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From a biological point of view, the transcriptome represents the intermediate state of gene expression, which can reflect mechanisms such as transcriptional regulation and post-transcriptional regulation. While proteins are the direct function executors of organisms. mRNA-protein interactions can only be truly observed when biological samples are systematically studied in combination with transcriptomic and proteomic expression data. It provides powerful help for exploring disease mechanism, medical diagnosis, drug research and development, etc.
The quantitative correlation between the proteome and the transcriptome of the samples processed under multiple experimental conditions is reflected by the cumulative distribution map of the pearson correlation coefficient of the expression of each gene. And then based on the pearson correlation coefficient data, GSEA enrichment analysis was performed on genes to reveal the KEGG pathway potentially involved in proteins or transcripts under different regulatory relationships.
Pearson correlation coefficient distribution diagram of gene expression at transcription level and protein level. The horizontal axis is the Pearson correlation coefficient, and the vertical axis is the number of genes.
GSEA analysis results of KEGG pathway based on Pearson correlation coefficient. The color of the left bar represents the Pearson correlation coefficient of the gene, and the color of the right square represents the NES value of the KEGG pathway GSEA analysis.
Integrative Proteomic Characterization of Human Lung Adenocarcinoma-2020-Cell
Proteogenomic Characterization of Human Early-Onset Gastric Cancer-2019-Cancer Cell
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