Covariance-Guided Mixture Probabilistic Principal Component Analysis (C-MPPCA)

<div><p>To extract information from high-dimensional data efficiently, visualization tools based on data projection methods have been developed and shown useful. However, a single two-dimensional visualization is often insufficient for capturing all or most interesting structures in complex high-dimensional datasets. For this reason, Tipping and Bishop developed mixture probabilistic principal component analysis (MPPCA) that separates data into multiple groups and enables a unique projection per group; that is, one probabilistic principal component analysis (PPCA) data visualization per group. Because the group labels are assigned to observations based on their high-dimensional coordinates, MPPCA works well to reveal homoscedastic structures in data that differ spatially. In the presence of heteroscedasticity, however, MPPCA may still mask noteworthy data structures. We propose a new method called covariance-guided MPPCA (C-MPPCA) that groups subsets of observations based on covariance, not locality, and, similar to MPPCA, displays them using PPCA. PPCA projects data in the dimensions with the highest variances, thus grouping by covariance makes sense and enables some data structures to be visible that were masked originally by MPPCA. We demonstrate the performance of C-MPPCA in an extensive simulation study. We also apply C-MPPCA to a real world dataset. Supplementary materials for this article are available online.</p></div

The progression of V2PI-GTM.

<p>Fig a is a GTM display (in latent dimensions <i>q</i>₁, <i>q</i>₂) of the simulated dataset when <i>K</i> = 16, <i>J</i> = 400. The data points are labeled according to their cluster numbers. The arrows show how a user may interact. A user may move one point from location A to location B and another point from location C to location D. Figs b, c, and d show respectively how the observations respond (or do not respond) to the move when stages 1, 2, and 3 of V2PI-GTM are in place.</p

A visual description of GTM.

<p>This exemplifies how the latent space constructed by <b><i>r</i></b> (denoted by ⋆ on the left) and the manifold constructed by <b><i>y</i></b> (denoted by ⋆ on the right) in a three-dimensional data space relate. Raw data points <b><i>x</i></b> are denoted by •.</p

A simulated three-dimensional dataset from five Multivariate Normal distributions and its GTM visualization.

<p>Fig a) shows that there are two groups of clusters in three dimensions. The first group includes clusters 1, 2, and 3. The second group includes clusters 4 and 5. Fig b) provides a two-dimensional visualization of the data using GTM.</p

V2PI-GTM with NIH data.

<p>We provide a GTM display of the NIH abstracts (labeled by their identification numbers) before and after user interaction in Figs a and b, respectively. The interaction is portrayed by the pink arrow in Fig a; Abstract 7 was moved to a location near cluster D. In addition, to labeling and learning about four clusters in the data (marked by A, B, C, and D), we also tagged the latent GTM space. After the interaction, we see that the clusters grouped differently and the meaning of the latent space changed. Also, the manifold changed dramatically.</p

Analysis of Proteome Profile in Germinating Soybean Seed, and Its Comparison with Rice Showing the Styles of Reserves Mobilization in Different Crops

<div><p>Background</p><p>Seed germination is a complex physiological process during which mobilization of nutrient reserves happens. In different crops, this event might be mediated by different regulatory and metabolic pathways. Proteome profiling has been proved to be an efficient way that can help us to construct these pathways. However, no such studies have been performed in soybean germinating seeds up to date.</p> <p>Results</p><p>Proteome profiling was conducted through one-dimensional gel electrophoresis followed by liquid chromatography and tandem mass spectrometry strategy in the germinating seeds of soybean (<i>glycine max</i>). Comprehensive comparisons were also carried out between rice and soybean germinating seeds. 764 proteins belonging to 14 functional groups were identified and metabolism related proteins were the largest group. Deep analyses of the proteins and pathways showed that lipids were degraded through lipoxygenase dependent pathway and proteins were degraded through both protease and 26S proteosome system, and the lipoxygenase could also help to remove the reactive oxygen species during the rapid mobilization of reserves of soybean germinating seeds. The differences between rice and soybean germinating seeds proteome profiles indicate that each crop species has distinct mechanism for reserves mobilization during germination. Different reserves could be converted into starches before they are totally utilized during the germination in different crops seeds.</p> <p>Conclusions</p><p>This study is the first comprehensive analysis of proteome profile in germinating soybean seeds to date. The data presented in this paper will improve our understanding of the physiological and biochemical status in the imbibed soybean seeds just prior to germination. Comparison of the protein profile with that of germinating rice seeds gives us new insights on mobilization of nutrient reserves during the germination of crops seeds.</p> </div

Descriptions of Abstracts 20, 22, 32 and 39 in Fig 4.

<p>Descriptions of Abstracts 20, 22, 32 and 39 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129122#pone.0129122.g004" target="_blank">Fig 4</a>.</p

This table lists the Top 10 keywords that either differentiate clusters A, B, C, and D or are shared among all of the clusters in Fig 4.

<p>This table lists the Top 10 keywords that either differentiate clusters A, B, C, and D or are shared among all of the clusters in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129122#pone.0129122.g004" target="_blank">Fig 4</a>.</p

Accumulation of ROS in germinating crop seeds.

<p>(A) soybean; (B) rice. The upper panel shows the accumulation of H₂O₂ with TMB staining, the bottom panel shows the accumulation of superoxide anions.</p

Quantitative Proteomics Reveals the Role of Protein Phosphorylation in Rice Embryos during Early Stages of Germination

Seed germination begins with water uptake and ends with radicle emergence. A gel-free phosphoproteomic technique was used to investigate the role of protein phosphorylation events in the early stages of rice seed germination. Both seed weight and ATP content increased gradually during the first 24 h following imbibition. Proteomic analysis indicated that carbohydrate metabolism- and protein synthesis/degradation-related proteins were predominantly increased and displayed temporal patterns of expression. Analyses of cluster and protein–protein interactions indicated that the regulation of sucrose synthases and alpha-amylases was the central event controlling germination. Phosphoproteomic analysis identified several proteins involved in protein modification and transcriptional regulation that exhibited significantly temporal changes in phosphorylation levels during germination. Cluster analysis indicated that 12 protein modification-related proteins had a peak abundance of phosphoproteins at 12 h after imbibition. These results suggest that the first 12 h following imbibition is a potentially important signal transduction phase for the initiation of rice seed germination. Three core components involved in brassinosteroid signal transduction displayed significant increases in phosphoprotein abundance during the early stages of germination. Brassinolide treatment increased the rice seed germination rate but not the rate of embryonic axis elongation. These findings suggest that brassinosteroid signal transduction likely triggers seed germination