The Drug target set enrichment analysis (DTSEA)

Description

The DTSEA implements a novel application to GSEA and extends the adoption of GSEA.

The Drug Target Set Enrichment Analysis (DTSEA) is a novel tool used to identify the most effective drug set against a particular disease based on the Gene Set Enrichment Analysis (GSEA).

The central hypothesis of DTSEA is that the targets of potential candidates for a specific disease (e.g., COVID-19) ought to be close to each other, or at least not so far away from the disease. The DTSEA algorithm determines whether a drug is potent for the chosen disease by the proximity between drug targets and the disease-related genes. Under the central hypothesis of DTSEA, the DTSEA consists of two main parts:

1. Evaluate the influence of the specific disease in the PPI network by the random walk with restart algorithm.
   To evaluate the influence, we compute the disease-node distance by using the random walk with restart (RwR) algorithm, then rank the nodes reversely.

2. Evaluate the drug-disease associations based on GSEA.
   The GSEA approach is adopted in this part to identify whether candidate drug targets are disease-related (top) or disease-unrelated (bottom) on the human PPI list. The specific disease gene list is normalized by the median and is set zero as the arbitrary cutoff point to classify the relations manually.

In this package, we provide the example data, which is a small set of data to demonstrate the usage and the main idea behind DTSEA. We provide some extra data files, the real data we used in the DTSEA paper. The supplementary package is now on the GitHub. Anyone can obtain this package by the example code.

Details

DTSEA

Examples

# if (!"devtools" %in% as.data.frame(installed.packages())$Package)
#   install.packages("devtools")
# devtools::install_github("hanjunwei-lab/DTSEAdata")

Main function of drug target set enrichment analysis (DTSEA)

Description

The DTSEA function determines whether a drug is potent for a specific disease by the proximity between its targets and the disease-related genes.

Usage

DTSEA(
  network,
  disease,
  drugs,
  rwr.pt = 0,
  sampleSize = 101,
  minSize = 1,
  maxSize = Inf,
  nproc = 0,
  eps = 1e-50,
  nPermSimple = 5000,
  gseaParam = 1,
  verbose = TRUE
)

Arguments

Value

The resulting dataframe consists of drug_id, pval, padj, log2err, ES, NES, size, and leadingEdge.

Examples

library(dplyr)
library(DTSEA)

# Load the data
data("example_disease_list", package = "DTSEA")
data("example_drug_target_list", package = "DTSEA")
data("example_ppi", package = "DTSEA")

# Run the DTSEA and sort the result dataframe by normalized enrichment scores
# (NES)
result <- DTSEA(
  network = example_ppi,
  disease = example_disease_list,
  drugs = example_drug_target_list,
  verbose = FALSE
) %>%
arrange(desc(NES))

# Or you can utilize the multi-core advantages by enable nproc parameters
# on non-Windows operating systems.
## Not run: result <- DTSEA(
         network = example_ppi,
         disease = example_disease_list,
         drugs = example_drug_target_list,
         nproc = 10, verbose = FALSE
)
## End(Not run)

# We can extract the significantly NES > 0 drug items.
result %>%
  filter(NES > 0 & pval < .05)
# Or we can draw the enrichment plot of the first predicted drug.
fgsea::plotEnrichment(
  pathway = example_drug_target_list %>%
    filter(drug_id == slice(result, 1)$drug_id) %>%
    pull(gene_target),
  stats = random.walk(network = example_ppi,
                      p0 = calculate_p0(nodes = example_ppi,
                                        disease = example_disease_list)
                      )
)

# If you have obtained the supplemental data, then you can do random walk
# with restart in the real data set

# supp_data <- get_data(c("graph", "disease_related", "example_ppi"))
# result <- DTSEA(network = supp_data[["graph"]],
#                disease = supp_data[["disease_related"]],
#                drugs = supp_data[["drug_targets"]],
#                verbose = FALSE)

Calculate between variance in network

Description

No description

Usage

calculate_between(graph, set_a, set_b)

Arguments

Value

a positive number

Function to calculate the p0 vector used in Random Walk with Restart (RwR)

Description

The function provides a reliable approach to generating a p0 vector.

