Introduction:

This tool performs a partial least squares discriminant analysis on the inputted peak table data.

Input files:

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

 

1.      Group design file in Tab-delimited text file with two column (samplename     groupname).

For example:

HU_011

M

HU 014

F

HU_015

M

HU_017

M

HU_018

M

HU_019

M

Output files:

1.      'PLSDA_Score.txt', Component (scores) matrix.

2.      'PLSDA_R2X_R2Y_Q2.txt', data frame with the model overview.

3.      'PLSDA _Score_2D_Label.pdf', PLSDA scatter plot using Component 1 and Component 2 score values with the sample name label.

4.      'PLSDA _Score_2D.pdf', PLSDA scatter plot using Component 1 and Component 2 score values without the sample name label.