signatureSearch-package: Environment for Gene Expression Signature Searching Combined...

In girke-lab/signatureSearch: Environment for Gene Expression Searching Combined with Functional Enrichment Analysis

Environment for Gene Expression Signature Searching Combined with Functional Enrichment Analysis

Description

Welcome to the signatureSearch package! This package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods. These utilities are useful for studying the effects of genetic, chemical and environmental perturbations on biological systems. Specifically, in drug discovery they can be used for identifying novel modes of action (MOA) of bioactive compounds from reference databases such as LINCS containing the genome-wide GESs from tens of thousands of drug and genetic perturbations (Subramanian et al. 2017)

A typical GES search (GESS) workflow can be divided into two major steps. First, GESS methods are used to identify perturbagens such as drugs that induce GESs similar to a query GES of interest. The queries can be drug-, disease- or phenotype-related GESs. Since the MOAs of most drugs in the corresponding reference databases are known, the resulting associations are useful to gain insights into pharmacological and/or disease mechanisms, and to develop novel drug repurposing approaches.

Second, specialized functional enrichment analysis (FEA) methods using annotations systems, such as Gene Ontologies (GO), KEGG and Reactome pathways have been developed and implemented in this package to efficiently interpret GESS results. The latter are usually composed of lists of perturbagens (e.g. drugs) ranked by the similarity metric of the corresponding GESS method.

Finally, network reconstruction functionalities are integrated for visualizing the final results, e.g. in form of drug-target networks.

Details

The GESS methods include CMAP, LINCS, gCMAP, Fisher and Cor. For detailed description, please see help files of each method. Most methods can be easily paralleled for multiple query signatures.

GESS results are lists of perturbagens (here drugs) ranked by their signature similarity to a query signature of interest. Interpreting these search results with respect to the cellular networks and pathways affected by the top ranking drugs is difficult. To overcome this challenge, the knowledge of the target proteins of the top ranking drugs can be used to perform functional enrichment analysis (FEA) based on community annotation systems, such as Gene Ontologies (GO), pathways (e.g. KEGG, Reactome), drug MOAs or Pfam domains. For this, the ranked drug sets are converted into target gene/protein sets to perform Target Set Enrichment Analysis (TSEA) based on a chosen annotation system. Alternatively, the functional annotation categories of the targets can be assigned to the drugs directly to perform Drug Set Enrichment Analysis (DSEA). Although TSEA and DSEA are related, their enrichment results can be distinct. This is mainly due to duplicated targets present in the test sets of the TSEA methods, whereas the drugs in the test sets of DSEA are usually unique. Additional reasons include differences in the universe sizes used for TSEA and DSEA.

Importantly, the duplications in the test sets of the TSEA are due to the fact that many drugs share the same target proteins. Standard enrichment methods would eliminate these duplications since they assume uniqueness in the test sets. Removing duplications in TSEA would be inappropriate since it would erase one of the most important pieces of information of this approach. To solve this problem, we have developed and implemented in this package weighting methods (dup_hyperG, mGSEA and meanAbs) for duplicated targets, where the weighting is proportional to the frequency of the targets in the test set.

Instead of translating ranked lists of drugs into target sets, as for TSEA, the functional annotation categories of the targets can be assigned to the drugs directly to perform DSEA instead. Since the drug lists from GESS results are usually unique, this strategy overcomes the duplication problem of the TSEA approach. This way classical enrichment methods, such as GSEA or tests based on the hypergeometric distribution, can be readily applied without major modifications to the underlying statistical methods. As explained above, TSEA and DSEA performed with the same enrichment statistics are not expected to generate identical results. Rather they often complement each other's strengths and weaknesses.

To perform TSEA and DSEA, drug-target annotations are essential. They can be obtained from several sources, including DrugBank, ChEMBL, STITCH, and the Touchstone dataset from the LINCS project (https://clue.io/). Most drug-target annotations provide UniProt identifiers for the target proteins. They can be mapped, if necessary via their encoding genes, to the chosen functional annotation categories, such as GO or KEGG. To minimize bias in TSEA or DSEA, often caused by promiscuous binders, it can be beneficial to remove drugs or targets that bind to large numbers of distinct proteins or drugs, respectively.

Note, most FEA tests involving proteins in their test sets are performed on the gene level in signatureSearch. This way one can avoid additional duplications due to many-to-one relationships among proteins and their encoding gents. For this, the corresponding functions in signatureSearch will usually translate target protein sets into their encoding gene sets using identifier mapping resources from R/Bioconductor such as the org.Hs.eg.db annotation package. Because of this as well as simplicity, the text in the vignette and help files of this package will refer to the targets of drugs almost interchangeably as proteins or genes, even though the former are the direct targets and the latter only the indirect targets of drugs.

Terminology

The term Gene Expression Signatures (GESs) can refer to at least four different situations of pre-processed gene expression data: (1) normalized gene expression intensity values (or counts for RNA-Seq); (2) log2 fold changes (LFC), z-scores or p-values obtained from analysis routines of differentially expressed genes (DEGs); (3) rank transformed versions of the expression values obtained under (1) and (2); and (4) gene identifier sets extracted from the top and lowest ranks under (3), such as n top up/down regulated DEGs.

Author(s)

• Yuzhu Duan ([e-mail])

• Brendan Gongol ([e-mail]>)

• Thomas Girke ([e-mail])

References

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

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

Sandmann, Thomas, Sarah K Kummerfeld, Robert Gentleman, and Richard Bourgon. 2014. gCMAP: User-Friendly Connectivity Mapping with R. Bioinformatics 30 (1): 127-28. http://dx.doi.org/10.1093/bioinformatics/btt592

Subramanian, Aravind, Pablo Tamayo, Vamsi K Mootha, Sayan Mukherjee, Benjamin L Ebert, Michael A Gillette, Amanda Paulovich, et al. 2005. Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles. Proc. Natl. Acad. Sci. U. S. A. 102 (43): 15545-50. http://dx.doi.org/10.1073/pnas.0506580102

See Also

Methods for GESS:

• gess_cmap, gess_lincs, gess_gcmap gess_fisher, gess_cor

Methods for FEA:

• TSEA methods: tsea_dup_hyperG, tsea_mGSEA, tsea_mabs

• DSEA methods: dsea_hyperG, dsea_GSEA

girke-lab/signatureSearch documentation built on Feb. 21, 2024, 8:32 a.m.