Introduction

The investigateTarget() function in drugfindR enables comprehensive functional genomics analyses by exploring relationships between genes and drugs in the LINCS database. This vignette covers:

Understanding what happens when a gene is knocked down or overexpressed
Finding drugs that mimic or reverse gene perturbation effects
Discovering genes with similar functional roles
Elucidating drug mechanisms of action

Prerequisites

library(drugfindR)
library(dplyr)
library(ggplot2)
library(tidyr)
library(purrr)

The Target Investigation Workflow

Key Questions Answered

Gene Function: What cellular changes occur when a gene is perturbed?
Drug Mimicry: Which drugs produce effects similar to gene perturbation?
Drug Opposition: Which drugs counteract gene perturbation effects?
Functional Similarity: Which genes have similar functional roles?
Mechanism of Action: What genes does a drug affect?

Workflow Overview

Target (Gene/Drug)
    ↓
Retrieve LINCS Signatures
    ↓
Filter by Magnitude/Direction
    ↓
Query Output Library
    ↓
Generate Consensus
    ↓
Functional Insights

Basic Target Investigation

Example 1: What does TP53 knockdown do?

TP53 is a tumor suppressor gene. Let’s explore what happens when it’s knocked down:

# Investigate TP53 knockdown effects
tp53_kd <- investigateTarget(
    target = "TP53",
    inputLib = "KD", # Use knockdown signatures
    outputLib = "KD", # Find similar gene knockdowns
    filterThreshold = 0.5,
    similarityThreshold = 0.3
)

# View results
head(tp53_kd, 15)

Interpreting Results

The output shows:

Target: Other genes whose knockdown produces similar effects
Similarity: Positive values = similar function, negative = opposite function
SourceSignature: TP53 knockdown signature ID(s)
TargetSignature: Matched gene signature IDs

# Find genes with similar function to TP53
similar_genes <- tp53_kd %>%
    filter(Similarity > 0.3) %>%
    arrange(desc(Similarity))

cat("Genes functionally similar to TP53:\n")
head(similar_genes, 10)

# Find genes with opposite function
opposite_genes <- tp53_kd %>%
    filter(Similarity < -0.3) %>%
    arrange(Similarity)

cat("\nGenes with opposite function:\n")
head(opposite_genes, 10)

Example 2: Which drugs mimic TP53 loss?

Cancer cells often lose TP53 function. Which drugs produce similar effects?

# Find drugs that mimic TP53 knockdown
tp53_mimics <- investigateTarget(
    target = "TP53",
    inputLib = "KD", # Start with KD signature
    outputLib = "CP", # Find chemical perturbagens
    filterThreshold = 0.5,
    similarityThreshold = 0.3
)

# Drugs with positive similarity mimic TP53 loss
mimicking_drugs <- tp53_mimics %>%
    filter(Similarity > 0.3) %>%
    arrange(desc(Similarity))

head(mimicking_drugs, 10)

Example 3: Which drugs rescue TP53 loss?

More importantly, which drugs might compensate for TP53 loss?

# Drugs with negative similarity oppose TP53 loss
rescue_drugs <- tp53_mimics %>%
    filter(Similarity < -0.3) %>%
    arrange(Similarity)

cat("Potential TP53 loss rescue compounds:\n")
head(rescue_drugs, 15)

Gene Overexpression Analysis

Example 4: Effects of MYC overexpression

MYC is an oncogene frequently overexpressed in cancer:

# Investigate MYC overexpression
myc_oe <- investigateTarget(
    target = "MYC",
    inputLib = "OE", # Overexpression signatures
    outputLib = "CP", # Find drugs
    filterThreshold = 0.5
)

# Drugs that oppose MYC overexpression (therapeutic potential)
myc_antagonists <- myc_oe %>%
    filter(Similarity < -0.3) %>%
    arrange(Similarity)

cat("Drugs that oppose MYC overexpression:\n")
head(myc_antagonists, 10)

Comparing KD vs OE

Different perturbation types reveal different biology:

# BRCA1 knockdown
brca1_kd <- investigateTarget(
    target = "BRCA1",
    inputLib = "KD",
    outputLib = "CP",
    filterThreshold = 0.5
)

# BRCA1 overexpression
brca1_oe <- investigateTarget(
    target = "BRCA1",
    inputLib = "OE",
    outputLib = "CP",
    filterThreshold = 0.5
)

# Compare top drugs
kd_top <- brca1_kd %>%
    filter(Similarity < -0.3) %>%
    pull(Target)
oe_top <- brca1_oe %>%
    filter(Similarity < -0.3) %>%
    pull(Target)

overlap <- intersect(kd_top, oe_top)
cat("Drugs opposing both KD and OE:", length(overlap), "\n")

Drug Mechanism of Action

Example 5: What does metformin affect?

