CARDIA proteiomics pathway analysis_Feb14-2024

1 Introduction

1.1 libraries

This is my R Markdown document.

2 CARDIA Project:: Proteiomics pathway analysis_Feb14-2024

2.1 Identify the variables, model in use and seperate the significant protein markers based on its pvalue<0.05.

2.2 Pathway Analysis

2.2.1 Identify the UniProt ID coloum

2.2.2 UniProt IDs to Entrez Gene IDs

2.2.3 Perform pathway enrichment analysis

2.2.4 Print the pathway enrichment results

2.2.5 Plot pathway enrichment results

3 The format of CARDIA proteiomics dataset should look familiar to anyone who has worked with differential expression results. It contains columns of gene identifiers (UNIPORT ID ), Protein symbols, log2foldchange values, P-Values and adjusted P-Values.

4 Identify the Uniport ID and convert them to Entrez Gene ID as its required for KEGG pathay analysis.

5 Now let’s prepare significant protein lists using some conventional criteria (Pvalue<0.05).

6 We have Uniport IDs, but the package we are going to use wants Entrez IDs. Fortunately, the package provides its own converter called bitr. This is the first function in the clusterProfiler package that we’ll be using:

7 Pathway Enrichment Analysis:: we are ready to perform enrichment analysis. Let’s start with Gene Ontology.

8 Convert Enrichment result to table

knitr::opts_chunk$set(collapse = TRUE, include = TRUE)

enrich_result_df<- data.frame(enrich_result)

enrich_result_2 <- enrichKEGG(gene         = gene_ids_df$gene_ids,
                            organism     = 'hsa',
                            pvalueCutoff = 0.05)

kegg <- enrichKEGG(  gene_ids_df$gene_ids,  organism = "hsa",  keyType = "kegg",  pvalueCutoff = 0.05,
                     pAdjustMethod = "BH", qvalueCutoff = 0.05)

p<- dotplot(kegg, showCategory=20) + ggtitle("CARDIA_protiomics ")
p + theme(
  plot.title = element_text(color="black", size=14, face="bold", hjust=0.5))