Title: | Investigate iLINCS for candidate repurposable drugs |
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Description: | This package provides a convenient way to access the LINCS Signatures available in the iLINCS database. These signatures include Consensus Gene Knockdown Signatures, Gene Overexpression signatures and Chemical Perturbagen Signatures. It also provides a way to enter your own transcriptomic signatures and identify concordant and discordant signatures in the LINCS database. |
Authors: | Ali Sajid Imami [aut, cre] , Smita Sahay [aut] , Justin Fortune Creeden [aut] |
Maintainer: | Ali Sajid Imami <[email protected]> |
License: | GPL-3 + file LICENSE |
Version: | 0.99.1017 |
Built: | 2024-11-16 15:27:51 UTC |
Source: | https://github.com/CogDisResLab/drugfindR |
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
consensusConcordants(..., paired = FALSE, cutoff = 0.321, cellLine = NULL)
consensusConcordants(..., paired = FALSE, cutoff = 0.321, cellLine = NULL)
... |
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. |
A tibble with the filtered and deduplicated results
# 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 of signatures with defaults consensus <- consensusConcordants(concordantSignatures) # Get the consensus list of signatures with a different cutoff consensus <- consensusConcordants(concordantSignatures, cutoff = 0.5 ) # Get the consensus list of signatures with a specified cell lines consensus <- consensusConcordants(concordantSignatures, cellLine = c("A549", "MCF7") ) # Doing a paired analysis 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 )
# 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 of signatures with defaults consensus <- consensusConcordants(concordantSignatures) # Get the consensus list of signatures with a different cutoff consensus <- consensusConcordants(concordantSignatures, cutoff = 0.5 ) # Get the consensus list of signatures with a specified cell lines consensus <- consensusConcordants(concordantSignatures, cellLine = c("A549", "MCF7") ) # Doing a paired analysis 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 )
This function filters the L1000 Signature to a given threshold, identifying up-regulated or down-regulated or both up- and down-regulated genes
filterSignature(signature, direction = "any", threshold = NULL, prop = NULL)
filterSignature(signature, direction = "any", threshold = NULL, prop = NULL)
signature |
A dataframe with the L1000 signature |
direction |
Direction to filter to. Must be one of "up", "down" or "any". Defaults to "any" |
threshold |
A Log Fold-Change Threshold to filter at. This can either be a single value or a vector of two values. If a single value is given, then it is assumed to be a symmetric threshold. If two values are given, then the first value is the down-regulated threshold and the second value is the up-regulated threshold. Cannot be specified with prop |
prop |
A proportion of genes to take from top and bottom. Cannot be specified with threshold |
a tibble with the filtered L1000 Signature
# Get the L1000 signature for LINCSKD_28 kdSignature <- getSignature("LINCSKD_28") # Filter signature by a specific threshold filteredSignature <- filterSignature(kdSignature, threshold = 0.5) # Filter signature by a proportion filteredSignature <- filterSignature(kdSignature, prop = 0.1) # Filter Signature to up-regulated genes only by a threshold filteredSignature <- filterSignature(kdSignature, direction = "up", threshold = 0.5 ) # Filter the signature using differing thresholds for up and # down-regulated genes filteredSignature <- filterSignature(kdSignature, threshold = c(-0.75, 0.5) )
# Get the L1000 signature for LINCSKD_28 kdSignature <- getSignature("LINCSKD_28") # Filter signature by a specific threshold filteredSignature <- filterSignature(kdSignature, threshold = 0.5) # Filter signature by a proportion filteredSignature <- filterSignature(kdSignature, prop = 0.1) # Filter Signature to up-regulated genes only by a threshold filteredSignature <- filterSignature(kdSignature, direction = "up", threshold = 0.5 ) # Filter the signature using differing thresholds for up and # down-regulated genes filteredSignature <- filterSignature(kdSignature, threshold = c(-0.75, 0.5) )
This function takes a full or filtered signature and gets concordant signatures from any of the 3 LINCS databases in iLINCS. This can get Overexpression, Knockdown or Chemical Perturbagen signatures.