Reverse the query to understand drug mechanisms:

# Find genes affected by metformin
metformin_targets <- investigateTarget(
    target = "metformin",
    inputLib = "CP", # Drug signature
    outputLib = "KD", # Find similar gene knockdowns
    filterThreshold = 0.5
)

# Genes whose loss mimics metformin treatment
affected_genes <- metformin_targets %>%
    filter(Similarity > 0.3) %>%
    arrange(desc(Similarity))

cat("Genes potentially targeted by metformin:\n")
head(affected_genes, 15)

Example 6: Compare drug to drug

Find drugs with similar mechanisms:

# Find drugs similar to aspirin
aspirin_similar <- investigateTarget(
    target = "aspirin",
    inputLib = "CP",
    outputLib = "CP",
    filterThreshold = 0.5
)

similar_drugs <- aspirin_similar %>%
    filter(Similarity > 0.4) %>%
    arrange(desc(Similarity))

head(similar_drugs, 10)

Paired vs. Unpaired Analysis

Paired Analysis (Default)

Analyzes up and down-regulated genes separately:

# Paired analysis captures directional effects
egfr_paired <- investigateTarget(
    target = "EGFR",
    inputLib = "KD",
    outputLib = "CP",
    filterThreshold = 0.5,
    paired = TRUE # Default
)

# Results preserve directional information
cat("Directional analysis:\n")
table(egfr_paired$InputSigDirection)

When to use paired:

Complex genes with many targets
When directionality matters
More biologically precise
Recommended for most analyses

Unpaired Analysis

Combines all significant genes:

# Unpaired analysis for simpler interpretation
egfr_unpaired <- investigateTarget(
    target = "EGFR",
    inputLib = "KD",
    outputLib = "CP",
    filterThreshold = 0.5,
    paired = FALSE
)

# Faster, simpler results
cat("Combined analysis genes:\n")
nrow(egfr_unpaired)

When to use unpaired:

Exploratory analysis
Simpler interpretation needed
Symmetric signatures
Computational efficiency matters

Cell Line Considerations

Filtering Input Cell Lines

Restrict source signatures to specific cell lines:

# Only use TP53 KD from breast cancer cell lines
tp53_breast <- investigateTarget(
    target = "TP53",
    inputLib = "KD",
    outputLib = "CP",
    inputCellLines = c("MCF7", "MDAMB231", "T47D"),
    filterThreshold = 0.5
)

cat("Results from breast cancer cell lines only\n")

Filtering Output Cell Lines

Restrict target signatures to specific contexts:

# Find drugs tested in lung cancer cell lines
kras_lung <- investigateTarget(
    target = "KRAS",
    inputLib = "KD",
    outputLib = "CP",
    outputCellLines = c("A549", "H1299", "H460"),
    filterThreshold = 0.5
)

cat("Drugs tested in lung cancer models\n")

Cross-Cell Line Analysis

# Get all results
all_cells <- investigateTarget(
    target = "TP53",
    inputLib = "KD",
    outputLib = "CP",
    filterThreshold = 0.5
)

# Analyze cell line distribution
cellline_summary <- all_cells %>%
    group_by(Target) %>%
    summarize(
        n_celllines = n_distinct(TargetCellLine),
        mean_similarity = mean(Similarity),
        .groups = "drop"
    ) %>%
    filter(n_celllines >= 3) %>%
    arrange(desc(n_celllines))

cat("Drugs tested across multiple cell lines:\n")
head(cellline_summary, 10)

Threshold Optimization

Conservative Analysis

High confidence, fewer hits:

conservative <- investigateTarget(
    target = "TP53",
    inputLib = "KD",
    outputLib = "CP",
    filterThreshold = 0.85, # Very stringent
    similarityThreshold = 0.4 # High similarity required
)

cat("Conservative results:", nrow(conservative), "\n")

