Getting Started with drugfindR

Introduction

Welcome to drugfindR! This package provides R-based programmatic access to the iLINCS (Integrative Library of Integrated Network-Based Cellular Signatures) database for drug repurposing and functional genomics research.

What is drugfindR?

drugfindR enables researchers to:

Identify candidate repurposable drugs from transcriptomic signatures
Discover functional relationships between genes and compounds
Query LINCS databases programmatically without web platform dependencies
Process signatures efficiently with high-throughput batch capabilities

What is LINCS?

The Library of Integrated Network-Based Cellular Signatures (LINCS) project systematically catalogs cellular responses to genetic and chemical perturbations. The iLINCS platform integrates three major signature types:

Chemical Perturbagen (CP): Drug and compound treatment signatures
Gene Knockdown (KD): Gene silencing signatures
Gene Overexpression (OE): Gene upregulation signatures

Installation

From r-universe (Recommended)

install.packages("drugfindR",
    repos = c(
        "https://cogdisreslab.r-universe.dev",
        "https://cran.r-project.org"
    )
)

From GitHub (Development Version)

if (!requireNamespace("devtools", quietly = TRUE)) {
    install.packages("devtools")
}
devtools::install_github("CogDisResLab/drugfindR")

Loading the Package

library(drugfindR)
library(dplyr)
library(readr)

Two Approaches to Using drugfindR

drugfindR offers two complementary approaches:

1. High-Level Convenience Functions

These wrapper functions handle the entire workflow with sensible defaults:

investigateSignature(): Analyze transcriptomic data to find concordant drugs/genes
investigateTarget(): Explore functional relationships for a specific gene or drug

Use when: You want rapid results with minimal code

2. Modular Pipeline Functions

Five building-block functions provide fine-grained control:

getSignature(): Retrieve LINCS signatures by ID
prepareSignature(): Format custom signatures for iLINCS
filterSignature(): Apply thresholds to signatures
getConcordants(): Query iLINCS for concordant signatures
consensusConcordants(): Generate consensus rankings

Use when: You need custom workflows or detailed intermediate results

Quick Start Example 1: Investigate a Transcriptomic Signature

Let’s identify candidate drugs for a disease signature using the convenience function.

Load Example Data

We’ll use a COVID-19 differential expression dataset included with the package:

# Load the COVID-19 differential expression data
covid_file <- system.file("extdata", "dCovid_diffexp.tsv", package = "drugfindR")
covid_diffexp <- read_tsv(covid_file, show_col_types = FALSE)

# Preview the data
head(covid_diffexp)
#> # A tibble: 6 × 3
#>   hgnc_symbol logFC     PValue
#>   <chr>       <dbl>      <dbl>
#> 1 CCL4L2      -3.98 0.00000177
#> 2 IL5RA       -4.83 0.00000870
#> 3 FN1          4.94 0.0000117 
#> 4 GSTM1       -8.21 0.0000153 
#> 5 CD180        2.15 0.0000202 
#> 6 FAM20C       3.11 0.0000255

This dataset contains: - hgnc_symbol: Gene symbols - logFC: Log fold-change values - PValue: Statistical significance

One-Line Analysis

# Find drugs that may counteract the COVID-19 signature
results <- investigateSignature(
    expr = covid_diffexp,
    outputLib = "CP", # Query Chemical Perturbagens
    filterThreshold = 1.5, # Keep genes with |logFC| >= 1.5
    geneColumn = "hgnc_symbol",
    logfcColumn = "logFC",
    pvalColumn = "PValue"
)

# View top candidates
head(results, 10)

Understanding the Results

The output contains:

Source: Your input signature identifier
Target: Drug/compound name
Similarity: Concordance score (-1 to 1)
- Negative scores: Drug reverses your signature (potential therapeutic)
- Positive scores: Drug mimics your signature
TargetSignature: iLINCS signature ID
TargetCellLine: Cell line used in the signature
pValue: Statistical significance

Quick Start Example 2: Investigate a Specific Gene

Let’s explore what happens when TP53 is knocked down and find drugs with similar effects.

