signatureSearchData package provides access to the reference data used by
signatureSearch software package. The latter allows to search
with a query gene expression signature (GES) a database of treatment GESs to
identify cellular states sharing similar expression responses (connections). This
way one can identify drugs or gene knockouts that induce expression phenotypes
similar to a sample of interest. The resulting associations may lead to novel
functional insights how perturbagens of interest interact with biological systems.
signatureSearchData includes GES data from the CMap (Connectivity
Map) and LINCS (Library of Network-Based Cellular Signatures) projects that are
largely based on drug and genetic perturbation experiments performed on
variable numbers of human cell lines [@Lamb2006-du; @Subramanian2017-fu]. In
signatureSearchData these data sets have been preprocessed to be compatible
with the different gene expression signature search (GESS) algorithms
signatureSearch. The preprocessed data types include but are
not limited to normalized gene expression values (e.g. intensity values), log
fold changes (LFC) and Z-scores, p-values or FDRs of differentially expressed
genes (DEGs), rankings based on selected preprocessing routines or sets of top
The CMap data were downloaded from the CMap project site (Version build02). The latter is a collection of over 7,000 gene expression profiles (signatures) obtained from perturbation experiments with 1,309 drug-like small molecules on five human cancer cell lines. The Affymetrix Gene Chip technology was used to generate the CMAP2 data set.
In 2017, the LINCS Consortium generated a similar but much larger data set where the total number of gene expression signatures was scaled up to over one million. This was achieved by switching to a much more cost effective gene expression profiling technology called L1000 assay [@Peck2006-rf; @Edgar2002-di]. The current set of perturbations covered by the LINCS data set includes 19,811 drug-like small molecules applied at variable concentrations and treatment times to ~70 human non-cancer (normal) and cancer cell lines. Additionally, it includes several thousand genetic perturbagens composed of gene knockdown and over-expression experiments.
The data structures and search algorithms used by
signatureSearchData are designed to work with most genome-wide expression
data including hybridization-based methods, such as Affymetrix or L1000, as
well as sequencing-based methods, such as RNA-Seq. Currently,
signatureSearchData does not include preconfigured RNA-Seq reference data mainly
due to the lack of large-scale perturbation studies (e.g. drug-based) available in the public
domain that are based on RNA-Seq. This situation may change in the near future
once the technology has become more affordable for this purpose.
The vignette of this package is available at here
signatureSearchData is a R/Bioconductor package and can be installed using
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("signatureSearchData", version = "3.9")
To obtain the most recent updates immediately, one can install it directly from GitHub as follows.
After the package is installed, it can be loaded into an R session as follows.
For a detailed description of loading and/or generating signature databases,
please refer to the vignette of this package by running
browseVignettes("signatureSearchData") in an R session.
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