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cluster3 - Online in the Cloud

Run cluster3 in OnWorks free hosting provider over Ubuntu Online, Fedora Online, Windows online emulator or MAC OS online emulator

This is the command cluster3 that can be run in the OnWorks free hosting provider using one of our multiple free online workstations such as Ubuntu Online, Fedora Online, Windows online emulator or MAC OS online emulator

PROGRAM:

NAME


cluster3 — Eisen-clustering of microarray data

SYNOPSIS


cluster3 [-f filename] [-l 0|1] [-u jobname] [-g [0..9]] [-e [0..9]] [-m [msca]] [-k
number] [-s 0|1] [-x number] [-y number]

OPTIONS


-h--help Show summary of options.

-f filename
file loding

-u jobname
Allows you to specify a different name for the output files (default is derived
from the input file name)

-l 0|1 Specifies whether to log-transform the data (default is 0, no log-transform)

-k number Specifies whether to run k-means clustering instead of hierarchical clustering,
and the number of clusters k to use (default: 0)

-x number Specifies the horizontal dimension of the SOM grid (default: 2)

-y number Specifies the vertical dimension of the SOM grid (default: 1)

-s 0|1 Specifies whether to calculate an SOM instead of hierarchical clustering
(default: 0)

-v --version
Show version of program.

-g [0..9] 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: Harmonically summed Euclidean distance 9: City-block distance
(default: 1)

-e [0..9] 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: Harmonically summed Euclidean distance 9: 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)

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