Generate a Consensus list of Targets
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Description
This function takes a list of (optionally split) concordance dataframes and returns a ranked list of gene or drug targets that have been chose for their maximal similarity to the signature
Usage
consensusConcordants(..., paired = FALSE, cutoff = 0.321, cellLine = NULL)consensusConcordants(..., paired = FALSE, cutoff = 0.321, cellLine = NULL)
Arguments
... |
One or Two (see paired) Data Frames with the concordants |
paired |
Logical indicating whether you split the dataframes by up and down regulated in prior analysis |
cutoff |
A similarity cutoff value. Defaults to 0.321 |
cellLine |
A character vector of Cell Lines you are interested in. |
Value
A tibble with the filtered and deduplicated results
Examples
# Create mock concordants data for demonstration mockConcordants <- data.frame( signatureid = paste0("SIG", 1:10), treatment = c( "TP53", "TP53", "MYC", "MYC", "EGFR", "EGFR", "KRAS", "BRCA1", "BRCA1", "PIK3CA" ), cellline = c( "A375", "PC3", "A375", "MCF7", "A375", "PC3", "A375", "A375", "MCF7", "A375" ), time = rep("24H", 10), concentration = rep(NA, 10), sig_direction = rep("DOWN", 10), sig_type = rep("single", 10), similarity = c( 0.85, 0.72, -0.68, -0.45, 0.55, 0.38, 0.42, 0.51, 0.33, 0.29 ), pValue = rep(0.001, 10) ) # Example 1: Basic consensus with default cutoff consensus <- consensusConcordants(mockConcordants) nrow(consensus) # Targets with |similarity| >= 0.321 # Example 2: Consensus with higher cutoff consensus_strict <- consensusConcordants(mockConcordants, cutoff = 0.5) nrow(consensus_strict) # Fewer targets with higher threshold # Example 3: Filter by cell line consensus_A375 <- consensusConcordants(mockConcordants, cellLine = "A375") unique(consensus_A375$CellLine) # Only A375 # Network-dependent examples using real iLINCS data # Get the L1000 signature for LINCSKD_28 kdSignature <- getSignature("LINCSKD_28") # Get concordant gene knockdown signatures concordantSignatures <- getConcordants(kdSignature, ilincsLibrary = "KD") # Get the consensus list with different parameters consensus <- consensusConcordants(concordantSignatures, cutoff = 0.5) # Paired analysis example filteredUp <- filterSignature(kdSignature, direction = "up", threshold = 0.5) filteredDown <- filterSignature(kdSignature, direction = "down", threshold = -0.5) concordants_up <- getConcordants(filteredUp, ilincsLibrary = "KD") concordants_down <- getConcordants(filteredDown, ilincsLibrary = "KD") consensus <- consensusConcordants(concordants_up, concordants_down, paired = TRUE)# Create mock concordants data for demonstration mockConcordants <- data.frame( signatureid = paste0("SIG", 1:10), treatment = c( "TP53", "TP53", "MYC", "MYC", "EGFR", "EGFR", "KRAS", "BRCA1", "BRCA1", "PIK3CA" ), cellline = c( "A375", "PC3", "A375", "MCF7", "A375", "PC3", "A375", "A375", "MCF7", "A375" ), time = rep("24H", 10), concentration = rep(NA, 10), sig_direction = rep("DOWN", 10), sig_type = rep("single", 10), similarity = c( 0.85, 0.72, -0.68, -0.45, 0.55, 0.38, 0.42, 0.51, 0.33, 0.29 ), pValue = rep(0.001, 10) ) # Example 1: Basic consensus with default cutoff