gess_cmap {signatureSearch} | R Documentation

## CMAP Search Method

### Description

Implements the original Gene Expression Signature Search (GESS) from Lamb et al (2006) known as Connectivity Map (CMap). The method uses as query the two label sets of the most up- and down-regulated genes from a genome-wide expression experiment, while the reference database is composed of rank transformed expression profiles (e.g. ranks of LFC or z-scores).

### Usage

    gess_cmap(qSig, chunk_size = 5000, ref_trts = NULL, workers = 1)

### Arguments

`qSig` | `qSig` object defining the query signature including the GESS method (should be 'CMAP') and the path to the reference database. For details see help of `qSig` and `qSig-class`.
`chunk_size` | number of database entries to process per iteration to limit memory usage of search.
`ref_trts` | character vector. If users want to search against a subset of the reference database, they could set ref_trts as a character vector representing column names (treatments) of the subsetted refdb.
`workers` | integer(1) number of workers for searching the reference database parallelly, default is 1.

### Details

Lamb et al. (2006) introduced the gene expression-based search method known as Connectivity Map (CMap) where a GES database is searched with a query GES for similar entries. Specifically, this GESS method uses as query the two label sets of the most up- and down-regulated genes from a genome-wide expression experiment, while the reference database is composed of rank transformed expression profiles (e.g.ranks of LFC or z-scores). The actual GESS algorithm is based on a vectorized rank difference calculation. The resulting Connectivity Score expresses to what degree the query up/down gene sets are enriched on the top and bottom of the database entries, respectively. The search results are a list of perturbagens such as drugs that induce similar or opposing GESs as the query. Similar GESs suggest similar physiological effects of the corresponding perturbagens. Although several variants of the CMAP algorithm are available in other software packages including Bioconductor, the implementation provided by `signatureSearch` follows the original description of the authors as closely as possible.

### Value

`gessResult` object, the result table contains the search results for each perturbagen in the reference database ranked by their signature similarity to the query.

### Column description

Descriptions of the columns specific to the CMAP method are given below. Note, the additional columns, those that are common among the GESS methods, are described in the help file of the `gessResult` object.

- raw_score: bi-directional enrichment score (Kolmogorov-Smirnov statistic) of up and down set in the query siganture

- scaled_score: raw_score scaled to valules from 1 to -1 by dividing the positive and negative scores with the maxmum positive score and the absolute value of the minimum negative score, respectively.

### References

For detailed description of the CMap method, please refer to: Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Golub, T. R. (2006). The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science, 313 (5795), 1929-1935. URL: https://doi.org/10.1126/science.1132939

### See Also

`qSig`, `gessResult`, `gess`

### Examples

    db_path <- system.file("extdata", "sample_db.h5",
                           package = "signatureSearch")
    # qsig_cmap <- qSig(query = list(
    #                   upset=c("230", "5357", "2015", "2542", "1759"),
    #                   downset=c("22864", "9338", "54793", "10384", "27000")),
    #                   gess_method = "CMAP", refdb = db_path)
    # cmap <- gess_cmap(qSig=qsig_cmap, chunk_size=5000)
    # result(cmap)

[Package signatureSearch version 1.6.3 Index]