Post Analysis: Pathway Enrichment & Automated Literature Search

This vignette will guide you through the post analysis of the results obtained from the HDAnalyzeR pipeline. The post analysis consists of two possible steps: pathway enrichment analysis and automated literature search. The pathway enrichment analysis is performed using the Gene Ontology and Reactome databases from clusterProfiler and ReactomePA packages respectively. The automated literature search is performed using the the PubMed database.

Let’s start by loading the packages, widen the example dataset, load the metadata, and run some differential expression analysis to get the results for the post analysis. For the Over Representation Analysis we could also use the features list from the classification models or even run both and get the intersect as it is done in the Get Started guide.

library(HDAnalyzeR)
library(dplyr)
library(utils)

wide_data <- widen_data(example_data)
metadata <- example_metadata
de_res <- do_limma(wide_data, metadata, case = "AML", control = c("CLL", "MYEL", "GLIOM"))

First, we will perform an Over Representation Analysis (ORA) using the Gene Ontology database. We will use the do_ora() function. The function requires a list of proteins. In this example we will extract the top-20 up-regulated proteins.

proteins <- de_res$de_results |> filter(logFC > 0) |> pull(Assay) |> head(20)

ora_res <- do_ora(proteins, "GO")
#> No background provided. When working with Olink data it is recommended to use background.
#> 
#> 
#> 'select()' returned 1:1 mapping between keys and columns
plot_ora(ora_res, proteins)
#> 'select()' returned 1:1 mapping between keys and columns
#> $dotplot

#> 
#> $barplot

#> 
#> $cnetplot

> 📓 When working with a real Olink dataset, it is recommended to use a background list of proteins that are present in the Olink panel to minimize bias. This can be done by using the background parameter in the do_ora() function.

Let’s change the database and the p-value threshold.

ora_res <- do_ora(proteins, "Reactome", pval = 0.2)
plot_ora(ora_res, proteins, pval = 0.2, fontsize = 6)
#> $dotplot

#> 
#> $barplot

#> 
#> $cnetplot

We can also run a Gene Set Enrichment Analysis (GSEA) using the do_gsea() function. In this case, the function requires differential expression results.

gsea_res <- do_gsea(de_res$de_results, "GO", pval_lim = 0.7)
plot_gsea(gsea_res, de_res$de_results, pval_lim = 0.7, fontsize = 7)
#> $dotplot

#> 
#> $cnetplot

#> 
#> $ridgeplot

#> 
#> $gseaplot

Finally, let’s perform an automated literature search using the literature_search(). The function requires a list with disease names as names and proteins as values. We will create the list, run the search and preview the results.

biomarkers <- list("acute myeloid leukemia" = c("FLT3", "EPO"),
                   "chronic lymphocytic leukemia" = c("PARP1", "FCER2"))

lit_res <- literature_search(biomarkers, max_articles = 5)
#> Searching for articles on FLT3 and acute myeloid leukemia
#> Searching for articles on EPO and acute myeloid leukemia
#> Searching for articles on PARP1 and chronic lymphocytic leukemia
#> Searching for articles on FCER2 and chronic lymphocytic leukemia

lit_res$`acute myeloid leukemia`$FLT3$title
#> [1] "Only FLT3-ITD co-mutation did not have a deleterious effect on acute myeloid leukemia patients with NPM1 mutation, but concomitant with DNMT3A co-mutation or a&#x2009;&lt;&#x2009;3log reduction of MRD2 predicted poor survival."
#> [2] "Synergistic effect of FMS-like tyrosine kinase-3 (FLT3) inhibitors combined with a CDK7 inhibitor in FLT3-ITD-mutated acute myeloid leukemia."                                                                                     
#> [3] "Distinct <i>FLT3</i> Pathways Gene Expression Profiles in Pediatric De Novo Acute Lymphoblastic and Myeloid Leukemia with <i>FLT3</i> Mutations: Implications for Targeted Therapy."                                               
#> [4] "Enhancing Therapeutic Efficacy of FLT3 Inhibitors with Combination Therapy for Treatment of Acute Myeloid Leukemia."                                                                                                               
#> [5] "Simultaneous inhibition of FLT3 and HDAC by novel 6-ethylpyrazine-2-Carboxamide derivatives provides therapeutic advantages in acute myelocytic leukemia."

📓 Remember that these data are a dummy-dataset with fake data and the results in this guide should not be interpreted as real results. The purpose of this vignette is to show you how to use the package and its functions.