consensus <- consensusConcordants(mockConcordants) nrow(consensus) # Targets with |similarity| >= 0.321 # Example 2: Consensus with higher cutoff consensus_strict <- consensusConcordants(mockConcordants, cutoff = 0.5) nrow(consensus_strict) # Fewer targets with higher threshold # Example 3: Filter by cell line consensus_A375 <- consensusConcordants(mockConcordants, cellLine = "A375") unique(consensus_A375$CellLine) # Only A375 # Network-dependent examples using real iLINCS data # Get the L1000 signature for LINCSKD_28 kdSignature <- getSignature("LINCSKD_28") # Get concordant gene knockdown signatures concordantSignatures <- getConcordants(kdSignature, ilincsLibrary = "KD") # Get the consensus list with different parameters consensus <- consensusConcordants(concordantSignatures, cutoff = 0.5) # Paired analysis example filteredUp <- filterSignature(kdSignature, direction = "up", threshold = 0.5) filteredDown <- filterSignature(kdSignature, direction = "down", threshold = -0.5) concordants_up <- getConcordants(filteredUp, ilincsLibrary = "KD") concordants_down <- getConcordants(filteredDown, ilincsLibrary = "KD") consensus <- consensusConcordants(concordants_up, concordants_down, paired = TRUE)
Filter the L1000 Signature
Description
This function filters the L1000 signature to a given threshold, identifying up-regulated, down-regulated, or both up- and down-regulated genes. The function supports both absolute threshold filtering and proportional filtering based on quantiles of the expression data.
Usage
filterSignature(signature, direction = "any", threshold = NULL, prop = NULL)filterSignature(signature, direction = "any", threshold = NULL, prop = NULL)
Arguments
signature |
A data.frame, tibble, or DataFrame containing the L1000 signature. Must contain a column named "Value_LogDiffExp" with log fold-change values. |
direction |
Character string specifying the direction to filter. Must be one of "up" (up-regulated genes only), "down" (down-regulated genes only), or "any" (both up- and down-regulated genes). Defaults to "any". |
threshold |
Numeric value or vector specifying the log fold-change threshold(s).
Can be:
* A single positive value: Creates symmetric thresholds ( |
prop |
Numeric value between 0 and 1 specifying the proportion of genes
to select from the top and bottom of the expression distribution. For example,
|
Details
The filtering process follows these steps:
-
Input validation: Checks data frame structure and parameter consistency
-
Threshold calculation: Computes filtering thresholds based on either absolute values (
threshold) or quantiles (prop) -
Direction-based filtering: Applies the computed thresholds according to the specified direction
When using threshold:
-
Single value: Genes with |logFC| >= threshold are retained
-
Two values: Genes with logFC <=
threshold[1]OR logFC >=threshold[2]
When using prop:
-
Thresholds are calculated as quantiles of the expression distribution
-
Down threshold = quantile(logFC, prop)
-
Up threshold = quantile(logFC, 1 - prop)
Value
A tibble containing the filtered L1000 signature with the same structure as the input but containing only genes that meet the filtering criteria.
