signatureSearchData: Reference Data for Gene Expression Signature Searching

Introduction

The signatureSearchData package provides access to the reference data used by the associated 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.

Currently, 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 implemented in 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 up/down-regulated DEGs.

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.

In 2020, the LINCS 2017 database is expanded to the beta release, here refer to as LINCS2. It contains >80k perturbations and >200 cell lines and over 3M gene expression profiles. This represents roughly a 3-fold expansion on the LINCS 2017 database and notable new subsets of data include CRISPSR knockout of >5k genes and hematopoietic and non-cancer cell models. The datasets can be accessed at https://clue.io/releases/data-dashboard.

The data structures and search algorithms used by signatureSearch and 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.

Vignette

The vignette of this package is available at here

Install and Load Package

signatureSearchData is a R/Bioconductor package and can be installed using BiocManager::install().

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.

devtools::install_github("yduan004/signatureSearchData")

After the package is installed, it can be loaded into an R session as follows.

library(signatureSearchData)

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.