Integrated Correlation Analysis of Proteomics and Transcriptomics Data in Alzheimer's Disease

Abstract

We wanted to see if there existed any significant correlations between two -omics layers. So, here, we performed a correlation analysis to study the disease. The pipeline building consisted of first performing the differential expression of two datasets (proteomics and transcriptomics) individually. An in-depth analysis of the proteomics data was performed, followed by differential expression analysis of RNA seq data and then a correlational analysis of the differentially expressed proteins (from proteomics data) and genes (from RNA seq data). From our analysis, we found fascinating information about the correlations between proteins and genes in AD. We performed a correlation analysis of AD (N= 84), Control (N = 31), and PSP (N = 85) samples for proteomics data and got 114 differentially expressed proteins (DEPs = 114). The RNA seq data had AD (N = 82), Control (N = 31) and PSP (N = 84) samples which gave us 61 differentially expressed genes (DEGs = 61). A correlation analysis using Spearman’s correlation coefficient method between proteins involved in AD revealed 192 very significant correlations with p-value <= 0.00000000000005. The mean correlation coefficient was quite high (r = 0.52). A correlation analysis using Spearman’s correlation coefficient method between genes involved in AD revealed 208 very significant correlations with p-value <= 0.00000000000005. The mean correlation coefficient was quite high (r = 0.52). A correlation analysis using Spearman’s correlation coefficient method between proteins and genes involved in AD revealed 395 significant correlations with p-value <= 0.0001. The correlation coefficient (quite high of +0.53), which might help in understanding the molecular pathways behind the disease could uncover new prospects of understanding the disease as well as design treatments. We observed that different genes interact with different proteins (correlation coefficient r >= 0.5, p-value < 0.05). We also observed that a single protein interacts with multiple genes, and a single gene is interestingly associated with multiple proteins. The patterns of correlations are also different in that a protein/gene positively correlates with some proteins/genes and negatively with some other proteins/genes. We hope that this observation is quite useful. However, understanding how it works and how they interact with each other needs further assessment at the molecular level.