See Also
\link{getSignature} for retrieving L1000 signatures from iLINCS,
\link{prepareSignature} for preparing custom signatures,
\link{getConcordants} for finding concordant signatures
Examples
# Create a mock signature for demonstration mockSignature <- data.frame( signatureID = rep("MOCK001", 20), Name_GeneSymbol = paste0("GENE", 1:20), ID_geneid = 1:20, Value_LogDiffExp = c( -3.5, -2.8, -2.1, -1.5, -1.2, -0.8, -0.5, -0.3, -0.1, 0.1, 0.3, 0.6, 0.9, 1.2, 1.6, 2.0, 2.4, 2.9, 3.3, 3.8 ) ) # Example 1: Filter by symmetric absolute threshold # Keeps genes with |logFC| >= 1.5 filteredSymmetric <- filterSignature(mockSignature, threshold = 1.5) nrow(filteredSymmetric) # Should return 8 genes # Example 2: Filter by asymmetric absolute thresholds # Keeps genes with logFC <= -2.0 OR logFC >= 2.5 filteredAsymmetric <- filterSignature(mockSignature, threshold = c(-2.0, 2.5)) nrow(filteredAsymmetric) # Should return 5 genes # Example 3: Filter by proportion (top and bottom 20%) filteredProportion <- filterSignature(mockSignature, prop = 0.2) nrow(filteredProportion) # Should return 8 genes (4 up + 4 down) # Example 4: Filter only up-regulated genes by threshold upRegulated <- filterSignature(mockSignature, direction = "up", threshold = 1.0) all(upRegulated$Value_LogDiffExp >= 1.0) # Should be TRUE # Example 5: Filter only down-regulated genes by threshold downRegulated <- filterSignature(mockSignature, direction = "down", threshold = 1.0) all(downRegulated$Value_LogDiffExp <= -1.0) # Should be TRUE # Network-dependent examples using real iLINCS data # Get the L1000 signature for LINCSKD_28 kdSignature <- getSignature("LINCSKD_28") # Filter for top 5% most extreme genes topExtreme <- filterSignature(kdSignature, prop = 0.05) # Get top 20% most up-regulated genes topUpregulated <- filterSignature(kdSignature, direction = "up", prop = 0.2)# Create a mock signature for demonstration mockSignature <- data.frame( signatureID = rep("MOCK001", 20), Name_GeneSymbol = paste0("GENE", 1:20), ID_geneid = 1:20, Value_LogDiffExp = c( -3.5, -2.8, -2.1, -1.5, -1.2, -0.8, -0.5, -0.3, -0.1, 0.1, 0.3, 0.6, 0.9, 1.2, 1.6, 2.0, 2.4, 2.9, 3.3, 3.8 ) ) # Example 1: Filter by symmetric absolute threshold # Keeps genes with |logFC| >= 1.5 filteredSymmetric <- filterSignature(mockSignature, threshold = 1.5) nrow(filteredSymmetric) # Should return 8 genes # Example 2: Filter by asymmetric absolute thresholds # Keeps genes with logFC <= -2.0 OR logFC >= 2.5 filteredAsymmetric <- filterSignature(mockSignature, threshold = c(-2.0, 2.5)) nrow(filteredAsymmetric) # Should return 5 genes # Example 3: Filter by proportion (top and bottom 20%) filteredProportion <- filterSignature(mockSignature, prop = 0.2) nrow(filteredProportion) # Should return 8 genes (4 up + 4 down) # Example 4: Filter only up-regulated genes by threshold upRegulated <- filterSignature(mockSignature, direction = "up", threshold = 1.0) all(upRegulated$Value_LogDiffExp >= 1.0) # Should be TRUE # Example 5: Filter only down-regulated genes by threshold downRegulated <- filterSignature(mockSignature, direction = "down", threshold = 1.0) all(downRegulated$Value_LogDiffExp <= -1.0) # Should be TRUE # Network-dependent examples using real iLINCS data # Get the L1000 signature for LINCSKD_28 kdSignature <- getSignature("LINCSKD_28") # Filter for top 5% most extreme genes topExtreme <- filterSignature(kdSignature, prop = 0.05) # Get top 20% most up-regulated genes topUpregulated <- filterSignature(kdSignature, direction = "up", prop = 0.2)
Get concordant signatures from iLINCS database
Description
This function queries the iLINCS (Integrative Library of Integrated Network-based Cellular Signatures) database to find signatures that are concordant (similar) to a given input signature.
Usage
getConcordants(signature, ilincsLibrary = "CP")getConcordants(signature, ilincsLibrary = "CP")
Arguments
signature |
A data.frame, tibble, or S4Vectors::DataFrame containing
the signature data. Must conform to iLINCS signature structure with columns:
* Use |
ilincsLibrary |
Character string specifying the iLINCS library to search.