Usage

calculate_p0(nodes, disease)

Arguments

Value

The resulting p0 vector.

Examples

library(DTSEA)
library(dplyr)

# Load the data
data("example_disease_list", package = "DTSEA")
data("example_drug_target_list", package = "DTSEA")
data("example_ppi", package = "DTSEA")

# Compute the p0 vector
p0 <- calculate_p0(nodes = example_ppi, disease = example_disease_list)

# You can decrease the order of the p0 to get the most affected nodes.
p0 <- sort(p0, decreasing = TRUE) %>%
  names() %>%
  head(10)

# If you have obtained the supplemental data, then you can compute the p0
# in the real data set

# supp_data <- get_data(c("graph", "disease_related"))
# p0 <- calculate_p0(nodes = supp_data[["graph"]],
#                    disease = supp_data[["disease_related"]])

Calculate within variance

Description

No description

Usage

calculate_within(graph, given_set)

Arguments

Value

a positive number

Cronbach's alpha

Description

Computes Cronbach's alpha

Usage

cronbach.alpha(data)

Arguments

Value

The Cronbach's alpha (unstandardized)

Examples

library(DTSEA)
library(tibble)

# Load the data
data <- tribble(~x, ~y, ~z, 1, 1, 2, 5, 6, 5, 7, 8, 4, 2, 3, 2, 8, 6, 5)

# Run Cronbach's alpha
cat(cronbach.alpha(data))

An example vector of disease nodes

Description

The list was integrated the significantly differentially expressed genes (DEGs) of GEO dataset GSE183071 and the work from Feng, Song, Guo, and et al.

Usage

example_disease_list

Format

An object of class character of length 63.

References

Gómez-Carballa A, Rivero-Calle I, Pardo-Seco J, Gómez-Rial J, Rivero-Velasco C, Rodríguez-Núñez N, Barbeito-Castiñeiras G, Pérez-Freixo H, Cebey-López M, Barral-Arca R, Rodriguez-Tenreiro C, Dacosta-Urbieta A, Bello X, Pischedda S, Currás-Tuala MJ, Viz-Lasheras S, Martinón-Torres F, Salas A; GEN-COVID study group. A multi-tissue study of immune gene expression profiling highlights the key role of the nasal epithelium in COVID-19 severity. Environ Res. 2022 Jul;210:112890. doi: 10.1016/j.envres.2022.112890. Epub 2022 Feb 22. PMID: 35202626; PMCID: PMC8861187.

Feng S, Song F, Guo W, Tan J, Zhang X, Qiao F, Guo J, Zhang L, Jia X. Potential Genes Associated with COVID-19 and Comorbidity. Int J Med Sci. 2022 Jan 24;19(2):402-415. doi: 10.7150/ijms.67815. PMID: 35165525; PMCID: PMC8795808.

Examples

library(DTSEA)

data("example_disease_list", package = "DTSEA")

An example data frame of drug target lists

Description

Drug-target interactions were downloaded and integrated from DrugBank and ChEMBL.

Usage

example_drug_target_list

Format

A data frame with 970 rows and 3 variables:

• drug_id: the DrugBank ID

• drug_name: the name of each drug

• gene_target: the targets of drugs

References

Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, Maciejewski A, Gale N, Wilson A, Chin L, Cummings R, Le D, Pon A, Knox C, Wilson M. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018 Jan 4;46(D1):D1074-D1082. doi: 10.1093/nar/gkx1037. PMID: 29126136; PMCID: PMC5753335.

