References 1. Sirén K, Fischer U and Vestner J. "Automated supervised learning pipeline for non-targeted GC-MS data analysis “. Analytica Chimica Acta: X, Volume 1, March 2019.

2. S. Reichenbach, X. Tian, C. Cordero, Q. Tao. "Features for non-targeted cross-sample analysis with comprehensive two-dimensional chromatography". Journal of Chromatography A, 1226:140-148, 2012.

3. S. Reichenbach, X. Tian, Q. Tao, E. Ledford, Z. Wu, O. Fiehn. "Informatics for Cross-Sample Analysis with Comprehensive Two-Dimensional Gas Chromatography and High-Resolution Mass Spectrometry (GCxGC-

HRMS)". Talanta, 83(4):1279-1288, 2011.

4. Q. Tao, S. E. Reichenbach, C. Heble, and Z. Wu. "New Investigator Tools for Finding Unique and Common Components in Multiple Samples with Comprehensive Two-Dimensional Chromatography."

Chromatography Today, 11, 13-18, 2018.

5. Z. Wu, J. Coleman, Q. Tao. "Distinguishing Commercial Diesel Fuel Brands Using Comprehensive Two-Dimensional Gas Chromatography/Mass Spectrometry". The International Symposium on Capillary

Chromatography (ISCC), Riva del Garda, Italy, May 2018.

6. N. Hoffmann, M. Wilhelm, A. Doebbe, K. Niehaus, J. Stoye, "BiPACE 2D—graph-based multiple alignment for comprehensive 2D gas chromatography-mass spectrometry." Bioinformatics 30.7 (2014): 988-995.

Data processes and screenshots for this publication are from an alpha version of GC Image v2.9 GCxGC-HRMS (Visit www.gcimage.com for current v2.9 releases).

Introduction

Qingping Tao;1 Daniel Geschwender;1 Chase Heble;1 Stephen E. Reichenbach;1,2 1 GC Image, LLC, Lincoln NE; 2 University of Nebraska, Lincoln NE

Process individual chromatograms

Generate compound features for each chromatogram

Match compound features across chromatograms

Extract compound feature vectors

Analyze compound feature vectors for

sample profiling and discover potential

marker compounds

Compound Features

Investigator Workflow with Peak-Region Features

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Chromatograms Peak-Region Features PCA Result

Example 1: GC-MS

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The extracted segmentation of the composite chromatogram

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Left: PCA result of all features with F value > 5.

Right: A bubble plot shows all compound features with F values as bubble sizes and color assigned by

the class that has larger Fisher ratios against other classes of samples.

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Example 2: GCxGC-MS

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Visual confirmation of the split peaks. Black peaks are all labeled ‘Octadecatrienoic acid, 6,9,12-

(Z,Z,Z)-, n- (1TMS)’

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Determine reliable peaks by pair-wise peak

pattern matching

Create a composite chromatogram by

aligning chromatograms with reliable peaks

Extract peak-region features, one per peak

in the composite chromatogram

Align peak-region features to individual chromatograms with

reliable peaks

Align Peak-Region Feature with TICs

Compute Match Ranking for All Peaks Cross-Sample with:

• ID information, i.e. library search, characteristic ions, etc;

• Spectral match factors;

• Retention time differences.

Generate an Aligned Aggregated Peak Table

WT_T2

Our Alignment Corrected

MUT_T2 WT_T1 MUT_T1

Our Alignment Manual Reference Alignment

Peaks matched across all samples