Liberal Analysis

Broader coverage, more hypotheses:

liberal <- investigateTarget(
    target = "TP53",
    inputLib = "KD",
    outputLib = "CP",
    filterThreshold = 0.3, # Permissive
    similarityThreshold = 0.15 # Lower cutoff
)

cat("Liberal results:", nrow(liberal), "\n")

Comparing Thresholds

# Test multiple thresholds
thresholds <- c(0.3, 0.5, 0.7, 0.85)

threshold_results <- map_dfr(thresholds, function(thresh) {
    results <- investigateTarget(
        target = "TP53",
        inputLib = "KD",
        outputLib = "CP",
        filterThreshold = thresh,
        similarityThreshold = 0.2
    )

    tibble(
        threshold = thresh,
        n_results = nrow(results),
        n_strong = sum(abs(results$Similarity) > 0.4)
    )
})

print(threshold_results)

Multiple Target Analysis

Batch Processing Targets

# Analyze multiple related genes
cell_cycle_genes <- c("TP53", "RB1", "CDKN2A", "MYC", "E2F1")

cell_cycle_results <- map_dfr(cell_cycle_genes, function(gene) {
    tryCatch(
        {
            results <- investigateTarget(
                target = gene,
                inputLib = "KD",
                outputLib = "CP",
                filterThreshold = 0.5
            )

            results %>%
                mutate(SourceGene = gene) %>%
                filter(Similarity < -0.3) # Therapeutic candidates
        },
        error = function(e) {
            message("No results for ", gene)
            tibble()
        }
    )
})

# Find drugs targeting multiple cell cycle genes
multi_target_drugs <- cell_cycle_results %>%
    group_by(Target) %>%
    summarize(
        n_genes_affected = n_distinct(SourceGene),
        mean_similarity = mean(Similarity),
        .groups = "drop"
    ) %>%
    filter(n_genes_affected >= 3) %>%
    arrange(n_genes_affected, mean_similarity)

cat("Drugs affecting multiple cell cycle genes:\n")
head(multi_target_drugs, 10)

Pathway-Level Analysis

Analyzing Gene Sets

# DNA repair pathway genes
dna_repair_genes <- c("BRCA1", "BRCA2", "ATM", "ATR", "CHEK1", "CHEK2")

# Get concordant drugs for each gene
dna_repair_drugs <- map_dfr(dna_repair_genes, function(gene) {
    tryCatch(
        {
            investigateTarget(
                target = gene,
                inputLib = "KD",
                outputLib = "CP",
                filterThreshold = 0.5
            ) %>%
                mutate(PathwayGene = gene)
        },
        error = function(e) tibble()
    )
})

# Drugs consistently affecting the pathway
pathway_drugs <- dna_repair_drugs %>%
    group_by(Target) %>%
    summarize(
        genes_affected = n_distinct(PathwayGene),
        mean_effect = mean(Similarity),
        .groups = "drop"
    ) %>%
    filter(genes_affected >= 4) %>%
    arrange(desc(genes_affected))

cat("Drugs affecting DNA repair pathway:\n")
head(pathway_drugs, 10)

Visualization Strategies

Similarity Score Distribution

tp53_results <- investigateTarget(
    target = "TP53",
    inputLib = "KD",
    outputLib = "CP",
    filterThreshold = 0.5
)

ggplot(tp53_results, aes(x = Similarity)) +
    geom_histogram(bins = 50, fill = "steelblue", alpha = 0.7) +
    geom_vline(
        xintercept = c(-0.3, 0.3),
        linetype = "dashed",
        color = "red",
        size = 1
    ) +
    labs(
        title = "TP53 Knockdown: Drug Similarity Distribution",
        x = "Similarity Score",
        y = "Number of Drug Signatures",
        subtitle = "Negative = opposes TP53 loss; Positive = mimics TP53 loss"
    ) +
    theme_minimal()

