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Mfuzz method adopts a new clustering algorithm fuzzy c-means algorithm. Compared with hard clustering algorithms such as K-means, it reduces the interference of noise on the clustering results to a certain extent, and this algorithm effectively defines the gene and relationship between clusters. We used the Mfuzz method to perform cluster analysis of protein expression in different consecutive samples. To further understand the biological processes involved in the proteins in each cluster, we performed enrichment analysis of GO functions, KEGG pathways and protein domains for the proteins in each cluster.
The figure shows the summary of Mfuzz analysis of expression patterns of continuous samples. On the left side of the figure, a line chart is used to visually show the trend of protein expression changes in consecutive samples, which are then divided into 6 different trends (clusters) through cluster analysis. For each trended protein set, expression heatmaps are drawn and GO function, KEGG pathway and protein domain enrichment analyses are performed.
The heatmap based on functional enrichment cluster analysis of protein clusters. The horizontal lines represent different Mfuzz clusters; the vertical lines represent the KEGG pathway with significantly enriched cluster proteins. The corresponding color blocks indicate the degree of enrichment of different cluster proteins in different KEGG pathways. Red represents strong enrichment and blue represents weak enrichment.
Investigation of Specific Proteins Related to Different Types of Coronary Atherosclerosis-2021-Frontiers in Cardiovascular Medicine
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