The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Title: Personalized Network-Based Anti-Cancer Therapy Evaluation
Version: 1.0.1
Maintainer: Ege Ulgen <egeulgen@gmail.com>
Description: Identification of the most appropriate pharmacotherapy for each patient based on genomic alterations is a major challenge in personalized oncology. 'PANACEA' is a collection of personalized anti-cancer drug prioritization approaches utilizing network methods. The methods utilize personalized "driverness" scores from 'driveR' to rank drugs, mapping these onto a protein-protein interaction network. The "distance-based" method scores each drug based on these scores and distances between drugs and genes to rank given drugs. The "RWR" method propagates these scores via a random-walk with restart framework to rank the drugs. The methods are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2023. PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology. Bioinformatics <doi:10.1093/bioinformatics/btad022>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.2.3
URL: https://github.com/egeulgen/PANACEA, https://egeulgen.github.io/PANACEA/
BugReports: https://github.com/egeulgen/PANACEA/issues
Imports: org.Hs.eg.db, DBI, igraph, reshape2
Suggests: rmarkdown, knitr, testthat (≥ 3.0.0), covr
Config/testthat/edition: 3
Depends: R (≥ 4.0)
LazyData: true
LazyDataCompression: xz
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2023-08-19 12:57:02 UTC; egeulgen
Author: Ege Ulgen ORCID iD [aut, cre, cph]
Repository: CRAN
Date/Publication: 2023-08-19 13:22:36 UTC

PANACEA: Personalized Network-based Anti-Cancer Therapy Evaluation

Description

Identification of the most appropriate pharmacotherapy for each patient based on genomic alterations is a major challenge in personalized oncology. PANACEA is a collection of personalized anti-cancer drug prioritization approaches utilizing network methods. The methods utilize personalized "driverness" scores from 'driveR' to rank drugs, mapping these onto a protein-protein interaction network (PIN). The "distance-based" method scores each drug based on these scores and distances between drugs and genes to rank given drugs. The "RWR" method propagates these scores via a random-walk with restart framework to rank the drugs.

Author(s)

Maintainer: Ege Ulgen egeulgen@gmail.com (ORCID) [copyright holder]

See Also

score_drugs for the wrapper function for scoring of drugs via network-based methods

DGIdb Interactions Expert-curated Sources

Description

Data frame containing drug-gene interactions from expert-curated sources (CancerCommons, CGI, ChemblInteractions, CIViC, ClearityFoundationBiomarkers, ClearityFoundationClinicalTrial, COSMIC, DoCM, MyCancerGenome, MyCancerGenomeClinicalTrial, TALC, TdgClinicalTrial, TEND) from DGIdb.

Usage

DGIdb_interactions_df

Format

a data frame containing 11323 rows and 2 variables:

drug_name

Drug name

gene_name

HGNC gene symbol for the interacting gene

Graph Laplacian Normalization

Description

Graph Laplacian Normalization

Usage

Laplacian.norm(W)

Arguments

W

square symmetric adjacency matrix

Value

normalized adjacency matrix

Adjacency List for STRING v11.5 - High Confidence Interactions

Description

Data frame of adjacency list for STRING v11.5 interactions with combined score > 700 (high confidence)

Usage

STRING_adj_df

Format

a data frame with 887797 rows and 3 variables:

protein1

Interactor 1

protein2

Interactor 2

value

edge weight(combined score)

Add Drugs as Nodes

Description

Add Drugs as Nodes

Usage

add_drugs_as_nodes(W_mat, drug_target_interactions, edge_weight = 1000)

Arguments

W_mat

adjacency matrix for the chosen PIN

drug_target_interactions

data frame containing (processed) drugs and target genes

edge_weight

edge weight for drug-target gene interaction (default = 1000)

Value

adjacency matrix with the drugs added as nodes

Turn Adjacency List into Adjacency Matrix

Description

Turn Adjacency List into Adjacency Matrix

Usage

adj_list2mat(adj_list)

Arguments

adj_list

Adjacency list

Value

Adjacency matrix

Convert Input Gene Symbols to Alias

Description

Convert Input Gene Symbols to Alias

Usage

convert2alias(input_genes, target_genes)

Arguments

input_genes

vector of input genes

target_genes

vector of target genes

Value

vector of converted gene symbols (if any alias in target genes)

Example driveR Result

Description

Data frame containing 'driveR' results for a lung adenocarcinoma case.

Usage

example_driveR_res

Format

a data frame containing 106 rows and 3 variables:

gene_symbol

HGNC gene symbol

driverness_prob

'driverness' probability

prediction

driveR's prediction for whether the gene is a 'driver' or 'non-driver'

Example PANACEA "RWR" Method Result

Description

Vector containing 'PANACEA' "RWR" results for a lung adenocarcinoma case. Names are drug names, values are scores

Usage

example_scores_RWR

Format

named vector of 1423 values

Example PANACEA "distance-based" Method Result

Description

Vector containing 'PANACEA' "distance-based" results for a lung adenocarcinoma case. Names are drug names, values are scores

Usage

example_scores_dist

Format

named vector of 1423 values

Network Propagation (Random-walk with Restart)

Description

Network Propagation (Random-walk with Restart)

Usage

network_propagation(prior_vec, W_prime, alpha, max.iter = 1000, eps = 1e-04)

Arguments

prior_vec

vector of prior knowledge on selected genes (names are gene symbols)

W_prime

(Laplacian-normalized, symmetric) adjacency matrix

alpha

restart parameter, controlling trade-off between prior information and network smoothing

max.iter

maximum allowed number of iterations (default = 1000)

eps

epsilon value to assess the L2 norm of the difference between iterations (default = 1e-4)

Details

Implementing RWR following the following publications: Cowen L, Ideker T, Raphael BJ, Sharan R. Network propagation: a universal amplifier of genetic associations. Nat Rev Genet. 2017 Sep;18(9):551–62. Shnaps O, Perry E, Silverbush D, Sharan R. Inference of personalized drug targets via network propagation. Pac Symp Biocomput. 2016;21:156–67.

