Introduction:

This tool takes a peak table file as input and performs cluster analysis on it. The metabolites are classified into several groups according to their distance or variation similarity.

Input files:

1. Peak table file in Tab-delimited txt format, with the first column as the compound identifier and others as samples.

For example:

	HU_011	HU_014	HU_015	HU_017	HU_018	HU_019
(2-methoxyethoxy)propanoic acid isomer	3.019766	3.814339	3.519691	2.562183	3.781922	4.161074
(gamma)Glu-Leu/Ile	3.888479	4.277149	4.195649	4.32376	4.629329	4.412266
1-Methyluric acid	3.869006	3.837704	4.102254	4.53852	4.178829	4.516805
1-Methylxanthine	3.717259	3.776851	4.291665	4.432216	4.11736	4.562052
1,3-Dimethyluric acid	3.535461	3.932581	3.955376	4.228491	4.005545	4.320582
1,7-Dimethyluric acid	3.325199	4.025125	3.972904	4.109927	4.024092	4.326856
2-acetamido-4-methylphenyl acetate	4.204754	5.181858	3.88568	4.237915	1.852994	4.080681
2-Aminoadipic acid	4.080204	4.359246	4.249111	4.231404	4.323679	4.244485

Parameter:

Distance calculate method:

1. Euclidean:

The Euclidean distance between points p and q is the length of the line segment connecting them.

2. Correlation distance:

Correlation coefficient:

3. Canberra distance：sum (|p_i - q_i| / |p_i + q_i|). Terms with zero numerator and denominator are omitted from the sum and treated as if the values were missing. This is intended for non-negative values (e.g., counts): take the absolute value of the denominator.

4. Binary distance: The vectors are regarded as binary bits, so non-zero elements are ‘on’ and zero elements are ‘off’. The distance is the proportion of bits in which the only one is on amongst those in which at least one is on.

5. Minkowski distance: The p norm, the pth root of the sum of the pth powers of the differences between the components.

6. Manhattan: Absolute distance between the two vectors.

where (p, q) are vectors.

7. maximum distance：Maximum distance between two components of x and y (supremum norm).

Cluster methods:

1. ward: Ward's minimum variance method aims at finding compact, spherical clusters.

2. complete: The complete linkage method finds similar clusters.

3. single: The single linkage method (which is closely related to the minimal spanning tree) adopts a ‘friends of friends’ clustering strategy.

The other methods can be regarded as aiming for clusters with characteristics somewhere between the single and complete link methods.

4. centroid: Method "centroid" is typically meant to be used with squared Euclidean distances.

5. average: The average distance method measures the average distance between each pair of observations

6. mcquitty: It finds the similar cluster.

7. median: Median distance method.

Output files:

1. 'subcluster_1.matrix.txt', subcluster data information.

2. 'subcluster_1.pdf', subcluster line chart.