Top Candidates Bar Plot

top_drugs <- tp53_results %>%
    arrange(Similarity) %>%
    head(20)

ggplot(top_drugs, aes(x = reorder(Target, Similarity), y = Similarity)) +
    geom_col(aes(fill = Similarity < 0), show.legend = FALSE) +
    scale_fill_manual(values = c("red3", "steelblue")) +
    coord_flip() +
    labs(
        title = "Top 20 Drugs Related to TP53 Knockdown",
        subtitle = "Blue = opposes TP53 loss (therapeutic potential)",
        x = "Drug/Compound",
        y = "Similarity Score"
    ) +
    theme_minimal() +
    theme(axis.text.y = element_text(size = 10))

Network Visualization Concept

# Prepare data for network visualization
# (requires igraph or visNetwork package)

# Get relationships between multiple genes and drugs
network_data <- map_dfr(c("TP53", "BRCA1", "EGFR"), function(gene) {
    investigateTarget(
        target = gene,
        inputLib = "KD",
        outputLib = "CP",
        filterThreshold = 0.5,
        similarityThreshold = 0.3
    ) %>%
        mutate(GeneSource = gene) %>%
        select(GeneSource, Target, Similarity) %>%
        filter(abs(Similarity) > 0.4)
})

# This data can be used with igraph to create gene-drug networks
head(network_data)

Heatmap of Gene-Drug Relationships

# Create a matrix of gene-drug similarities
genes_of_interest <- c("TP53", "BRCA1", "MYC", "EGFR", "KRAS")

heatmap_data <- map_dfr(genes_of_interest, function(gene) {
    investigateTarget(
        target = gene,
        inputLib = "KD",
        outputLib = "CP",
        filterThreshold = 0.5
    ) %>%
        mutate(Gene = gene) %>%
        group_by(Gene, Target) %>%
        summarize(Similarity = mean(Similarity), .groups = "drop")
})

# Filter to drugs affecting multiple genes
common_drugs <- heatmap_data %>%
    group_by(Target) %>%
    filter(n() >= 3) %>%
    ungroup()

ggplot(common_drugs, aes(x = Target, y = Gene, fill = Similarity)) +
    geom_tile(color = "white") +
    scale_fill_gradient2(
        low = "blue", mid = "white", high = "red",
        midpoint = 0,
        name = "Similarity"
    ) +
    labs(
        title = "Gene-Drug Relationship Heatmap",
        x = "Drug",
        y = "Gene"
    ) +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))

Advanced Use Cases

Case 1: Synthetic Lethality Discovery

Find drugs that are specifically toxic when a gene is lost:

# In BRCA1-deficient cells, PARP inhibitors are synthetically lethal
# Find similar relationships

brca1_synthetic <- investigateTarget(
    target = "BRCA1",
    inputLib = "KD",
    outputLib = "CP",
    filterThreshold = 0.6,
    paired = TRUE
)

# Drugs with strong positive similarity might indicate synergy
potential_synthetic <- brca1_synthetic %>%
    filter(Similarity > 0.4) %>%
    arrange(desc(Similarity))

cat("Potential synthetic lethal partners:\n")
head(potential_synthetic, 10)

Case 2: Rescue vs. Enhancement

results <- investigateTarget(
    target = "TP53",
    inputLib = "KD",
    outputLib = "CP",
    filterThreshold = 0.5
)

# Classify drugs
drug_classification <- results %>%
    mutate(
        Classification = case_when(
            Similarity < -0.4 ~ "Strong Rescue",
            Similarity < -0.2 ~ "Moderate Rescue",
            Similarity > 0.4 ~ "Strong Enhancement",
            Similarity > 0.2 ~ "Moderate Enhancement",
            TRUE ~ "Neutral"
        )
    )

table(drug_classification$Classification)

Case 3: Temporal Analysis

# Analyze time-dependent effects
results <- investigateTarget(
    target = "MYC",
    inputLib = "OE",
    outputLib = "CP",
    filterThreshold = 0.5
)

# Group by time point
temporal_effects <- results %>%
    group_by(TargetTime) %>%
    summarize(
        n_drugs = n_distinct(Target),
        mean_similarity = mean(Similarity),
        .groups = "drop"
    ) %>%
    arrange(TargetTime)

cat("Time-dependent drug effects:\n")
print(temporal_effects)