Must be one of:
* |
Details
The function performs the following steps:
-
Validates input parameters
-
Creates a temporary file with signature data
-
Detects signature direction from expression values
-
Sends a multipart POST request to the iLINCS API
-
Processes the JSON response into a standardized tibble
-
Cleans up temporary files
The signature direction is determined as follows:
-
"Up": All expression values are greater than or equal to zero -
"Down": All expression values are less than or equal to zero -
"Any": Mixed positive and negative values
Value
A data structure containing concordant signatures. The return type matches the input signature type: * tibble for data.frame or tibble inputs * S4Vectors::DataFrame for DataFrame inputs
Contains the following columns:
* signatureid: Unique signature identifier
* compound or treatment: Drug/treatment name
* concentration: Drug concentration (CP library only)
* time: Treatment duration
* cellline: Cell line used
* similarity: Similarity score (rounded to 8 decimal places)
* pValue: Statistical significance (rounded to 20 decimal places)
* sig_direction: Signature direction ("Up", "Down", or "Any")
API Details
This function interfaces with the iLINCS web service API. The signature is uploaded as a tab-separated file and analyzed against the specified library. Results are returned as JSON and parsed into a tibble.
Error Handling
The function will stop execution with informative error messages for:
-
Invalid signature data types (must be data.frame, tibble, or DataFrame)
-
Invalid signature structure (missing required columns, wrong order, etc.)
-
Missing values in signature data
-
Unsupported iLINCS library names
-
HTTP errors from the iLINCS API
-
Invalid or empty API responses
References
iLINCS Portal: http://www.ilincs.org/
Pilarczyk et al. (2020). Connecting omics signatures and revealing biological mechanisms with iLINCS. Nature Communications, 11(1), 4058.
See Also
[ prepareSignature() ] for signature preparation,
[ filterSignature() ] for signature filtering,
[ investigateSignature() ] for signature investigation
Examples
# Input validation examples (no API calls) # These demonstrate proper signature structure mockSig <- data.frame( signatureID = rep("TEST", 3), ID_geneid = c("123", "456", "789"), Name_GeneSymbol = c("TP53", "MYC", "EGFR"), Value_LogDiffExp = c(1.5, -2.0, 0.8) ) # Validate library parameter (should produce error) tryCatch( getConcordants(mockSig, ilincsLibrary = "INVALID"), error = function(e) message("Expected error: invalid library") ) # This example requires network access to the iLINCS API # Load example differential expression data dge_file <- system.file("extdata", "dCovid_diffexp.tsv", package = "drugfindR" ) dge_data <- read.delim(dge_file) # Prepare signature to ensure proper structure signature <- prepareSignature( dge_data[1:50, ], geneColumn = "hgnc_symbol", logfcColumn = "logFC", pvalColumn = "PValue" ) # Find concordant chemical perturbagens cpConcordants <- getConcordants(signature, ilincsLibrary = "CP") head(cpConcordants) # Find concordant knockdown signatures kdConcordants <- getConcordants(signature, ilincsLibrary = "KD") head(kdConcordants) # Find concordant overexpression signatures oeConcordants <- getConcordants(signature, ilincsLibrary = "OE") head(oeConcordants) # Works with different data frame types signatureDf <- as.data.frame(signature) cpConcordantsDf <- getConcordants(signatureDf, "CP") # Works with S4Vectors::DataFrame signatureDataFrame <- S4Vectors::DataFrame(signature) cpConcordantsDataFrame <- getConcordants(signatureDataFrame, "CP") # Returns S4Vectors::DataFrame to match input type# Input validation examples (no API calls) # These demonstrate proper signature structure mockSig <- data.frame( signatureID = rep("TEST", 3), ID_geneid = c("123", "456", "789"), Name_GeneSymbol = c("TP53", "MYC", "EGFR"), Value_LogDiffExp = c(1.5, -2.0, 0.8) ) # Validate library parameter (should produce error) tryCatch( getConcordants(mockSig, ilincsLibrary = "INVALID"), error = function(e) message("Expected