Dataset Information

GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network.

ABSTRACT:

Background

Gene Set Analysis (GSA) has proven to be a useful approach to microarray analysis. However, most of the method development for GSA has focused on the statistical tests to be used rather than on the generation of sets that will be tested. Existing methods of set generation are often overly simplistic. The creation of sets from individual pathways (in isolation) is a poor reflection of the complexity of the underlying metabolic network. We have developed a novel approach to set generation via the use of Principal Component Analysis of the Laplacian matrix of a metabolic network. We have analysed a relatively simple data set to show the difference in results between our method and the current state-of-the-art pathway-based sets.

Results

The sets generated with this method are semi-exhaustive and capture much of the topological complexity of the metabolic network. The semi-exhaustive nature of this method has also allowed us to design a hypergeometric enrichment test to determine which genes are likely responsible for set significance. We show that our method finds significant aspects of biology that would be missed (i.e. false negatives) and addresses the false positive rates found with the use of simple pathway-based sets.

Conclusions

The set generation step for GSA is often neglected but is a crucial part of the analysis as it defines the full context for the analysis. As such, set generation methods should be robust and yield as complete a representation of the extant biological knowledge as possible. The method reported here achieves this goal and is demonstrably superior to previous set analysis methods.

SUBMITTER: Jacobson D

PROVIDER: S-EPMC3626710 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Publications

GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network.

Jacobson Dan D Emerton Guy G

BMC bioinformatics 20120809

<h4>Background</h4>Gene Set Analysis (GSA) has proven to be a useful approach to microarray analysis. However, most of the method development for GSA has focused on the statistical tests to be used rather than on the generation of sets that will be tested. Existing methods of set generation are often overly simplistic. The creation of sets from individual pathways (in isolation) is a poor reflection of the complexity of the underlying metabolic network. We have developed a novel approach to set ...[more]

PMID: 22876834

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Project description:Here, we report data of the principal component analysis (PCA) assessment and clustering analysis related to low-temperature thermal hydrolysis process (THP) for enhancing the anaerobic digestion (AD) of sludge in wastewater treatment plants (WWTPs) with primary sludge fermentation (Azizi et al., 2021). The PCA was examined to pinpoint the influence of different THP schemes on the variations of macromolecular compounds solubilization after low-temperature THP and the relative performances in enhancing methane potential in AD. We established 2 experimental setups with a total of 18 treatment conditions (3 exposure times, 30, 60, and 90 min at three temperature levels 50, 70 and 90 °C) in comparison to the untreated control samples. Scheme-1 comprises the THP of a mixture of (1:1 vol ratio) fermented primary sludge (FPS) and thickened waste activated sludge (TWAS); while scheme-2 comprised the THP of TWAS only. The factors employed in the assessment of the PCA encompassed the variations in the macromolecular compounds and other solubilization metrics. This included the variations in the levels of carbohydrates, lipids, proteins, and solubilization of chemical oxygen demand (COD) and volatile suspended solids (VSS). Furthermore, the evaluation considered the changes of volatile fatty acids (VFAs) and total ammonia nitrogen (TAN) with respect to time and temperature. The assessment of PCA classified the THP based on their differences and alterations that occurred after the treatment. The indices of the PCA assessments differed based on the factors of concern and the focus of each individual PCA assessment. In every individual PCA assessment, the respective contribution to the total variance in PCA analysis was calculated and manifested by the highest distribution of the principal components (PCs) axis PC1 and PC2. The differences in distributions of PCs after various PCA examinations can describe the relative influence of THP schemes and the most significant variables that can trigger major differences among THP conditions. The comparative differences demonstrated by PCA support the potential investigations of the efficiency of THPs conditions and their performance categories.

Project description:We have previously developed a retrospective 4D-MRI technique using body area as the respiratory surrogate, but generally, the reconstructed 4D MR images suffer from severe or mild artifacts mainly caused by irregular motion during image acquisition. Those image artifacts may potentially affect the accuracy of tumor target delineation or the shape representation of surrounding nontarget tissues and organs. So the purpose of this study is to propose an approach employing principal component analysis (PCA), combined with a linear polynomial fitting model, to remodel the displacement vector fields (DVFs) obtained from deformable image registration (DIR), with the main goal of reducing the motion artifacts in 4D MR images. Seven patients with hepatocellular carcinoma (2/7) or liver metastases (5/7) in the liver, as well as a patient with non-small cell lung cancer (NSCLC), were enrolled in an IRB-approved prospective study. Both CT and MR simulations were performed for each patient for treatment planning. Multiple-slice, multiple-phase, cine-MRI images were acquired in the axial plane for 4D-MRI reconstruction. Single-slice 2D cine-MR images were acquired across the center of the tumor in axial, coronal, and sagittal planes. For a 4D MR image dataset, the DVFs in three orthogonal direction (inferior–superior (SI), anterior–posterior (AP), and medial–lateral (ML)) relative to a specific reference phase were calculated using an in-house DIR algorithm. The DVFs were preprocessed in three temporal and spatial dimensions using a polynomial fitting model, with the goal of correcting the potential registration errors introduced by three-dimensional DIR. Then PCA was used to decompose each fitted DVF into a linear combination of three principal motion bases whose spanned subspaces combined with their projections had been validated to be sufficient to represent the regular respiratory motion. By wrapping the reference MR image using the remodeled DVFs, 'synthetic' MR images with reduced motion artifacts were generated at selected phase. Tumor motion trajectories derived from cine-MRI, 4D CT, original 4D MRI, and 'synthetic' 4D MRI were analyzed in the SI, AP, and ML directions, respectively. Their correlation coefficient (CC) and difference (D) in motion amplitude were calculated for comparison. Of all the patients, the means and standard deviations (SDs) of CC comparing 'synthetic' 4D MRI and cine-MRI were 0.98 ± 0.01, 0.98 ± 0.01, and 0.99 ± 0.01 in SI, AP, and ML directions, respectively. The mean ± SD Ds were 0.59 ± 0.09 mm, 0.29± 0.10 mm, and 0.15 ± 0.05 mm in SI, AP and ML directions, respectively. The means and SDs of CC comparing 'synthetic' 4D MRI and 4D CT were 0.96 ± 0.01, 0.95± 0.01, and 0.95 ± 0.01 in SI, AP, and ML directions, respectively. The mean ± SD Ds were 0.76 ± 0.20 mm, 0.33 ± 0.14 mm, and 0.19± 0.07 mm in SI, AP, and ML directions, respectively. The means and SDs of CC comparing 'synthetic' 4D MRI and original 4D MRI were 0.98 ± 0.01, 0.98± 0.01, and 0.97± 0.01 in SI, AP, and ML directions, respectively. The mean ± SD Ds were 0.58 ± 0.10 mm, 0.30 ± 0.09mm, and 0.17 ± 0.04 mm in SI, AP, and ML directions, respectively. In this study we have proposed an approach employing PCA combined with a linear polynomial fitting model to capture the regular respiratory motion from a 4D MR image dataset. And its potential usefulness in reducing motion artifacts and improving image quality has been demonstrated by the preliminary results in oncological patients.

Dataset Information

GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network.

Background

Results

Conclusions

Publications

GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network.

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