signatureSearchData: signatureSearchData: Reference Data for Gene Expression...

In yduan004/signatureSearchData: Datasets for signatureSearch package

signatureSearchData: Reference Data for Gene Expression Signature Searching

Description

This 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, this data package 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 (Lamb et al. 2006; Subramanian et al. 2017). 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 (Peck et al. 2006; Edgar, Domrachev, and Lash 2002). 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 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.

This package also contains other intermediate annotation datasets, such as drug-target information of small molecules in DrugBank, CLUE and STITCH databases used for getting target annotation of query drugs, GO system annotations and drugs to GO functional category mappings used for signatureSearch's functional enrichment analysis (FEA) routines or null distribution of Enrichment Scores (ES) used for computing WTCS p-values from LINCS GESS method.

Details

This package contains the following main datasets:

• cmap

• cmap_expr

• cmap_rank

• lincs

• lincs_expr

• dtlink_db_clue_sti

• taurefList

For documentation of generating the CMAP/LINCS databases from sources, please refer to the vignette of this package by running browseVignettes("signatureSearchData") in R.

References

Lamb, Justin, Emily D Crawford, David Peck, Joshua W Modell, Irene C Blat, Matthew J Wrobel, Jim Lerner, et al. 2006. “The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease.” Science 313 (5795): 1929–35. http://dx.doi.org/10.1126/science.1132939.

Subramanian, Aravind, Rajiv Narayan, Steven M Corsello, David D Peck, Ted E Natoli, Xiaodong Lu, Joshua Gould, et al. 2017. “A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.” Cell 171 (6): 1437–1452.e17. http://dx.doi.org/10.1016/j.cell.2017.10.049.

Edgar, Ron, Michael Domrachev, and Alex E Lash. 2002. “Gene Expression Omnibus: NCBI Gene Expression and Hybridization Array Data Repository.” Nucleic Acids Res. 30 (1): 207–10. https://www.ncbi.nlm.nih.gov/pubmed/11752295.

Peck, David, Emily D Crawford, Kenneth N Ross, Kimberly Stegmaier, Todd R Golub, and Justin Lamb. 2006. “A Method for High-Throughput Gene Expression Signature Analysis.” Genome Biol. 7 (7): R61. http://dx.doi.org/10.1186/gb-2006-7-7-r61.

See Also

signatureSearch

Examples

library(ExperimentHub)
eh <- ExperimentHub()
ssd <- query(eh, c("signatureSearchData"))
ssd
ssd$title
as.list(ssd)[10]

## Retrieve CMAP databases
cmap <- eh["EH3223"] # stores LFC scores
cmap_expr <- eh["EH3224"] # stores gene expression intensity values
cmap_rank <- eh["EH3225"] # stores gene ranks

## Retrieve LINCS databases
lincs <- eh['EH3226'] # stores moderated z-scores
lincs_expr <- eh['EH3227'] # stores gene expression intensity values

yduan004/signatureSearchData documentation built on April 7, 2023, 4:12 a.m.