error: invalid library") ) # This example requires network access to the iLINCS API # Load example differential expression data dge_file <- system.file("extdata", "dCovid_diffexp.tsv", package = "drugfindR" ) dge_data <- read.delim(dge_file) # Prepare signature to ensure proper structure signature <- prepareSignature( dge_data[1:50, ], geneColumn = "hgnc_symbol", logfcColumn = "logFC", pvalColumn = "PValue" ) # Find concordant chemical perturbagens cpConcordants <- getConcordants(signature, ilincsLibrary = "CP") head(cpConcordants) # Find concordant knockdown signatures kdConcordants <- getConcordants(signature, ilincsLibrary = "KD") head(kdConcordants) # Find concordant overexpression signatures oeConcordants <- getConcordants(signature, ilincsLibrary = "OE") head(oeConcordants) # Works with different data frame types signatureDf <- as.data.frame(signature) cpConcordantsDf <- getConcordants(signatureDf, "CP") # Works with S4Vectors::DataFrame signatureDataFrame <- S4Vectors::DataFrame(signature) cpConcordantsDataFrame <- getConcordants(signatureDataFrame, "CP") # Returns S4Vectors::DataFrame to match input type
Get the L1000 Signature from iLINCS
Description
This function acts as the entrypoint to the iLINCS database. This takes in an ID and returns the signature after making a call to the iLINCS database. The function automatically detects whether the signature is an L1000 signature based on the signature ID and metadata tables, and retrieves all available genes for comprehensive signature analysis.
Usage
getSignature(sigId)getSignature(sigId)
Arguments
sigId |
character. The ilincs signature_id |
Value
a tibble with the signature data containing the following columns:
* signatureID: The signature identifier
* ID_geneid: Gene IDs (Entrez)
* Name_GeneSymbol: Gene symbols
* Value_LogDiffExp: Log fold-change values
* Significance_pvalue: Statistical significance p-values
Examples
# Input validation example (no API call) # Demonstrates proper signature ID format validation tryCatch( getSignature(""), # Empty string should error error = function(e) message("Expected error: empty signature ID") ) # These examples require network access to the iLINCS API # Get the L1000 signature for LINCSKD_28 kdSignature <- getSignature("LINCSKD_28") head(kdSignature) # Get an overexpression signature (L1000 status is automatically detected) oeSignature <- getSignature("LINCSOE_1000") head(oeSignature) # Check the structure of retrieved signature str(kdSignature)# Input validation example (no API call) # Demonstrates proper signature ID format validation tryCatch( getSignature(""), # Empty string should error error = function(e) message("Expected error: empty signature ID") ) # These examples require network access to the iLINCS API # Get the L1000 signature for LINCSKD_28 kdSignature <- getSignature("LINCSKD_28") head(kdSignature) # Get an overexpression signature (L1000 status is automatically detected) oeSignature <- getSignature("LINCSOE_1000") head(oeSignature) # Check the structure of retrieved signature str(kdSignature)
Investigate a given DGE dataset
Description
This function takes a DGE Data frame and then finds concordant signatures to that. This generates an L1000 signature from the DGE dataset and then uploads that signature to iLINCS to find the relevant concordant (or discordant) signatures
Usage
investigateSignature( expr, outputLib, filterThreshold = NULL, filterProp = NULL, similarityThreshold = 0.2, paired = TRUE, outputCellLines = NULL, geneColumn = "Symbol", logfcColumn = "logFC", pvalColumn = "PValue", sourceName = "Input", sourceCellLine = NA, sourceTime = NA, sourceConcentration = NA )investigateSignature( expr, outputLib, filterThreshold = NULL, filterProp = NULL, similarityThreshold = 0.2, paired = TRUE, outputCellLines = NULL, geneColumn = "Symbol", logfcColumn = "logFC", pvalColumn = "PValue", sourceName = "Input", sourceCellLine = NA, sourceTime = NA, sourceConcentration = NA )
Arguments
expr |
A dataframe that has differential gene expression analysis |
outputLib |
The library to search |