getConcordants(signature, ilincsLibrary = "CP")
getConcordants(signature, ilincsLibrary = "CP")
signature |
A data frame with the names of genes, their expression value and optionally their p-value |
ilincsLibrary |
The Library you want to search. Must be one of "OE", "KD" or "CP" for Overexpression, Knockdown or Chemical Perturbagens |
A tibble with the list of concordant and discordant signatures
# Get the L1000 signature for LINCSKD_28 kdSignature <- getSignature("LINCSKD_28") # Get concordant gene knockdown signatures concordant_signatures <- getConcordants(kdSignature, ilincsLibrary = "KD") head(concordant_signatures)
# Get the L1000 signature for LINCSKD_28 kdSignature <- getSignature("LINCSKD_28") # Get concordant gene knockdown signatures concordant_signatures <- getConcordants(kdSignature, ilincsLibrary = "KD") head(concordant_signatures)
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 default mode for drugfindR
is to use L1000
signatures. However,
if you are trying to retrieve a different transcriptomic signature,
that is also supported
by setting the l1000
parameter to FALSE
.
getSignature(sigId, l1000 = TRUE)
getSignature(sigId, l1000 = TRUE)
sigId |
character. The ilincs signature_id |
l1000 |
boolean. If you have a known l1000 signature |
a tibble with the L1000 Signature
# Get the L1000 signature for LINCSKD_28 kdSignature <- getSignature("LINCSKD_28")
# Get the L1000 signature for LINCSKD_28 kdSignature <- getSignature("LINCSKD_28")
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
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" )
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 |
A tibble with the the similarity scores and signature metadata
# Investigate a signature # Load and prepare the signature inputSignature <- read.table(system.file("extdata", "dCovid_diffexp.tsv", package = "drugfindR" ), header = TRUE) # Investigate the signature investigatedSignature <- investigateSignature(inputSignature, outputLib = "CP", filterThreshold = 0.5, geneColumn = "hgnc_symbol", logfcColumn = "logFC", pvalColumn = "PValue" )
# Investigate a signature # Load and prepare the signature inputSignature <- read.table(system.file("extdata", "dCovid_diffexp.tsv", package = "drugfindR" ), header = TRUE) # Investigate the signature investigatedSignature <- investigateSignature(inputSignature, outputLib = "CP", filterThreshold = 0.5, geneColumn = "hgnc_symbol", logfcColumn = "logFC", pvalColumn = "PValue" )
This function takes the name of a gene or a drug and a database to use to pull signatures from and then queries iLINCS to get concordant signatures
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 )
target |
The name of the gene or drug |
inputLib |
One of "OE", "KD" or "CP". Marks the database to use. |
outputLib |
One of "OE", "KD" or "CP". Marks the database to query. |
filterThreshold |
The Filtering threshold. |
similarityThreshold |
The Similarity Threshold |
paired |
Logical. Whether to query iLINCS separately for up and down regulated genes |
inputCellLines |
A character vector of cell lines to restrict our search for input signatures to. |
outputCellLines |
A character vetor of cell lines to restrict the output search to. |
A tibble with the the similarity scores and signature metadata
# Search the whole iLINCS database for top concordant signatures for an # ABL2 knockdown signature investigatedSignature <- investigateTarget("ABL2", inputLib = "KD", outputLib = "CP", filterThreshold = 0.5 )
# Search the whole iLINCS database for top concordant signatures for an # ABL2 knockdown signature investigatedSignature <- investigateTarget("ABL2", inputLib = "KD", outputLib = "CP", filterThreshold = 0.5 )
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.
prepareSignature( dge, geneColumn = "Symbol", logfcColumn = "logFC", pvalColumn = "PValue" )
prepareSignature( dge, geneColumn = "Symbol", logfcColumn = "logFC", pvalColumn = "PValue" )
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 |
A tibble with the L1000 signature.
# Prepare an L1000 signature from a differential gene expression output inputSignature <- read.table(system.file("extdata", "dCovid_diffexp.tsv", package = "drugfindR" ), header = TRUE) signature <- prepareSignature(inputSignature, geneColumn = "hgnc_symbol", logfcColumn = "logFC", pvalColumn = "PValue" ) head(signature)
# Prepare an L1000 signature from a differential gene expression output inputSignature <- read.table(system.file("extdata", "dCovid_diffexp.tsv", package = "drugfindR" ), header = TRUE) signature <- prepareSignature(inputSignature, geneColumn = "hgnc_symbol", logfcColumn = "logFC", pvalColumn = "PValue" ) head(signature)
This function is used to standardize the names of the columns output at the end of the result.
targetRename(inputNames)
targetRename(inputNames)
inputNames |
A character vector of input_names |
A character vector of new names