies whether to run k-means clustering
                instead of hierarchical clustering, and the number
                of clusters k to use
  -r number     For k-means clustering, the number of times the
                k-means clustering algorithm is run
                (default: 1)
  -pg           Specifies to apply Principal Component Analysis to
                genes instead of clustering
  -pa           Specifies to apply Principal Component Analysis to
                arrays instead of clustering
  -s            Specifies to calculate an SOM instead of hierarchical
                clustering
  -x number     Specifies the horizontal dimension of the SOM grid
                (default: 2)
  -y number     Specifies the vertical dimension of the SOM grid
                (default: 1)
[user@cn3200 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$