filterThreshold |
The Filtering threshold. |
filterProp |
The Filtering proportion. |
similarityThreshold |
The Similarity Threshold |
paired |
Logical. Whether to query iLINCS separately for up and down regulated genes |
outputCellLines |
A character vector of cell lines to restrict the output search to. |
geneColumn |
The name of the column that has gene symbols |
logfcColumn |
The name of the column that has log_2 fold-change values |
pvalColumn |
The name of the column that has p-values |
sourceName |
(Optional) An annotation column to identify the signature by name |
sourceCellLine |
(Optional) An annotation column to specify the cell line for the input data |
sourceTime |
(Optional) An annotation column to specify the time for the input data |
sourceConcentration |
(Optional) An annotation column to specify the concentration for the input data |
Value
A tibble with the the similarity scores and signature metadata
Examples
# Input validation example (no API calls) mockExpr <- data.frame( Symbol = c("TP53", "MYC"), logFC = c(2.5, -1.8), PValue = c(0.001, 0.01) ) # Validate library parameter (should produce error) tryCatch( investigateSignature(mockExpr, outputLib = "INVALID"), error = function(e) message("Expected error: invalid library") ) # This function makes multiple API calls to iLINCS and may take several minutes # Load differential expression data inputSignature <- read.table( system.file("extdata", "dCovid_diffexp.tsv", package = "drugfindR"), header = TRUE ) # Investigate the signature against chemical perturbagen library investigatedSignature <- investigateSignature( inputSignature, outputLib = "CP", filterThreshold = 0.5, geneColumn = "hgnc_symbol", logfcColumn = "logFC", pvalColumn = "PValue" ) head(investigatedSignature)# Input validation example (no API calls) mockExpr <- data.frame( Symbol = c("TP53", "MYC"), logFC = c(2.5, -1.8), PValue = c(0.001, 0.01) ) # Validate library parameter (should produce error) tryCatch( investigateSignature(mockExpr, outputLib = "INVALID"), error = function(e) message("Expected error: invalid library") ) # This function makes multiple API calls to iLINCS and may take several minutes # Load differential expression data inputSignature <- read.table( system.file("extdata", "dCovid_diffexp.tsv", package = "drugfindR"), header = TRUE ) # Investigate the signature against chemical perturbagen library investigatedSignature <- investigateSignature( inputSignature, outputLib = "CP", filterThreshold = 0.5, geneColumn = "hgnc_symbol", logfcColumn = "logFC", pvalColumn = "PValue" ) head(investigatedSignature)
Investigate concordant signatures for a gene or drug
Description
Given the name of a target (gene knockdown/overexpression or compound) this high–level convenience wrapper:
Usage
investigateTarget( target, inputLib, outputLib, filterThreshold = 0.85, similarityThreshold = 0.321, paired = TRUE, inputCellLines = NULL, outputCellLines = NULL )investigateTarget( target, inputLib, outputLib, filterThreshold = 0.85, similarityThreshold = 0.321, paired = TRUE, inputCellLines = NULL, outputCellLines = NULL )
Arguments
target |
Character scalar. Gene symbol (for KD/OE libraries), or drug / compound name (for CP library) used to locate source signatures. |
inputLib |
Character ("OE", "KD", or "CP"). Library from which source
signatures for |
outputLib |
Character ("OE", "KD", or "CP"). Library queried for concordant signatures. |
filterThreshold |
Numeric in \(0,1]. Minimum absolute (or directional)
change used to retain genes in each source signature prior to concordance.
Default |
similarityThreshold |
Numeric in [0,1]. Minimum similarity score retained
in the final consensus result set. Default |
paired |
Logical. If |
inputCellLines |
Optional character vector restricting the search for
source signatures to specified cell line(s). If |
outputCellLines |
Optional character vector restricting target signatures
(during consensus formation) to specified cell line(s). If |
Details
-
Locates iLINCS source signatures for the target in the specified input library.