# Find drugs that mimic TP53 knockdown
tp53_results <- investigateTarget(
    target = "TP53",
    inputLib = "KD", # Use Knockdown signatures
    outputLib = "CP", # Find Chemical Perturbagens
    filterThreshold = 0.5
)

# View results
head(tp53_results, 10)

Modular Approach: Step-by-Step Workflow

For more control, let’s break down the analysis into individual steps.

Step 1: Prepare Your Signature

# Convert your data to iLINCS format
signature <- prepareSignature(
    covid_diffexp,
    geneColumn = "hgnc_symbol",
    logfcColumn = "logFC",
    pvalColumn = "PValue"
)

# Preview the prepared signature
head(signature)
#>    signatureID ID_geneid Name_GeneSymbol Value_LogDiffExp Significance_pvalue
#> 1     InputSig      4860             PNP         1.709692         0.002436390
#> 5     InputSig      4125          MAN2B1         1.100506         0.003027542
#> 8     InputSig      2274            FHL2         1.330287         0.142375657
#> 14    InputSig       351             APP         1.050513         0.010844053
#> 26    InputSig      7077           TIMP2         1.795990         0.012416655
#> 29    InputSig     23659         PLA2G15         1.113302         0.049536693

The prepareSignature() function: - Standardizes column names - Maps genes to L1000 gene space - Validates data format

Step 2: Filter by Threshold

# Filter for strongly upregulated genes
filtered_up <- filterSignature(
    signature,
    direction = "up",
    threshold = 1.5
)

# Filter for strongly downregulated genes
filtered_down <- filterSignature(
    signature,
    direction = "down",
    threshold = 1.5
)

cat("Upregulated genes:", nrow(filtered_up), "\n")
#> Upregulated genes: 72
cat("Downregulated genes:", nrow(filtered_down), "\n")
#> Downregulated genes: 15

Step 3: Query for Concordant Signatures

# Find drugs that match upregulated genes
concordants_up <- getConcordants(
    filtered_up,
    ilincsLibrary = "CP",
    direction = "up"
)

# Find drugs that match downregulated genes
concordants_down <- getConcordants(
    filtered_down,
    ilincsLibrary = "CP",
    direction = "down"
)

Step 4: Generate Consensus Rankings

# Combine and rank candidate drugs
consensus <- consensusConcordants(
    concordants_up,
    concordants_down,
    paired = TRUE,
    cutoff = 0.2
)

head(consensus, 10)

Choosing Your Approach

Aspect	Convenience Functions	Modular Functions
Code Length	Minimal (1 function call)	Longer (5 steps)
Flexibility	Limited customization	Full control
Intermediate Results	Hidden	Available
Learning Curve	Easy	Moderate
Best For	Quick analyses, standard workflows	Custom pipelines, research

Filtering Strategies

Absolute Thresholds

Use specific log fold-change cutoffs:

# Single threshold (symmetric)
filtered_symmetric <- filterSignature(signature, threshold = 1.5)
# Keeps genes with |logFC| >= 1.5

# Double threshold (asymmetric)
filtered_asymmetric <- filterSignature(signature, threshold = c(-2.0, 1.5))
# Keeps genes with logFC <= -2.0 OR logFC >= 1.5

Proportional Thresholds

Select a percentage of most extreme genes:

# Top/bottom 10% most differentially expressed
filtered_prop <- filterSignature(signature, prop = 0.1)

Direction-Specific Filtering

# Only upregulated genes
up_only <- filterSignature(signature, direction = "up", threshold = 1.0)

# Only downregulated genes
down_only <- filterSignature(signature, direction = "down", threshold = 1.0)

# Both directions (default)
both_directions <- filterSignature(signature, direction = "any", threshold = 1.0)

Understanding Library Types

Chemical Perturbagen (CP)

Drug and compound treatment signatures
Use for: Drug repurposing, finding therapeutic candidates
Example: outputLib = "CP"

Gene Knockdown (KD)

Gene silencing (shRNA, siRNA) signatures
Use for: Understanding gene function, finding genes with similar effects
Example: inputLib = "KD"

Gene Overexpression (OE)

Gene upregulation signatures
Use for: Understanding gain-of-function effects
Example: inputLib = "OE"

Paired vs. Unpaired Analysis

Paired Analysis (Default)

Separately analyzes up- and down-regulated genes:

