Introduction:

This tool inputs a peak table file and performs correlation analysis on it.

Input files:

Peak table file in Tab-delimited txt format, first column is the compound identifier, the others are 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:

Correlation methods:

1. Kendall rank correlation:

The Kendall rank correlation coefficient, commonly referred to as Kendall's tau coefficient (after the Greek letter τ), is a statistic used to measure the ordinal association between two measured quantities. A tau test is a non-parametric hypothesis test for statistical dependence based on the tau coefficient.

2. Pearson correlation:

the Pearson correlation coefficient, also referred to as Pearson's r is a measure of the linear correlation between two variables X and Y. It has a value between +1 and −1, where 1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation.

3. Spearman correlation:

Spearman's rank correlation coefficient or Spearman's rho is a nonparametric measure of rank correlation. It assesses how well the relationship between two variables can be described using a monotonic function.

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 similar cluster.

7. median: Median distance method.

Output files:

1. ' sample_cor_plot.pdf', correlation heatmap plot result.

2. ' sample_cor.txt', correlation matrix result.