Case 4: Dose-Response Patterns

# Examine concentration-dependent effects
drug_doses <- results %>%
    filter(!is.na(TargetConcentration)) %>%
    group_by(Target, TargetConcentration) %>%
    summarize(
        mean_similarity = mean(Similarity),
        .groups = "drop"
    ) %>%
    group_by(Target) %>%
    filter(n() >= 2) %>% # Multiple concentrations tested
    ungroup()

# Plot dose-response for top drugs
top_drugs_dose <- drug_doses %>%
    group_by(Target) %>%
    slice_head(n = 5) %>%
    ungroup()

ggplot(top_drugs_dose, aes(x = TargetConcentration, y = mean_similarity)) +
    geom_line(aes(color = Target)) +
    geom_point(aes(color = Target), size = 2) +
    labs(
        title = "Dose-Response Patterns",
        x = "Concentration",
        y = "Mean Similarity"
    ) +
    theme_minimal()

Integration with Experimental Data

Validating Predictions

# After getting computational predictions
predictions <- investigateTarget(
    target = "TP53",
    inputLib = "KD",
    outputLib = "CP",
    filterThreshold = 0.5
)

# Select top candidates for validation
validation_candidates <- predictions %>%
    filter(Similarity < -0.4, pValue < 0.05) %>%
    arrange(Similarity) %>%
    head(10)

# Export for experimental testing
# write.csv(validation_candidates, "tp53_rescue_candidates.csv")

Comparing with Literature

# Known TP53-interacting drugs from literature
literature_drugs <- c("nutlin-3", "PRIMA-1", "APR-246")

# Check if they appear in predictions
literature_hits <- predictions %>%
    filter(grepl(paste(literature_drugs, collapse = "|"),
        Target,
        ignore.case = TRUE
    ))

cat("Literature-validated drugs in predictions:\n")
print(literature_hits)

Best Practices

1. Start Broad, Then Narrow

# Step 1: Broad discovery
broad_search <- investigateTarget(
    target = "EGFR",
    inputLib = "KD",
    outputLib = "CP",
    filterThreshold = 0.5,
    similarityThreshold = 0.2
)

# Step 2: Focus on high-confidence
high_confidence <- broad_search %>%
    filter(abs(Similarity) > 0.4, pValue < 0.01)

# Step 3: Cell line-specific validation
validated <- investigateTarget(
    target = "EGFR",
    inputLib = "KD",
    outputLib = "CP",
    outputCellLines = c("A549", "MCF7"),
    filterThreshold = 0.6,
    similarityThreshold = 0.3
)

2. Consider Biological Context

Cell type: Use appropriate cell line filters
Perturbation type: KD vs OE have different implications
Magnitude: Balance stringency with coverage
Direction: Paired analysis for complex effects

3. Multiple Evidence Lines

# Evidence 1: KD → CP
evidence_kd <- investigateTarget(
    target = "BRCA1", inputLib = "KD", outputLib = "CP",
    filterThreshold = 0.5
)

# Evidence 2: OE → CP
evidence_oe <- investigateTarget(
    target = "BRCA1", inputLib = "OE", outputLib = "CP",
    filterThreshold = 0.5
)

# Find drugs appearing in both analyses
drugs_kd <- evidence_kd %>%
    filter(Similarity < -0.3) %>%
    pull(Target)
drugs_oe <- evidence_oe %>%
    filter(Similarity < -0.3) %>%
    pull(Target)

robust_candidates <- intersect(drugs_kd, drugs_oe)
cat("Drugs validated across KD and OE:", length(robust_candidates), "\n")

4. Document Parameters

Always record your analysis parameters:

analysis_params <- list(
    target = "TP53",
    input_library = "KD",
    output_library = "CP",
    filter_threshold = 0.5,
    similarity_threshold = 0.3,
    paired = TRUE,
    date = Sys.Date(),
    drugfindR_version = packageVersion("drugfindR")
)