# Default behavior
results_paired <- investigateSignature(
    covid_diffexp,
    outputLib = "CP",
    filterThreshold = 1.5,
    paired = TRUE, # This is the default
    geneColumn = "hgnc_symbol"
)

Advantages: - More biological precision - Distinguishes directional effects - Better for complex signatures

Unpaired Analysis

Treats all significant genes together:

results_unpaired <- investigateSignature(
    covid_diffexp,
    outputLib = "CP",
    filterThreshold = 1.5,
    paired = FALSE,
    geneColumn = "hgnc_symbol"
)

Advantages: - Simpler interpretation - Faster execution - Good for exploratory analysis

Common Use Cases

1. Drug Repurposing

Goal: Find drugs that reverse a disease signature

disease_results <- investigateSignature(
    expr = disease_signature,
    outputLib = "CP",
    filterThreshold = 1.5,
    paired = TRUE
)

# Look for negative similarity scores (drug reverses disease)
potential_therapeutics <- disease_results %>%
    filter(Similarity < -0.3) %>%
    arrange(Similarity)

2. Gene Function Discovery

Goal: Understand what a gene does by finding similar genes

gene_function <- investigateTarget(
    target = "BRCA1",
    inputLib = "KD",
    outputLib = "KD", # Find genes with similar knockdown effects
    filterThreshold = 0.5
)

3. Mechanism of Action

Goal: Find genes affected by a drug

drug_mechanism <- investigateTarget(
    target = "metformin",
    inputLib = "CP",
    outputLib = "KD", # Find genes whose KD mimics drug effect
    filterThreshold = 0.5
)

Next Steps

Now that you understand the basics, explore these advanced topics:

Drug Repurposing from Transcriptomic Data: Deep dive into therapeutic discovery workflows
Target Investigation and Functional Genomics: Comprehensive guide to gene and drug target analysis
Package Documentation: Complete function reference

Session Information

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
#>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
#>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
#> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] readr_2.2.0      dplyr_1.2.1      drugfindR_1.0.0  BiocStyle_2.40.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] utf8_1.2.6          rappdirs_0.3.4      sass_0.4.10        
#>  [4] generics_0.1.4      stringi_1.8.7       hms_1.1.4          
#>  [7] digest_0.6.39       magrittr_2.0.5      evaluate_1.0.5     
#> [10] bookdown_0.47       fastmap_1.2.0       jsonlite_2.0.0     
#> [13] DFplyr_1.6.0        BiocManager_1.30.27 purrr_1.2.2        
#> [16] httr2_1.2.3         textshaping_1.0.5   jquerylib_0.1.4    
#> [19] cli_3.6.6           crayon_1.5.3        rlang_1.3.0        
#> [22] bit64_4.8.2         withr_3.0.3         cachem_1.1.0       
#> [25] yaml_2.3.12         otel_0.2.0          parallel_4.6.1     
#> [28] tools_4.6.1         tzdb_0.5.0          BiocGenerics_0.58.1
#> [31] curl_7.1.0          vctrs_0.7.3         R6_2.6.1           
#> [34] stats4_4.6.1        lifecycle_1.0.5     stringr_1.6.0      
#> [37] S4Vectors_0.50.1    fs_2.1.0            htmlwidgets_1.6.4  
#> [40] bit_4.6.0           vroom_1.7.1         ragg_1.5.2         
#> [43] pkgconfig_2.0.3     desc_1.4.3          pkgdown_2.2.1      
#> [46] pillar_1.11.1       bslib_0.11.0        glue_1.8.1         
#> [49] systemfonts_1.3.2   xfun_0.60           tibble_3.3.1       
#> [52] tidyselect_1.2.1    knitr_1.51          htmltools_0.5.9    
#> [55] rmarkdown_2.31      compiler_4.6.1

References

O’Donovan SM, Imami A, et al. (2021). Identification of candidate repurposable drugs to combat COVID-19 using a signature-based approach. Scientific Reports, 11:4495. doi:10.1038/s41598-021-84044-9
Subramanian A, et al. (2017). A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell, 171(6):1437-1452.e17. doi:10.1016/j.cell.2017.10.049

Ali Sajid Imami

2026-07-10