-
Optionally filters by source cell line(s).
-
Retrieves each source signature and filters genes by direction and magnitude.
-
Queries iLINCS for concordant signatures in the chosen output library.
-
Computes paired or unpaired consensus concordance across up/down regulated sets.
-
Returns an augmented tibble of similarity scores and rich source/target metadata.
The paired workflow evaluates concordance separately for up‑ and down‑regulated
genes and then combines (via consensusConcordants()) the two result sets. When
paired = FALSE a single aggregate signature (direction = "any") is used.
Network access: This function performs remote API calls (unless tests are run
under a mocking context such as httptest2::with_mock_api()). Examples are
wrapped in \donttest{} to avoid false negatives on CRAN / Bioconductor builders
without network access.
Errors are raised if:
-
No source signatures match
targetin the requestedinputLib(empty set). -
Invalid library codes are supplied.
Internally this function orchestrates: getSignature(), filterSignature(),
getConcordants() and consensusConcordants(). It returns a vertically
concatenated result across all matching source signatures.
Value
A tibble (data frame) with one row per consensus concordant target signature. Typical columns include:
-
Source/Target– gene or compound names. -
Similarity– numeric concordance score in [-1,1]. -
SourceSignature,TargetSignature– iLINCS signature identifiers. -
SourceCellLine,TargetCellLine– originating cell lines (if applicable). -
SourceConcentration,TargetConcentration– dosing information for CP. -
SourceTime,TargetTime– time point metadata.
Thresholds
-
filterThresholdcontrols gene selection within each source signature. It is passed tofilterSignature()as the absolute (or directional) threshold. -
similarityThresholdis applied when forming the consensus concordants to discard low similarity entries.
See Also
getSignature(), filterSignature(), getConcordants(),
consensusConcordants(), prepareSignature() for lower‑level operations.
Examples
# Input validation examples (no API calls) # Demonstrate library parameter validation tryCatch( investigateTarget(target = "TP53", inputLib = "INVALID", outputLib = "CP"), error = function(e) message("Expected error: invalid inputLib") ) tryCatch( investigateTarget(target = "TP53", inputLib = "KD", outputLib = "INVALID"), error = function(e) message("Expected error: invalid outputLib") ) # This function makes multiple API calls to iLINCS and may take several minutes # Basic paired investigation of a knockdown signature against compound library set.seed(1) res <- investigateTarget( target = "AATK", inputLib = "KD", outputLib = "CP", filterThreshold = 0.5, similarityThreshold = 0.3, paired = TRUE ) head(res) # Unpaired (aggregate) workflow — often faster, returns a single consensus set res_unpaired <- investigateTarget( target = "AATK", inputLib = "KD", outputLib = "CP", filterThreshold = 0.5, similarityThreshold = 0.3, paired = FALSE ) head(res_unpaired) # Restrict source signatures to specific cell lines (if available) # and target signatures to a subset of cell lines during consensus res_filtered <- investigateTarget( target = "AATK", inputLib = "KD", outputLib = "CP", outputCellLines = c("MCF7"), filterThreshold = 0.5, similarityThreshold = 0.3 ) head(res_filtered) # Using httptest2 (if installed) to mock network calls: # httptest2::with_mock_api({ # mock_res <- investigateTarget("AATK", "KD", "CP", filterThreshold = 0.5) # print(head(mock_res)) # })# Input validation examples (no API calls) # Demonstrate library parameter validation tryCatch( investigateTarget(target = "TP53", inputLib = "INVALID", outputLib = "CP"), error = function(e) message("Expected error: invalid