# Save with results
# saveRDS(list(results = tp53_results, params = analysis_params),
#         "tp53_analysis.rds")

Troubleshooting

No Signatures Found

# If no signatures found for your target:

# 1. Check spelling and case
# Note: drugfindR handles case-insensitivity, but verify gene name

# 2. Try alternate names
# Some genes have aliases (e.g., ERBB2 vs HER2)

# 3. Check metadata
kd_metadata <- drugfindR:::.loadMetadata("KD")
available_genes <- unique(kd_metadata$Source)

# Search for your gene
"TP53" %in% available_genes
grep("TP53", available_genes, value = TRUE, ignore.case = TRUE)

Too Few Results

# If results are sparse:

# 1. Lower thresholds
relaxed <- investigateTarget(
    target = "TP53",
    inputLib = "KD",
    outputLib = "CP",
    filterThreshold = 0.3, # More permissive
    similarityThreshold = 0.1
)

# 2. Try different libraries
# KD → KD instead of KD → CP
alternate <- investigateTarget(
    target = "TP53",
    inputLib = "KD",
    outputLib = "KD",
    filterThreshold = 0.5
)

# 3. Remove cell line restrictions
unrestricted <- investigateTarget(
    target = "TP53",
    inputLib = "KD",
    outputLib = "CP",
    inputCellLines = NULL, # All cell lines
    outputCellLines = NULL,
    filterThreshold = 0.5
)

Summary

Key takeaways for target investigation:

Choose right libraries: KD/OE for genes, CP for drugs
Direction matters: Positive = similar, negative = opposite
Use paired analysis: For nuanced biological interpretation
Consider cell context: Filter by relevant cell lines
Validate predictions: Multiple evidence lines + experiments
Document thoroughly: Record all parameters

Next Steps

Drug Repurposing: Apply to disease signatures
Getting Started: Quick reference guide
Function Reference: Complete API

Session Information

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04 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-r0.3.32.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] lubridate_1.9.5  forcats_1.0.1    stringr_1.6.0    purrr_1.2.2     
#>  [5] tibble_3.3.1     tidyverse_2.0.0  tidyr_1.3.2      ggplot2_4.0.3   
#>  [9] readr_2.2.0      dplyr_1.2.1      drugfindR_1.0.0  BiocStyle_2.41.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] gtable_0.3.6         xfun_0.60            bslib_0.11.0        
#>  [4] httr2_1.2.3          devtools_2.5.2       tzdb_0.5.0          
#>  [7] vctrs_0.7.3          tools_4.6.1          generics_0.1.4      
#> [10] stats4_4.6.1         curl_7.1.0           parallel_4.6.1      
#> [13] pkgconfig_2.0.3      RColorBrewer_1.1-3   S7_0.2.2            
#> [16] S4Vectors_0.51.5     lifecycle_1.0.5      compiler_4.6.1      
#> [19] farver_2.1.2         usethis_3.2.1        htmltools_0.5.9     
#> [22] sys_3.4.3            buildtools_1.0.0     sass_0.4.10         
#> [25] yaml_2.3.12          pillar_1.11.1        crayon_1.5.3        
#> [28] jquerylib_0.1.4      ellipsis_0.3.3       cachem_1.1.0        
#> [31] sessioninfo_1.2.4    tidyselect_1.2.1     digest_0.6.39       
#> [34] stringi_1.8.7        maketools_1.3.2      labeling_0.4.3      
#> [37] fastmap_1.2.0        grid_4.6.1           cli_3.6.6           
#> [40] magrittr_2.0.5       pkgbuild_1.4.8       utf8_1.2.6          
#> [43] withr_3.0.3          DFplyr_1.7.0         scales_1.4.0        
#> [46] rappdirs_0.3.4       bit64_4.8.2          timechange_0.4.0    
#> [49] rmarkdown_2.31       bit_4.6.0            otel_0.2.0          
#> [52] hms_1.1.4            memoise_2.0.1        evaluate_1.0.5      
#> [55] knitr_1.51           rlang_1.3.0          glue_1.8.1          
#> [58] BiocManager_1.30.27  BiocGenerics_0.59.10 pkgload_1.5.3       
#> [61] vroom_1.7.1          jsonlite_2.0.0       R6_2.6.1            
#> [64] fs_2.1.0

References

Subramanian A, et al. (2017). A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell, 171(6):1437-1452.
Lamb J, et al. (2006). The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science, 313(5795):1929-1935.
Corsello SM, et al. (2020). Discovering the anticancer potential of non-oncology drugs by systematic viability profiling. Nature Cancer, 1(2):235-248.

Target Investigation and Functional Genomics