![[Stable]](figures/lifecycle-stable.svg)
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.
Arguments
- signature
A data.frame, tibble, or S4Vectors::DataFrame containing the signature data. Must conform to iLINCS signature structure with columns: *
signatureID: Signature identifier *ID_geneid: Gene IDs *Name_GeneSymbol: Gene symbols *Value_LogDiffExp: Log fold-change values *Significance_pvalue: Statistical significance p-valuesUse
prepareSignature()to ensure proper formatting.- ilincsLibrary
Character string specifying the iLINCS library to search. Must be one of: *
"CP": Chemical Perturbagen library (default) *"KD": Knockdown library *"OE": Overexpression library
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")
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
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")
)
#> 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)
#> # A tibble: 6 × 9
#> signatureid treatment concentration time cellline similarity pValue
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 LINCSCP_83965 SCHEMBL15556278 0.37uM 24h ASC.C 1.000 2.18e-6
#> 2 LINCSCP_59213 CHEMBL2361430 10uM 24h VCAP -1.000 3.91e-6
#> 3 LINCSCP_6945 Coleoforsin 10uM 6h A549 -1.000 8.70e-6
#> 4 LINCSCP_251399 Tozasertib 1uM 24h MCF7 -1.000 1.48e-5
#> 5 LINCSCP_26754 XMD-16144 10uM 6h HT29 -1.000 1.72e-5
#> 6 LINCSCP_46851 AC1M43RN 10uM 24h PC3 1.000 1.91e-5
#> # ℹ 2 more variables: sig_direction <chr>, sig_type <chr>
# Find concordant knockdown signatures
kdConcordants <- getConcordants(signature, ilincsLibrary = "KD")
head(kdConcordants)
#> # A tibble: 6 × 9
#> signatureid treatment concentration time cellline similarity pValue
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 LINCSKD_20320 RNF145 NA 96 h HT29 -1.000 0.0000193
#> 2 LINCSKD_17785 ZNF267 NA 96 h HEPG2 -1.000 0.0000566
#> 3 LINCSKD_12636 HNF4G NA 96 h HCC515 1.000 0.0000624
#> 4 LINCSKD_14114 ZNF621 NA 96 h HCC515 1.000 0.0000788
#> 5 LINCSKD_6737 TAOK3 NA 96 h A549 -1.000 0.000107
#> 6 LINCSKD_2259 PCSK7 NA 96 h A375 -1.000 0.000110
#> # ℹ 2 more variables: sig_direction <chr>, sig_type <chr>
# Find concordant overexpression signatures
oeConcordants <- getConcordants(signature, ilincsLibrary = "OE")
head(oeConcordants)
#> # A tibble: 6 × 9
#> signatureid treatment concentration time cellline similarity pValue
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 LINCSOE_6126 GNB1 NA 96 h HA1E 1.000 0.0000234
#> 2 LINCSOE_4496 INHBE NA 96 h A549 -1.000 0.000329
#> 3 LINCSOE_5490 GPR65 NA 96 h HA1E -1.000 0.000450
#> 4 LINCSOE_10043 SMAD3 NA 48 h HEK293T -1.000 0.000500
#> 5 LINCSOE_10325 ZRSR1 NA 48 h HEK293T -0.999 0.000510
#> 6 LINCSOE_216 HACD1 NA 96 h A375 0.999 0.000517
#> # ℹ 2 more variables: sig_direction <chr>, sig_type <chr>
# 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