Value

vector of propagation values

Process Drug-Target Interactions

Description

Process Drug-Target Interactions

Usage

process_drug_target_interactions(
  drug_target_interactions,
  PIN_genes,
  drug_name_col = "drug_name",
  target_col = "gene_name"
)

Arguments

drug_target_interactions

data frame containing drugs and target genes

PIN_genes

gene symbols for the chosen PIN

drug_name_col

name of the column containing drug names (default = "drug_name")

target_col

name of the column containing drug targets (default = "converted_target_gene")

Value

processed drug-target interactions. Processing involves converting symbols missing in the PIN, merging drugs that have the same target gene(s)

Scoring of Drugs via Network-based Methods

Description

Scoring of Drugs via Network-based Methods

Usage

score_drugs(driveR_res, drug_interactions_df, W_mat, method, ...)

Arguments

driveR_res

data frame of driveR results

drug_interactions_df

data frame of drug-gene interactions

W_mat

adjacency matrix for the PIN

method

scoring method (one of 'distance-based' or 'RWR')

...

additional arguments for score_drugs_distance_based or score_drugs_RWR_based

Details

This is the wrapper function for the two proposed methods for personalized scoring of drugs for individual cancer samples via network-based methods. The available methods are 'distance-based' and 'RWR'. For the 'distance-based' method, the score between a gene (g) and drug (d) is formulated as:

score(g, d) = driver(g) / (d(g, d) + 1)^2

where driver(g) is the driverness probability of gene g, as predicted by 'driveR' and d(g, d) is the distance withing the PIN between gene g and drug d. The final score of the drug d is then the average of the scores between each altered gene and d:

score(d) = \Sigma{score(g, d)} / |genes|

For the 'RWR' method, a random-walk with restart framework is used to propagate the driverness probabilities.

By default DGIdb_interactions_df is used as the drug_interactions_df.

If the W_mat argument is not supplied, the built-in STRNG data STRING_adj_df is used to generate W_mat.

Value

vector of scores per drug.

Examples

toy_data <- data.frame(
  gene_symbol = c("TP53", "EGFR", "KDR", "ATM"),
  driverness_prob = c(0.94, 0.92, 0.84, 0.72)
)
toy_interactions <- DGIdb_interactions_df[1:25, ]
res <- score_drugs(
  driveR_res = toy_data,
  drug_interactions_df = toy_interactions, # leave blank for default
  W_mat = toy_W_mat, # leave blank for default
  method = "distance-based",
  verbose = FALSE
)

RWR-based Scoring of Drugs

Description

RWR-based Scoring of Drugs

Usage

score_drugs_RWR_based(
  driveR_res,
  drug_interactions_df,
  W_mat,
  alpha = 0.05,
  max.iter = 1000,
  eps = 1e-04,
  drug_name_col = "drug_name",
  target_col = "gene_name",
  verbose = TRUE
)

Arguments

driveR_res

data frame of driveR results

drug_interactions_df

data frame of drug-gene interactions

W_mat

adjacency matrix for the PIN

alpha

restart parameter, controlling trade-off between prior information and network smoothing

max.iter

maximum allowed number of iterations (default = 1000)

eps

epsilon value to assess the L2 norm of the difference between iterations (default = 1e-4)

drug_name_col

for 'drug_interactions_df', the column name containing drug names/identifiers

target_col

for 'drug_interactions_df', the column name containing target gene symbols

verbose

boolean to control verbosity (default = TRUE)

Value

vector of scores per drug. Drugs with the same target gene(s) are merged (via process_drug_target_interactions)

Examples

toy_data <- data.frame(
  gene_symbol = c("TP53", "EGFR", "KDR", "ATM"),
  driverness_prob = c(0.94, 0.92, 0.84, 0.72)
)
toy_interactions <- DGIdb_interactions_df[1:100, ]
res <- score_drugs_RWR_based(
  driveR_res = toy_data,
  drug_interactions_df = toy_interactions,
  W_mat = toy_W_mat, verbose = FALSE
)

Distance-based Scoring of Drugs

Description

Distance-based Scoring of Drugs

Usage

score_drugs_distance_based(
  driveR_res,
  drug_interactions_df,
  W_mat,
  driver_prob_cutoff = 0.05,
  drug_name_col = "drug_name",
  target_col = "gene_name",
  verbose = TRUE
)

Arguments

driveR_res

data frame of driveR results

drug_interactions_df

data frame of drug-gene interactions

W_mat

adjacency matrix for the PIN

driver_prob_cutoff

cut-off value for 'driverness_prob' (default = 0.05)

drug_name_col

for 'drug_interactions_df', the column name containing drug names/identifiers

target_col

for 'drug_interactions_df', the column name containing target gene symbols

verbose

boolean to control verbosity (default = TRUE)

Value

vector of scores per drug. Drugs with the same target gene(s) are merged (via process_drug_target_interactions)

Examples

toy_data <- data.frame(
  gene_symbol = c("TP53", "EGFR", "KDR", "ATM"),
  driverness_prob = c(0.94, 0.92, 0.84, 0.72)
)
toy_interactions <- DGIdb_interactions_df[1:100, ]
res <- score_drugs_distance_based(
  driveR_res = toy_data,
  drug_interactions_df = toy_interactions,
  W_mat = toy_W_mat, verbose = FALSE
)

Toy Adjacency Matrix (for examples)

Description

Symmetric matrix containing example adjacency data

Usage

toy_W_mat

Format

matrix of 84 rows and 84 columns

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.