Gaulton A, Hersey A, Nowotka M, Bento AP, Chambers J, Mendez D, Mutowo P, Atkinson F, Bellis LJ, Cibrián-Uhalte E, Davies M, Dedman N, Karlsson A, Magariños MP, Overington JP, Papadatos G, Smit I, Leach AR. The ChEMBL database in 2017. Nucleic Acids Res. 2017 Jan 4;45(D1):D945-D954. doi: 10.1093/nar/gkw1074. Epub 2016 Nov 28. PMID: 27899562; PMCID: PMC5210557.

Examples

library(DTSEA)
data("example_drug_target_list", package = "DTSEA")

An example human gene functional interaction network object

Description

We extracted the gene functional interaction network from multiple sources with experimental evidence and then integrated them.

Usage

example_ppi

Format

An igraph object

References

Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 2021 Jan 8;49 (D1):D545-D551. doi: 10.1093/nar/gkaa970. PMID: 33125081; PMCID: PMC7779016.

Examples

library(DTSEA)
data("example_ppi", package = "DTSEA")

Get extra data

Description

Get extra data

Usage

get_data(name)

Arguments

Value

A list with the wanted data

Examples

# Do some stuff

data <- get_data("ncbi_list")

Kendall's coefficient of concordance W

Description

Computes the Kendall's coefficient of concordance.

Usage

kendall.w(raw, correct = TRUE)

Arguments

Value

The resulting list consists of title, kendall.w, chisq, df, pval, report.

Examples

library(DTSEA)
library(tibble)

# Load the data
data <- tribble(~x, ~y, ~z, 1,1,2,  5,6,5,  7,8,4, 2,3,2, 8,6,5)

# Run Kendall's W
print(kendall.w(data)$report)

Function to implement Random Walk with Restart (RwR) algorithm on the input graph

Description

Function random.walk is supposed to implement the original Random Walk with Restart (RwR) on the input graph. If the seeds (i.e., a set of starting nodes) are given, it intends to calculate the affinity score of all nodes in the graph to the seeds.

Usage

random.walk(
  network,
  p0,
  edge_weight = FALSE,
  gamma = 0.7,
  threshold = 1e-10,
  pt.post.processing = "log",
  pt.align = "median",
  verbose = FALSE
)

Arguments

Value

pt vector

Examples

library(DTSEA)

# Load the data
data("example_disease_list", package = "DTSEA")
data("example_drug_target_list", package = "DTSEA")
data("example_ppi", package = "DTSEA")

# Perform random walk
p0 <- calculate_p0(nodes = example_ppi, disease = example_disease_list)
pt <- random.walk(network = example_ppi, p0 = p0)

# Perform GSEA analysis
# ....

# If you have obtained the supplemental data, then you can do random walk
# with restart in the real data set

# supp_data <- get_data(c("graph", "disease_related", "example_ppi"))
# p0 <- calculate_p0(nodes = supp_data[["graph"]],
#                    disease = supp_data[["disease_related"]])
# pt <- random.walk(network = supp_data[["example_ppi"]],
#                   p0 = p0)

A random graph for the computation of the separation measure

Description

The random graph was retrieved from Menche et al (2015).

Usage

random_graph

Format

An igraph object

References

Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barabási AL. Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science. 2015 Feb 20;347(6224):1257601. doi: 10.1126/science.1257601. PMID: 25700523; PMCID: PMC4435741.

Examples

library(DTSEA)
data("random_graph", package = "DTSEA")

A measure of network separation

Description

Calculates the separation of two sets of nodes on a network. The metric is calculated as in Menche et al. (2015).

Usage

separation(graph, set_a, set_b)

Arguments

Value

The separation and distance measurement of the specified two modules.

Examples

library(DTSEA)

# Load the data
data("random_graph", package = "DTSEA")

# Compute the separation metric
separation <- separation(
  graph = random_graph,
  set_a = c("4", "6", "8", "13"),
  set_b = c("8", "9", "10", "15", "18")
)
cat(separation, "\n")