inputLib") ) tryCatch( investigateTarget(target = "TP53", inputLib = "KD", outputLib = "INVALID"), error = function(e) message("Expected error: invalid outputLib") ) # This function makes multiple API calls to iLINCS and may take several minutes # Basic paired investigation of a knockdown signature against compound library set.seed(1) res <- investigateTarget( target = "AATK", inputLib = "KD", outputLib = "CP", filterThreshold = 0.5, similarityThreshold = 0.3, paired = TRUE ) head(res) # Unpaired (aggregate) workflow — often faster, returns a single consensus set res_unpaired <- investigateTarget( target = "AATK", inputLib = "KD", outputLib = "CP", filterThreshold = 0.5, similarityThreshold = 0.3, paired = FALSE ) head(res_unpaired) # Restrict source signatures to specific cell lines (if available) # and target signatures to a subset of cell lines during consensus res_filtered <- investigateTarget( target = "AATK", inputLib = "KD", outputLib = "CP", outputCellLines = c("MCF7"), filterThreshold = 0.5, similarityThreshold = 0.3 ) head(res_filtered) # Using httptest2 (if installed) to mock network calls: # httptest2::with_mock_api({ # mock_res <- investigateTarget("AATK", "KD", "CP", filterThreshold = 0.5) # print(head(mock_res)) # })
Prepare an L1000 Signature from a given differential gene expression output
Description
This function takes a differential gene expression output from any pipeline like edgeR or DeSeq2 or any that give you the gene symbol, log_2 fold-change and p-value and transforms that into an L1000 signature for later processing.
Usage
prepareSignature( dge, geneColumn = "Symbol", logfcColumn = "logFC", pvalColumn = "PValue" )prepareSignature( dge, geneColumn = "Symbol", logfcColumn = "logFC", pvalColumn = "PValue" )
Arguments
dge |
A dataframe-like object that has the differential gene expression information |
geneColumn |
The name of the column that has gene symbols |
logfcColumn |
The name of the column that has log_2 fold-change values |
pvalColumn |
The name of the column that has p-values |
Value
A tibble with the L1000 signature.
Examples
# Load example differential expression data from package dge_file <- system.file("extdata", "dCovid_diffexp.tsv", package = "drugfindR" ) dge_data <- read.delim(dge_file) # Prepare signature with p-values (standard workflow) signature <- prepareSignature( dge_data, geneColumn = "hgnc_symbol", logfcColumn = "logFC", pvalColumn = "PValue" ) head(signature) # Prepare signature without p-values signature_no_pval <- prepareSignature( dge_data, geneColumn = "hgnc_symbol", logfcColumn = "logFC", pvalColumn = NA ) head(signature_no_pval) # Custom column names example custom_dge <- data.frame( Gene = c("TP53", "MYC", "BRCA1", "EGFR"), FC = c(2.5, -1.8, 3.2, -2.1), Pval = c(0.001, 0.01, 0.0001, 0.005) ) custom_signature <- prepareSignature( custom_dge, geneColumn = "Gene", logfcColumn = "FC", pvalColumn = "Pval" ) print(custom_signature)# Load example differential expression data from package dge_file <- system.file("extdata", "dCovid_diffexp.tsv", package = "drugfindR" ) dge_data <- read.delim(dge_file) # Prepare signature with p-values (standard workflow) signature <- prepareSignature( dge_data, geneColumn = "hgnc_symbol", logfcColumn = "logFC", pvalColumn = "PValue" ) head(signature) # Prepare signature without p-values signature_no_pval <- prepareSignature( dge_data, geneColumn = "hgnc_symbol", logfcColumn = "logFC", pvalColumn = NA ) head(signature_no_pval) # Custom column names example custom_dge <- data.frame( Gene = c("TP53", "MYC", "BRCA1", "EGFR"), FC = c(2.5, -1.8, 3.2, -2.1), Pval = c(0.001, 0.01, 0.0001, 0.005) ) custom_signature <- prepareSignature( custom_dge, geneColumn = "Gene", logfcColumn = "FC", pvalColumn = "Pval" ) print(custom_signature)